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The effects of aging on temporal masking

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Title:
The effects of aging on temporal masking
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Fulton, Susan
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Temporal resolution
Gap detection
Speech in noise
Speech perception
Sensorineural hearing loss
Presbycusis
Dissertations, Academic -- Communication Sciences & Disorders -- Masters -- USF   ( lcsh )
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non-fiction   ( marcgt )

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Abstract:
ABSTRACT: The ability to resolve rapid intensity and frequency fluctuations in sound is important for understanding speech, especially in real-world environments that include background noise and reverberation. Older listeners often complain of difficulties understanding speech in such real-world environments. One factor thought to influence speech understanding in noisy and reverberant environments is temporal resolution, the ability to follow rapid acoustic changes over time. Temporal resolution is thought to help listeners resolve rapid acoustic changes in speech as well as use small glimpses of speech available in the dips or gaps in the background sounds. Temporal resolution is an ability that is known to deteriorate with age and hearing loss, negatively affecting the ability to understand speech in noisy real-world environments. Measures of temporal resolution, including temporal masking, gap detection, and speech in interrupted noise, use a silent gap as the cue of interest. Temporal masking and speech in interrupted noise tasks measure how well a listener resolves a stimulus before, after, or between sounds (i.e., within a silent gap), while gap detection tasks measure how well the listener resolves the timing of a silent gap itself. A listener needs to resolve information within the gap and the timing of the gap when listening to speech in background sounds. This study examined the role of aging and hearing loss on three measures of temporal resolution: temporal masking, gap detection, and speech understanding in interrupted noise. For all three measures, participants were young listeners with normal hearing (n = 8, mean age = 25.4 years) and older listeners with hearing loss (n = 9, mean age = 72.1 years). Results showed significant differences between listener groups for all three temporal measures. Specifically, older listeners with hearing loss had higher temporal masked thresholds, larger gap detection thresholds, and required a higher signal-to-noise ratio for speech understanding in interrupted noise. Relations between temporal tasks were observed. Temporal masked thresholds and gap detection thresholds accounted for a significant amount of the variance in speech-in-noise scores. Findings suggest that deficits in temporal resolution abilities may contribute to the speech-in-noise difficulties reported by older listeners.
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Dissertation (PHD)--University of South Florida, 2010.
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Includes bibliographical references.
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by Susan Fulton.
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The Effects of Aging on Temporal Masking by Susan E. Fulton A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Communication Sciences and Disorders College of Behavioral and Community Sciences University of South Florida Co-Major Professor: Jennifer J. Lister, Ph.D. Co-Major Professor: Richard H. Wilson, Ph.D. Rachel A. McArdle, Ph.D. Theresa H. Chisolm, Ph.D. Date of Approval: June 30, 2010 Keywords: temporal resolution, gap detection, speec h in noise, speech perception, sensorineural hearing loss, presbycusis Copyright 2010, Susan E. Fulton

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Dedication This achievement is dedicated to my loving husband John, who is truly my best friend. Without his encouragement, this dr eam would not be a reality. To my children Nathan, Mary, Matthew, and Michael: Be steadfast and work towards your goals. You can achieve anything you d esire. To my father and in memory of my mother: Thank you for your example and guidance throughout my life. Mom, although you were not here for the finish line, you gave me strength and encouragement along the way. I know you are still watching over me.

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Acknowledgements The journey down the Ph.D. road has been both chal lenging and rewarding. I have learned many things, professionally and pe rsonally. Success is not achieved alone. I would like to express my sincere st appreciation to a group of mentors and friends who have helped me along the wa y. I would like to thank Dr. Jennifer Lister, who has been a wonderful mentor and friend. Her vast knowledge, patience, guidance, and support have been both an example and inspiration to me to strive for the best in all I attempt to accomplish. I am grateful for Drs. Wilson, McArdle and Chisolm. Their guidance and insight have helped me refine and deve lop research skills that will be so crucial in the future The path to a Ph.D. is long and trying. I am gratef ul to Dr. Nathan Maxfield, who was always available to lend a listen ing ear when I became frustrated. His words of encouragement helped me fo cus and tackle the hurdles I faced. Appreciation is also given to Drs. Carr, Go nzalez, Hansel, Muscato, Nikjeh, and Zelski, who were always interested in m y progress and had faith in me. Your kind words brightened my spirits and gave me confidence. Finally, I would like to acknowledge Dr. Raymond Hu rley, who had faith in me and provided guidance at the very beginning of t his journey. I have finally arrived at the end of the tunnel!

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i Table of Contents List of Tables iv List of Figures vii Abstract ix Chapter One-Introduction 1 Chapter Two-Review of the Literature 6 Temporal Cues in Speech 6 Temporal Resolution and Speech Understanding in Noi se 6 Temporal Masking 9 Stimulus Effects 11 Forward-Backward Masking 13 Age and Hearing Loss Effects 14 Gap Detection and Gap Discrimination 17 Stimulus Effects 19 Age and Hearing Loss Effects 23 Summary 27 Research Questions 31 Hypotheses 31 Questions 31 Chapter Three-Method 33 Participants 33 Participant Selection 33 Inclusion/Exclusion Criteria 33 Stimuli 35 Stimuli Generation 35 Masker/Marker Stimuli 35 Click Stimuli 37 Speech Understanding in Interrupted Noise 38 Calibration 41 Procedures 42 Detection Thresholds 42 Word Recognition Score in Quiet (WRS-Q) 44 Temporal Masking 44 Gap Detection 47

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ii Speech Understanding in Interrupted Noise 48 Test Sessions 49 Chapter Four-Results 51 Detection Thresholds and Word Recognition Scores 52 Temporal Masking 53 Simultaneous Masking 53 Forward Masking 55 Backward Masking 60 Forward-Backward Masking 64 100 ms Interstimulus Interval (ISI) 65 50 ms ISI 69 25 ms ISI 73 Gap Detection Threshold (GDT) 77 Forward-Backward Threshold at GDT 78 Speech Understanding in Interrupted Noise 80 Correlation Analysis 86 Multivariate Regression Analysis 94 Young Listeners with Normal Hearing (YNH) 95 Older Listeners with Hearing Impairment (OHI) 95 Chapter Five-Discussion 99 Discussion of Findings in Relationship to Research Questions 100 Will OHI Listeners Exhibit Greater Temporal Masking Larger Gap Detection Thresholds, and Poorer Speech Understanding in Noise than YNH Listeners? 101 Will OHI Listeners Have Higher Forward, Backward and Forward-Backward Masked Thresholds than the YNH Listeners? 102 Will OHI Listeners Have More Threshold Shift in the Temporal Masked Conditions? 103 Will Only YNH Listeners Have Excess Masking in the Forward-Backward Masked Condition? 104 Will OHI Listeners Have Larger GDTs than YNH Listeners? 106 Will YNH Listeners Have More Release from Masking in the Speech in Interrupted Noise Condition? 106 Will Performance on Tasks of Temporal Masking, Gap Detection, and Speech Understanding in Noise be Strongly Related? 108 Will Performance on Temporal Masking and GDT Tasks Predict Performance on Speech Understanding in Interrupted Noise Tasks? 112 Summary 114 Limitations of Study 116

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iii Implications of Study 118 Future Research 119 References 121 Appendices Appendix A: Individual Thresholds for Study Condit ions 135 Appendix B: RPvds Programming for Temporal Masking and GDT Stimuli 141 Appendix C: Screen Shot of 2I/2AFC Response 144 Appendix D: Visual Basic (v 6.0) Programming for T emporal Masking and GDT Tasks 145 Appendix E: Example Results Form for Temporal Mask ing Tasks 211 Appendix F: Speech in Interrupted Nose Example Sco re Sheet 212 About the Author End Page

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iv List of Tables Table 1 Summary of research assessing temporal reso lution abilities in a variety of populations 30 Table 2 Pure tone thresholds (in dB HL) and pure to ne averages (PTA) for young listeners with normal hearing 35 Table 3 Pure tone thresholds (in dB HL) and pure to ne averages (PTA) for older listeners with hearing impairment 35 Table 4 Mean click thresholds, broadband noise (BBN ) thresholds and word recognition scores (WRS-Q) for both listener groups 53 Table 5 Mann-Whitney U Rank-Sum results measuring detection thresholds and word recognition score differences between groups 53 Table 6 Simultaneous masked (SM) mean thresholds an d standard deviations for both listener groups 54 Table 7 Forward masked (FM) mean thresholds for bot h listener groups 55 Table 8 ANOVA results for effect of group for each of the FM conditions 56 Table 9 FM threshold shift mean values for both lis tener groups 58 Table 10 FM release from masking mean values for bo th listener groups 59 Table 11 Backward masked (BM) mean threshold for bo th listener groups 60 Table 12 ANOVA results for effect of group for each of the BM conditions 61

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v Table 13 BM threshold shift mean values for both li stener groups 63 Table 14 BM release from masking mean values for bo th listener groups 64 Table 15 100-ms ISI forward-backward masked (FB-M) mean thresholds for both listener groups 65 Table 16 ANOVA results for effect of group at each of the 100ms ISI FB-M conditions 66 Table 17 100-ms ISI FB-M threshold shift mean value s for both listener groups 68 Table 18 100-ms ISI FB-M release from masking mean values for both listener groups 68 Table 19 50-ms ISI FB-M mean thresholds for both li stener groups 69 Table 20 ANOVA results for effect of group at each of the 50ms ISI FB-M conditions 70 Table 21 50-ms ISI FB-M threshold shift mean values for both listener groups 72 Table 22 50-ms ISI FB-M release from masking mean v alues for both listener groups 73 Table 23 25-ms ISI FB-M thresholds for both listene r groups 73 Table 24 ANOVA results for effect of group at each of the 50ms ISI FB-M conditions 74 Table 25 25-ms ISI threshold shift mean values for both listener groups 76 Table 26 25-ms ISI release from masking mean values for both listener groups 76 Table 27 Mean gap detection thresholds (GDT) and st andard deviations for both listener groups 77

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vi Table 28 Mean forward-backward at GDT (FB at GDT) m asked thresholds 78 Table 29 FB at GDT mean masking threshold shift and release from masking values for both listener groups 80 Table 30 Average SNR (dB HL) needed for 50% correct speech intelligibility in noise and slopes of probit func tions for both groups 84 Table 31 Average SNR (dB HL) needed for 70% correct speech intelligibility in noise for both groups 84 Table 32 ANOVA results for effect of group for each of the interrupted speech-in-noise conditions 85 Table 33 Spearman/s Rho correlation values for corr elation analysis for YNH data 88 Table 34 Spearman’s Rho correlation values for corr elation analysis for OHI data 89 Table 35 Spearman’s Rho correlation values for YNH temporal masking and SNR thresholds 92 Table 36 Spearman’s Rho correlation values for OHI temporal masking/GDT and SNR thresholds 93 Table 37 Multivariate regression analysis results f or YNH listener data 97 Table 38 Multivariate regression analysis results f or YNH listener data 98 Table 39 Comparison of study results and comparable literature 100

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vii List of Figures Figure 1 Waveform of broadband noise used for mask ers and markers as recorded through (A) the sound card and (B) the headphones 36 Figure 2 Magnitude spectrum of broadband noise use d for maskers and markers recorded through (A) the sound card and (B) the headphones 37 Figure 3 Waveform of click as recorded through (A) the sound card and (B) the headphones 38 Figure 4 Magnitude spectrum of click stimulus reco rded through (A) the sound card and (B) the headphones 38 Figure 5 Example of speech-in-noise stimuli 40 Figure 6 Examples of temporal masking stimuli 46 Figure 7 Example of gap detection stimuli 47 Figure 8 Mean forward masked, quiet, and simultane ous thresholds (SM) for both listener groups 57 Figure 9 Mean backward masked (BM), simultaneous ( SM), and quiet thresholds for both listener groups 62 Figure 10 Mean 100-ms ISI Forward-Backward masked (FB-M) thresholds for both listener groups 67 Figure 11 Mean 50-ms ISI Forward-Backward masked ( FB-M) thresholds for both listener groups 71 Figure 12 Mean 25-ms ISI Forward-Backward masked ( FB-M) thresholds for both listener groups 75 Figure 13 Forward-backward at GDT and simultaneous masked thresholds bar graph for both listener groups 79

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viii Figure 14 Best fit lines from probit analysis for YNH listeners in each of the four interrupted speech-in-noise conditions 82 Figure 15 Best fit lines from probit analysis for OHI listeners in each of the four interrupted speech-in-noise conditions 83 Figure 16 Thresholds for both listener groups on a ll 4 IPS conditions 86 Figure 17 Scatter plots of FB @ GDT and temporally masked thresholds for the YNH listeners 90 Figure 18 Scatter plots of FB @ GDT and temporally masked thresholds for the OHI listeners 91 Figure 19 Scatter plots of GDT and temporally maske d thresholds for OHI listeners 92 Figure 20 Scatter plot of OHI gap detection thresho lds and speech-in-interrupted noise conditions 94

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ix The Effects of Aging on Temporal Masking Susan E. Fulton ABSTRACT The ability to resolve rapid intensity and frequen cy fluctuations in sound is important for understanding speech, especially in r eal-world environments that include background noise and reverberation. Older listeners often complain of difficulties understanding speech in such real-worl d environments. One factor thought to influence speech understanding in noisy and reverberant environments is temporal resolution, the ability to follow rapid acoustic changes over time. Temporal resolution is thought to help listeners resolve rapid acoustic changes in speech as well as use small glimpses of speech available in the dips or gaps in the background sounds. Temporal resolut ion is an ability that is known to deteriorate with age and hearing loss, neg atively affecting the ability to understand speech in noisy real-world environments. Measures of temporal resolution, including temporal masking, gap detection, and speech in interrupted noise, use a s ilent gap as the cue of interest. Temporal masking and speech in interrupted noise ta sks measure how well a listener resolves a stimulus before, after, or betw een sounds (i.e., within a silent gap), while gap detection tasks measure how well th e listener resolves the timing of a silent gap itself. A listener needs to resolv e information within the gap and

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x the timing of the gap when listening to speech in b ackground sounds. This study examined the role of aging and hearing loss on thre e measures of temporal resolution: temporal masking, gap detection, and sp eech understanding in interrupted noise. For all three measures, partici pants were young listeners with normal hearing (n = 8, mean age = 25.4 years) and o lder listeners with hearing loss (n = 9, mean age = 72.1 years). Results showed significant differences between list ener groups for all three temporal measures. Specifically, older liste ners with hearing loss had higher temporal masked thresholds, larger gap detec tion thresholds, and required a higher signal-to-noise ratio for speech understanding in interrupted noise. Relations between temporal tasks were obser ved. Temporal masked thresholds and gap detection thresholds accounted f or a significant amount of the variance in speech-in-noise scores. Findings s uggest that deficits in temporal resolution abilities may contribute to the speech-in-noise difficulties reported by older listeners.

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1 Chapter One Introduction Understanding speech presented in a background of n oise, speech, environmental sounds, and reverberation can be diff icult for any listener. Older listeners, in particular, often complain of difficu lties understanding speech in noisy, real-world environments (Killion, 2002; Wils on & Strouse, 2002). A factor thought to influence speech understanding in a back ground of sound is temporal resolution, the ability to follow rapid acoustic ch anges over time. Listeners must resolve a number of acoustic changes that vary rapidly as a function of time, both in the speech itself and i n the background sounds that interfere with and degrade the speech, to process a speech signal in a real-world environment. In terms of the speech signal, the li stener must process specific temporal cues known to be critical for identifying the particular sounds of speech, such as discriminating between a single and double stop consonant, an affricate versus a fricative, or the voicing of a stop conson ant in medial word position (Dorman, Marton, & Hannley, 1985; Gordon-Salant & F itzgibbons, 1993; Price & Simon, 1984; Strouse, Ashmead, Ohde, & Grantham, 19 98; Tremblay, Piskosz, & Souza, 2002). When speech is presented in a back ground of interfering sounds, the rapid temporal changes of the backgroun d sounds are added to those inherent in the speech signal. The backgroun d sounds must be both processed and disregarded in a rapidly changing ser ies of acoustic events

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2 (Lutman, 1991; Schneider & Hamstra, 1999; Snell & F risina, 2000; Strouse et al., 1998). Background sounds found in typical environments act upon speech in three primary ways: temporal distortion of the spee ch signal waveform, filling silent gaps with reverberating sounds, and low-pass filtering of the speech signal (Gordon-Salant & Fitzgibbons, 1993; Houtgast & Stee neken, 1985, 1973). The auditory system is impressive in its ability to pro cess such a rapid series of relevant and irrelevant events leading to the accur ate understanding of a spoken message. Background sounds constantly fluctuate in amplitude, resulting in periods when the level of the speech signal is grea ter than the other sounds; these periods of improved signal-to-noise ratio are sometimes referred to as “glimpses” of the speech signal. In order to proce ss speech in background sounds, listeners may make use of glimpses of the t arget speech signal (Cooke, 2006). Effective use of auditory glimpses requires good temporal resolution (Gordon-Salant & Fitzgibbons, 1993; Houtgast & Stee neken, 1973). Fine temporal resolution is required to process both the cues inherent in the speech itself and the glimpses (Lutman, 1991; Schneider & Hamstra, 1999; Snell & Frisina, 2000; Strouse et al., 1998). Temporal resolution may be measured in a variety of ways, including temporal masking, silent gap detection/discriminati on, duration discrimination, and temporal order perception. Other measures incl ude speech degraded by temporal waveform distortion, such as time compress ion, reverberation, or interrupted speech (Gordon-Salant & Fitzgibbons, 19 93, 1999) or

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3 modulated/interrupted maskers (Dubno, Horwitz & Ahl strom, 2002; 2003, Miller & Licklider, 1950). Two traditional psychophysical m easures of temporal resolution that use a silent pause as the cue of interest are temporal masking and gap detection. Masking occurs when a sound (masker) in terferes with the ability to hear all or a portion of a target stimulus. Tempor al masking tasks measure the ability of a listener to resolve timing differences between masking noise and a target sound. Three types of temporal masking are commonly measured: Forward masking (masker precedes the target), backw ard masking (masker follows the target), and forward-backward masking ( target is between two maskers). Temporal masking represents the ability to process information within a silent pause, similar to a glimpse. A gap detect ion threshold (GDT) is the smallest time increment of silence between two stim uli that can be identified by a listener (Phillips, Taylor, Hall, Carr, & Mossop, 1 997; Plomp, 1964). Detection of silent gaps is considered representative of the abi lity to use certain brief silent cues in speech as well as glimpses in background so und. Both measures (temporal masking and gap detection) are related to the recognition of speech in the presence of modulated maskers (Dubno et al., 2002, 2003; Gifford, Bacon & Williams, 2007) or backgroun d noise (Gordon-Salant & Fitzgibbons, 1993; Snell & Frisina, 2000; Tyler, Su mmerfield, Wood, & Fernandes, 1982). If temporal resolution is impair ed (i.e., high temporal masked thresholds, slow recovery from temporal masking, or poor GDTs), then listeners may have difficulties understanding speech in the p resence of background sound (Moore, Peters & Stone, 1999; Peters, Moore & Baer, 1998). A population

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4 known to have impaired temporal resolution is older adults with sensorineural hearing loss. The effects of aging and hearing loss are thought t o contribute to poor temporal resolution. Poorer temporal masked thresh olds have been observed in older listeners (Dubno et al., 2002; Gehr & Sommers 1999; Gifford et al., 2007) and listeners with hearing loss (Kidd, Mason & Feth 1983; Oxenham & Moore, 1995) as compared to young listeners with normal he aring. However, the combined effects of age and hearing loss on tempora l masking have not been fully examined. Older listeners have shown poorer GDTs than younger listeners (Grose, Hall & Buss, 2001; Lister, 2000). Older li steners with hearing loss have been found to have poorer GDTs than younger listene rs with normal hearing (Fitzgibbons & Gordon-Salant, 1987; Glasberg & Moor e, 1992; Tyler et al., 1982). Interestingly, a relationship between recov ery from masking and gap detection may exist (Glasberg, Moore, & Bacon, 1987 ; Smiarowski, 1970) but the relationship has not been fully investigated. Speech understanding in the presence of background sounds has been shown to deteriorate with hearing loss (Tyler et al ., 1982), with age (Stuart & Phillips, 1996), and with both age and hearing loss (Wilson, McArdle, Betancourt, Herring, Lipton, & Chisolm, 2010). Interrupted bac kground noise can be used to evaluate temporal resolution in a speech-intelligib ility paradigm, specifically the ability to resolve speech cues present in the inter ruptions or gaps (Dubno, et al., 2002; Miller & Licklider, 1950; Stuart & Phillips, 1996). Performance of older and younger listeners on tasks measuring speech underst anding in interrupted

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5 background noise correlates with temporal masking p erformance (Dubno et al., 2002; 2003) and GDTs (Snell & Frisina, 2000; Lutman 1991). Benefit from interruptions in background noise decreased as forw ard masked thresholds increased. Thresholds to speech presented in noise increased as GDTs increased. The impact of reduced recovery from tem poral masking on gap detection and speech–in-noise performance needs to be examined to gain further insight about the difficulties understandin g speech in noise reported by many older listeners. This study was designed to investigate the role of aging and hearing loss on measures of temporal resolution. Temporal resol ution of young listeners with normal hearing (for pure tones) and older listeners with hearing loss were investigated using measures of temporal masking, ga p detection, and speech in interrupted noise.

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6 Chapter Two Review of the Literature Temporal Cues in Speech Temporal resolution refers to the ability of the a uditory system to process timing information within and across auditory stimu li. When processing speech, temporal information needs to be quickly and accura tely coded. Temporal cues in speech include the durations of speech segments (phonemes, syllables, etc.) and the durations of silent periods between speech segments. For example, formant transition duration may signal a stop conso nant versus a semivowel (Dorman, Marton, & Hannley, 1985). The duration of silent intervals in speech may cue the difference between affricates and frica tives, voicing of a stop consonant in medial word position, and single versu s double stop consonants (Dorman, et al., 1985; Lister & Tarver, 2004). Bec ause of the number of temporal cues in speech, optimal speech understandi ng may rely, at least in part, on good temporal resolution. Temporal Resolution and Speech Understanding in Noi se Temporal resolution is also important for understan ding speech that has been distorted by background noise and reverberatio n (Lutman, 1991; Schneider & Hamstra, 1999; Snell & Frisina, 2000; Strouse et al., 1998). Background noise typically varies in frequency and amplitude over ti me (Dubno et al., 2002). Any rapid changes in background noise are added to the speech signal, thereby

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7 altering temporal cues. The sounds in typical list ening environments can act upon speech in three primary ways: temporal distort ions of the speech signal, filling of silent gaps with sounds, and low-pass fi ltering of the speech signal (Gordon-Salant & Fitzgibbons, 1993; Houtgast & Stee neken, 1973, 1985; Lister, Koehnke, & Besing, 2000). Background noise and rev erberation may also act as temporal maskers. The inherent modulations in natu rally-occurring background noise can produce both forward and backward masking effects (Bacon, Opie, & Montoya, 1998; Dubno et al., 2002, 2003; Gifford et al., 2007). Intact temporal resolution may be a key factor influencing speech r ecognition in degraded listening situations (Gordon-Salant & Fitzgibbons, 1993). Performance on tasks of temporal resolution and spe ech-in-noise recognition are thought to be related. Forward mas ked thresholds have been shown to correlate with recognition scores of vowel -consonant syllables (Dubno et al., 2002; 2003) and sentences (Gifford et al., 2007) in the presence of a modulated masker. The ability to detect small incr ements of silence between noises (gap detection) appears to be related to the ability to process a speech signal in the presence of background noise. For ex ample, Snell and Frisina (2000) found significant correlations between GDTs and spondee-in-babble thresholds. GDTs also correlate with intelligibili ty of target words presented in background noise (Tyler et al., 1982). The ability to understand sentences in noise deteriorates as GDTs increases (Lutman, 1991) As compared to steady-state background noise, an im provement in speech recognition performance is observed when bac kground noise is

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8 modulated or interrupted (Dubno et al., 2002; Mille r & Licklider, 1950; Stuart & Phillips, 1996). When attempting to understand spe ech in background noise, listeners may take advantage of momentary improveme nts in the signal-to-noise ratio (the ratio of the level of the signal to the level of the noise). These momentary improvements in signal-to-noise ratio pro vide auditory glimpses of the speech signal, which the listener may use to pr ocess audible portions of the speech target (Cooke, 2006; Miller & Licklider, 195 0). In real-world environments, the interruptions in the background n oise occur randomly. Miller and Licklider (1950) compared intelligibility of mo nosyllabic words presented to listeners with normal hearing in white noise with r egularly spaced interruptions and with irregularly spaced interruptions. Improve d performance was found for both interrupted conditions (regular and irregular) ; suggesting that interrupted noise can simulate intelligibility performance in r eal-world listening situations. Good temporal resolution may help a listener optimi ze the information gleaned during the available glimpses of speech. If the gl impses of speech are not used effectively (i.e., the listener cannot resolve the dips or gaps in background noise), then the listener has greater difficulty understand ing speech in background noise (Moore et al., 1999; Peters et al., 1998). As noted above, decrements in temporal resolution h ave been associated with reduced identification of words presented in n oise (Snell & Frisina, 2000; Tyler et al., 1982) and poor sentence recognition i n the presence of noise (Gifford et al., 2007; Lutman, 1991). Important to understanding the speech-innoise difficulties expressed by older listeners are two psychophysical measures

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9 of temporal resolution that use a gap as the cue of interest (temporal masking and gap detection). As with most, if not all, audi tory functions, temporal masked thresholds, GDTs, and speech-in-noise perception ar e poorer among older listeners with and without hearing loss than young listeners (Gehr & Sommers, 1999; Gifford & Bacon, 2005; Gordon-Salant & Fitzgi bbons, 1993; Lister, Besing, & Koehnke, 2002; Snell & Frisina, 2000; Tyler et al ., 1982). Slow recovery from forward masking, or increased t needed to approach unmasked threshold, in older hearing impaired listeners as compared to you nger listeners is thought to negatively influence the ability to process tempora l dips in background noise (Moore et al., 1999). If so, then performance on m easures of temporal masking, especially forward masking, should be predictive of performance on measures of gap detection and speech in interrupted noise. Temporal Masking When an interfering sound interacts with a target s timulus, such that portions of the target become inaudible, masking is said to occur. Forward masking (FM) occurs when a masking stimulus is pres ented before the target stimulus. Backward masking (BM) occurs when a mask ing stimulus is presented after the target stimulus. Forward-backward maskin g (FB-M) occurs when a masking stimulus is presented both before and after the target stimulus. The time between the offset of the target and the onset of the masker (BM) or the offset of the masker and onset of the target (FM or FB-M) is termed delta t. Delta t (t) is reported as a positive number when describing the timing difference between the masker and target in a FM or FB-M parad igm and a negative

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10 number when describing the timing difference betwee n the masker and target in a BM paradigm. In temporal masking paradigms, an i ncrease in the target threshold occurs when the target and masker occur i n close temporal proximity. Forward, backward, and forward-backward masking can collectively be called temporal masking. The function for FM is linear (i n log time), with masking effects ceasing around 200-ms following masker offs et (Wilson & Carhart, 1971). Wilson and Carhart found that the FM slope decayed ~ 6-dB with each doubling of t (1-ms to 250-ms). Temporally, BM is about 10% th e duration of FM and has a slope function that is much steeper than the slope function of FM. The slope function for BM is bimodal, being very steep ( ~ 1.7-dB/ms) from -20-ms to the onset of the masker and very gradual ( ~ 0.02-dB/ms) from -20-ms to -250-ms (Wilson & Carhart, 1971). An unexpected increase i n the target threshold, which is more than expected by the additive effects of re sidual forward and backward masking, occurs when forward and backward maskers a re presented simultaneously at interburst intervals 200-ms (Wilson & Carhart, 1971; Robinson & Pollack, 1973). Temporal masking may influence GDTs by obscuring th e cues necessary to identify the small, silent gaps within the stimu lus paradigm. Temporal masking may also contribute to poor understanding of speech presented in background noise that has an amplitude modulation characterist ic. Some authors suggest that a reduced temporal recovery from masking may c ontribute to the speech-innoise difficulties experienced by older listeners ( Dubno et al., 2002; Stuart & Phillips, 1996). FM thresholds are negatively corr elated (decreased speech-in-

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11 noise performance as FM thresholds increased) with the performance of younger and older listeners with normal hearing (for pure t ones) when identifying vowelconsonant syllables presented in the presence of mo dulated noise (Dubno et al., 2002; 2003). Decreased recovery from temporal mask ing may reduce the ability to take advantage of the speech cues available in t he background noise modulations. Stimulus Effects The effects on temporal masking of the stimulus par adigm, level, duration, and frequency of the signal and masker have been in vestigated. Elliott (1962) studied stimulus paradigm effects when white noise maskers and noise burst targets were presented to the same ear (monotically ) and when the target stimuli and maskers were presented to different ears (dicho tically) in forward and backward masking tasks. The results indicated larg er (e.g., ~25 dB at 0 t for FM and ~40 dB at 0 t for BM) threshold shifts to monotic presentation of the stimuli as compared to dichotic presentation of the stimuli. Masker level can also have an effect on temporal ma sking. In a FM paradigm, Jesteadt, Bacon, & Lehman (1982) used a s inusoidal masker and a sinusoidal target signal, matching in frequency (12 5-4000 Hz). They found the slope of the masking function to become less steep as the masker level decreased. Similar results were found by Moore et al., (1988), using band pass noise to mask a sinusoidal target in a FB-M paradig m. Moore et al. suggested that the auditory system responds non-linearly with changes in masker level, resulting in masking function slope differences as masker level changes.

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12 There is some discrepancy as to whether masker dura tion has an effect on temporal masking thresholds. Wide-band masker d urations (25-, 50-, and 100-ms) had no effect on BM functions (Elliott, 196 2b). Similar results were found using forward wide-band masker durations of 2 5and 200-ms (Elliott, 1967). However, other studies have shown that ther e is a duration effect for forward masker durations (35-to 500-ms); FM thresho lds increase with increasing masker duration (Kidd & Feth, 1982; Zwicker, 1984). Signal duration effects have also been investigated Oxenham (2001) examined FM thresholds in young listeners with norm al hearing using a 200-ms broadband masker and a 4000 Hz signal of varying du rations (2-, 4-, 7-, 10-, 2050and 100-ms). Results suggested that masked thr esholds decreased as signal duration increased at short t (e.g., at a 9-ms t, thresholds decreased by 14-dB as the signal duration increased from 2to 7 -ms). Gehr and Sommers (1999) found that reducing the duration of 500 Hz t one from 10-ms to 5-ms resulted in increased BM thresholds. However, the slope of the masking function remained stable. Signal duration (3.3-ms as compar ed to 6.6-ms) does not appear to have an effect on the shape of masking fu nction for FB-M, as long as the duration of the signal does not exceed the dura tion (~ 8-ms) of the auditory temporal window (Moore et al., 1998). The auditory temporal window refers to a temporal epoch in which the auditory system integra tes information (Hall, Buss, & Grose, 2007). Signal and masker frequency may have an effect on t emporal masking. Masked thresholds for a low frequency signal (500 H z) were more elevated than

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13 for higher frequency signals (1000 and 4000 Hz) in a FM task (Elliott, 1962b). The opposite results were found for BM (Elliott). More masking occurred for high frequency signals (4000 Hz) as compared to a low fr equency signals (500 Hz). However, Patterson found different BM results. In his study, BM behaved in response to signal frequency (500, 1500, and 2500 H z) similarly to FM, with more masking occurring as frequency signal decrease d. Patterson also found more masking to occur with decreasing signal freque ncy in a FB-M paradigm. Plack and Moore (1990) examined FB-M functions in y oung listeners with normal hearing using 200-ms maskers band pass filtered at 0.5 and 2.0 times the center frequency of 10-ms pure tone signals (900, 2700, 81 00 Hz). A 1000 Hz band pass filter was used at the lowest signal frequency (300 Hz). Plack and Moore found a more gradual FB-M function for low frequenc ies (300 Hz) and steeper functions for higher frequencies (900, 2700, 8100 H z). The rate of threshold decrease with increasing t was similar for the three higher frequencies. Masking function slopes are shallower for maskers a nd targets differing in frequency (off-frequency) as compared to on-frequen cy conditions (Nelson & Freyman, 1987; Nelson & Pavlov, 1989; Rosengard, Ox enham, & Braida, 2005). Forward-Backward Masking Several researchers have investigated the increased masking effect which occurs in a FB-M paradigm (Patterson, 1971; R obinson & Pollack, 1973; Wilson & Carhart, 1971). Combined forward and back ward masking results in larger masked thresholds than forward or backward m asking alone (i.e., excessive masking). Wilson and Carhart used noise maskers and a click to

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14 establish forward, backward, and forward-backward m asked thresholds at various ts. FB-M thresholds were examined at several interb urst intervals (IBI), or time differences between maskers (25-, 50-, 100, 200-, 300-, 400-, and 500ms). The excessive masking was not present at IBIs longer than 200-ms and increased as the IBI became shorter. Robinson and Pollack found similar results to those of Wilson and Carhart. However, in their study, only IBIs of 50-ms and less were used. The authors suggested that the aud itory system uses a temporal integration period or window to process th e target within the gap between maskers. This window appears to be asymmet rical, with more gradual slopes before the center of the temporal window and steeper slopes after the center of the temporal window (Oxenham & Moore, 199 4; Robinson & Pollack, 1973; Wilson and Carhart, 1971). However, the temp oral window does not fully model the excessive masking which occurs during FBM. The cochlea responds to sound in a compressive and non-linear way (Oxenh am & Bacon, 2003). The addition of compressive non-linearity in the model before the temporal window was suggested by Penner (1980). A model that inclu ded non-linear compression within a temporal window accounted for decreases in threshold as a function of t and the non-linear additivity, or excessive maski ng, seen during FB-M (Oxenham & Moore, 1994). Age and Hearing Loss Effects Several studies have investigated the effects of ag ing on temporal masking. Gifford and Bacon (2005) found no age-rel ated decrement in listeners aged 30 years or younger and 60 years or older on s everal tasks thought to

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15 measure cochlear non-linearity (auditory filter sha pe, psychophysical suppression, and growth of forward masking). All o f the listeners were considered to have hearing (for pure tones) within normal limits (thresholds of 15dB HL or lower at 250-6000 Hz). In a subsequent st udy, Gifford et al. (2007) found a subset of the same subjects (Gifford and Ba con) to exhibit poor speech understanding in modulated noise and increased forw ard masked thresholds, as compared to YNH listeners, indicating more suscepti bility to forward masking. Combined, the results of the two studies found that older listeners with no evidence of changes in cochlear linearity (Gifford & Bacon) exhibited poor speech understanding in modulated noise and increas ed FM thresholds (Gifford et al.). Temporal masking may not be influenced by cochlear processes (i.e., changes in non-linearity), but may reflect a more c entral process (Gifford et al.). Gehr and Sommers (1999) investigated age-related ef fects on BM. Younger listeners (18-24 years) and older listeners (67-82 years of age) with normal hearing (20-dB HL at 4000 Hz and below) participated. A 10ms, 500 Hz signal and a broadband masker were used. ts included -1, -2, -4, -6, -8, -10 and -20 ms. The older listeners had higher masked thresh olds and slower temporal recovery from masking than the younger lis teners. These results support age-related declines in BM, independent of hearing loss. Gehr and Sommers suggest that both central and peripheral fa ctors may play a role in temporal masking. Dubno et al. (2002; 2003) found age-related increa ses in FM thresholds between older listeners (60-74 years of age) and yo unger listeners (20-27 years

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16 of age). All of the listeners had audiometric thre sholds < 20-dB HL at 250-4000 Hz. The FM thresholds were 3to 4-dB higher for t he older listeners as compared to the younger listeners. However, when t he thresholds for the stimuli in quiet were taken into account (a difference scor e was established by subtracting the threshold in quiet from the masked threshold in each condition), no differences between age groups were noted. Agerelated differences were attributed to elevated quiet thresholds to the targ et stimulus for the older listeners. Hearing loss may also have an effect on temporal m asking. Oxenham and Moore (1995) found differences between younger listeners (aged 24-30 years) and older listeners with sloping sensorineur al hearing loss (aged 73-77 years) on FM, BM, and FB-M. A 2-ms, 4000 Hz tone a nd a 200-ms broadband noise masker were used in all three listening condi tions. For the FM and BM conditions, the masker level was varied to establis h threshold, while the signal levels were fixed at 5, 10, 15, 20, and 25 dB above quiet threshold. Delta ts of 5, 10and 25-ms were used for the FM condition and ts of 1and 5-ms were used for the BM condition. Masking functions (effe ctive masker level on the yaxis, and tone levels on the x-axis) were plotted f or each listener in each condition. For the FB-M condition, the tone level was varied to establish threshold whereas the masker level remained stable. The three FM and two BM conditions described above were combined to create six FB-M conditions. For the younger listeners with normal hearing, the FM a nd BM thresholds had nonlinear masking functions, which became more pronoun ced with increasing t.

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17 The masking functions for the older hearing impaire d listeners were more linear. FB-M thresholds for the younger listeners showed an increase in masking effect, more than expected from the additive effects of for ward and backward masking (excess masking). The older hearing impaired liste ners had limited excess masking. The authors suggest that changes in the n on-linearity of the basilar membrane may account for the differences in excess masking observed between the younger and older listeners. Hearing impaired listeners may also have difficult y processing sequential sounds. Kidd, Mason and Feth (1984) examined the g rowth of FM as a function of masker level. The listeners were young adults w ith and without hearing loss. A 3000 Hz tone and masker were used. FM thresholds increased as a function of masker level for both groups, but grew at a stee per rate for the hearing impaired listeners. Gap Detection and Gap Discrimination A gap detection threshold (GDT) is the smallest inc rement of silence, or gap, between two stimuli (e.g., tones or bands of n oise) that can be identified by a listener (Phillips, Taylor, Hall, Carr, & Mossop, 1997; Plomp, 1964). The terms gap detection and gap discrimination are not used consistently in the literature. Both have been used to describe similar stimulus pr esentation methods. Philips et al. (1997) established GDT s using a 2 interval, 2 alternative forced choice method with a 1-ms standard gap. Lister et al. (20 00; 2002) used a similar method, but used the term gap discrimination Lister and Roberts (2005) later used the term gap detection for this paradigm. Grose et al. (2001) used gap

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18 discrimination to describe a task using standard gaps of 35and 250-ms. Grose, Hall, Buss and Hatch (2001) used the term gap detection when using a no-gap standard stimulus. There appears to be a lack of c onsensus in the literature regarding the terminology for a paradigm when the s tandard gap is very brief. Gap detection thresholds are often measured by pr esenting the listener with two or more intervals: one or more standard in tervals that contain a continuous or two contiguous signals and a signal t hat contains a silent pause or gap. Thresholds are established by asking the list ener to determine which of the intervals contains a silent gap. The duration of t he gap is varied to establish the shortest detectable gap (threshold) for that listen er. Gap discrimination tasks require the listener to compare one or more standar d intervals that contain a gap of fixed duration and a target interval that contai ns a gap of varying duration. Gap difference thresholds are established as the sh ortest gap duration increment that a listener can discriminate from the standard gap. The stimuli before and after the gap are called markers. Commonly, the st imulus before the gap is called the first marker and the stimulus following the gap is called the second marker. GDTs for broadband noise markers are usually small (3to 5-ms) for young listeners with normal hearing (Florentine & B uus, 1984). Some have suggested that, to detect a gap, listeners rely on the decrease in the level of the signal associated with the gap, rather than onset o r offset cues (Allen, Virag, & Ison, 2002). Florentine, Buus, and Geng (1999) sug gested that the onset

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19 response to the second marker may provide cues used for detecting gap durations above hearing threshold. Stimulus Effects When the stimuli bounding the silent gap are spectr ally similar and are presented to one ear (monotic) or both ears at the same time (diotic), the paradigm is considered to be within-channel When the stimuli bounding the gap are spectrally similar, but are presented to each e ar separately (dichotic), the paradigm is called across-channel or, more specifically, across-ear. When the stimuli bounding the gap are different in frequency and are presented diotically or monotically, the paradigm is also called across-cha nnel, or, more specifically, across-frequency. Dichotically presented markers t hat are different in frequency are called across-both. Stimuli with similar marke rs presented dichotically produce larger GDTs than the same stimuli presented diotically or monotically (Formby, Gerber, Sherlock & Magder, 1998; Phillips et. al, 1997). Gap detection thresholds are smaller when the markers bounding th e gap are similar in frequency than when they are frequency disparate (G rose et al., 2001; Lister et al., 2002). Some studies have found larger GDTs us ing the across-both condition, than either an across-frequency or dicho tic condition alone (Phillips et al., 1997; Taylor, Hall, Boehnke, & Phillips, 1999) However, other studies have found no significant difference in GDTs using acros s-frequency and across-both presentation paradigms (Formby et al., 1998; Lister & Roberts, 2005). Acrosschannel GDTs increase as the frequency disparity be tween markers increases (Grose et al., 2001; Lister et al., 2000). Withinchannel tasks are thought to be

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20 processed by one perceptual channel, whereas across -channel tasks may be processed by more than one perceptual channel (Phil lips et al., 1997; Lister et al., 2002; Lister & Roberts, 2005). Gap detection requiring the use of one perceptual channel may be measuring a different pro cessing task than gap detection using multiple perceptual channels (Phill ips et al, 1997). Electrophysiological measurement supports this hypo thesis. Across-channel stimuli show larger waveform amplitudes, shorter wa veform latencies, and differing scalp topography than within-channel stim uli (Lister, Maxfield, & Pitt, 2007). Other marker characteristics such as frequency, ban dwidth, type of noise stimulus, and duration can influence GDTs. Fitzgib bons (1983) suggested that frequency is the predominant marker feature influen cing gap detection and optimal gap detection occurs when markers include h igh frequency components. In particular, inclusion of spectral information in the region of 6000 Hz appears to produce the best GDTs. Using narrowband noise stim uli, GDTs decrease with increasing marker center frequency (De Filippo & Sn ell, 1986; Gordon-Salant & Fitzgibbons, 1987; Snell, Ison, & Frisina, 1994). However, other studies indicated that bandwidth is a more important factor than frequency for gap detection. Using markers with a narrow frequency r ange, Eddins, Hall, and Grose (1992) suggested that increased marker bandwi dth may result in smaller GDTs. Snell et al. (1994) extended these results b y varying bandwidth over a wide frequency range. When marker bandwidth was at least half of the upper cut-off frequency, GDTs were influenced more by upp er cut-off frequency than

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21 bandwidth. However, when the marker bandwidth was less than half of the upper cut-off frequency both factors (frequency and bandwidth) influenced GDTs. The largest mean GDT (6.98-ms) was established when the stimulus had a bandwidth of 1000 Hz and an upper cut-off frequency of 12000 Hz. Conversely, the smallest mean GDT (2.22-ms) was established whe n the bandwidth and the upper cut-off frequency of the stimulus were 12000 Hz. GDTs are small for wideband noise markers (Florentine & Buus, 1984) and la rger for narrow band noise markers (Fitzgibbons & Gordon-Salant, 1987). Rando m fluctuations are present in narrow band noise to a greater extent than in br oad-band noise. These random fluctuations may be confused with the silent gap, causing listener uncertainty, resulting in larger GDTs for narrow ba nd noise markers (Glasberg & Moore, 1992). Although Jerger and Musiek (2000) recommend the use of broadband markers for clinical gap detection, there are some inherent disadvantages in using broadband noise to mark a silent gap. Hearin g loss effectively filters the broadband noise, narrowing marker bandwidth. As no ted above, narrowing marker bandwidth generally results in larger GDTs. In addition, overall loudness of broadband markers is often difficult to equalize across intervals when one interval contains a continuous noise burst and the other two noise bursts are separated by a gap. Using broadband noise stimuli also limits the frequency specificity of the task (Lister, Roberts, Shackelfo rd, & Rogers, 2006). Although the above limitations exist, broadband stimuli will be used in this study to

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22 maintain consistency among temporal listening tasks (temporal masking and gap detection). Marker duration may also influence GDTs (Formby & M uir, 1989). Schneider and Hamstra (1999) found that GDTs did no t vary for tonal markers of equal duration (2.5-ms to 250-ms) on either side of the gap. He, Horwitz, Dubno, and Mills (1999) found that GDTs for a silent gap i nserted in the center of a 400ms noise burst were smaller than thresholds for gap s inserted in the center of a 100-ms noise burst. Gross et al. (2001) investigat ed gap duration discrimination to stimuli with varying first marker duration (50or 300-ms) and stable second marker duration (300-ms). The results showed small er gap duration discrimination to the shorter first marker. There are three common ways in which marker duration is manipulated across intervals: (1 ) marker duration is shortened relative to the gap duration so that the interval d uration remains constant but the interval with the gap has shorter markers than the interval without a gap; (2) marker duration is maintained so that the interval with the gap is longer than the interval without the gap; and (3) the second marker duration is varied randomly to reduced any timing cues. Fixed-duration intervals and fixed-duration markers may provide extraneous duration cues, which can be used by the listener to detect silent gaps (i.e., listener may respond to l onger interval or shorter markers rather than to gap). Lister and Tarver (2004) foun d that randomized marker duration may reduce these cues, preventing inaccura te (more sensitive) GDTs. The present study used second markers that randomly varied in duration between 250-to 350-ms in order to reduce any extran eous marker duration cues.

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23 Age and Hearing Loss Effects Several research teams have investigated the effect of hearing loss on gap detection and discrimination. Tyler et al., (1 982) investigated the ability of listeners with and without hearing loss to discrimi nate the duration of tones and silent gaps. Listeners with hearing impairment (me an age = 53 years) had larger GDTs than listeners with normal hearing (mean age = 23 years). However, the age differences between the two groups of listeners in the study cannot exclude age-related effects. Fitzgibbons and Gordon-Salant (1987) assessed the GDTs of listeners with normal hearing (25-40 years of ag e) and listeners with hearing impairment (43-60 years of age). Using a Bksy tr acking procedure, listeners tracked the minimum signal level to maintain the ga p at threshold levels. A minimum trackable gap (MTG) was established for all of the listeners. Significant differences were found between the MTG for the list eners with normal hearing and the MTG for the listeners with hearing impairme nt for all of the stimuli presented. The listeners with hearing impairment i n both of these studies (Tyler et al., 1982; Fitzgibbons & Gordon-Salant, 1987) de monstrated increased GDTs, suggesting that hearing loss can have a detrimental effect on temporal resolution. However, age may have been a contribut ing factor to the results of both studies. Increased GDTs among adults with hearing loss may b e related to hearing loss-related loss of cochlear non-linearity which c auses abnormal growth of loudness (loudness recruitment). Loudness recruitm ent could cause increased temporal fluctuations that could confound the detec tion of a silent gap (Glasberg

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24 & Moore, 1992; Moore & Oxenham, 1998). This hypoth esis has been supported by the finding that GDTs for listeners with hearing impairment improve when compression of the level fluctuations in the marker stimuli is simulated (Glasberg & Moore, 1992). Other studies have attempted to separate the effect s of age from the effects of hearing loss on temporal resolution. Li ster et al. (2000) examined withinand across-channel gap duration discriminat ion ability of listeners with normal hearing (aged 21-51) and listeners with hear ing impairment (aged 21-71). GDTs thresholds increased as the frequency separati on between markers increased. No significant difference was found bet ween the two groups, suggesting that hearing impairment does not impact withinor across-channel GDTs. When the data were assessed with age as a fa ctor, significant differences in GDT function slopes were seen betwee n age groups. The function slopes were steeper for the listeners over the age of 40 than the function slopes for the listeners under the age of 30, regardless o f hearing status. Other studies have supported the hypothesis that ag e, independent of hearing loss, influences gap detection and discrimi nation. Grose et al. (2001) investigated the gap discrimination ability of two groups of middle-aged listeners, one group was comprised of listeners with hearing l oss (aged 29-56 years) and the other group was comprised of listeners without hearing loss (aged 46-54 years). Since both groups were approximately the s ame age, the study investigated the effects of hearing loss alone on g ap duration discrimination. No significant difference between the two groups was f ound for stimuli of varying gap

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25 duration or varying first marker duration. These r esults suggest that hearing impairment does not affect gap discrimination, whic h is in contrast to the conclusions made by Fitzgibbons and Gordon-Salant ( 1987) and Tyler et al. (1982). Further, when Grose et al. (2001) complete d a subsidiary study including young listeners with normal hearing, the younger li steners had significantly better gap duration discrimination performance than middle -aged listeners with normal hearing. The results suggest that decrements in te mporal resolution may occur as age increases. Lister et al. (2002) examined the GDTs of young (18 -30 years), middleaged (40-52 years), and older listeners (62-74 year s) using withinand acrosschannel markers. All of the listeners had normal h earing ( 25-dB HL, 250 – 6000 Hz, and 30-dB HL at 8000 Hz). Lister et al. found no signi ficant difference between the GDTs of the younger and the middle-aged listeners. The older listeners had significantly larger GDTs than both t he younger and middle-aged groups. No hearing loss effects were noted, suppor ting the conclusion that age alone can have an effect on temporal resolution. G rose, Hall, and Buss (2006) completed an additional analysis of the data from t he Lister et al. (2002) study. The results showed a significant difference between the GDTs of the young and middle-aged listeners for the most difficult of the six marker conditions, when the markers differed by two octaves, extending age effe cts into middle-age. The effects of age on GDTs have been established in a variety of listening paradigms. Older listeners exhibit poor discrimina tion of gaps presented in complex tonal stimuli (Fitzgibbons & Gordon-Salant, 1995). Lister et al. (2002)

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26 found age-related differences as the frequency diff erence between markers increased during a gap detection task. The GDTs of the older listeners increased at a faster rate than the GDTs of the younger liste ners as the difference in center frequency of the markers increased. The effect of background noise on GDTs was investigated by Snell (1997). The gap threshol ds for the older listeners were more affected by the presence of low pass noise as compared to the GDTs of the younger listeners. Age-related differences in gap detection have been found when the gap is located close to marker onset or of fset as compared to a gap placed at the temporal center of a 100-ms noise bur st. For older listeners (n = 6, mean age = 70.5 years), GDTs increased as the gap m oved from the center of the noise burst toward the onset or offset of the n oise. For young listeners (n = 7, mean age = 31.9), no change in GDT was found wit h change in gap location (He et al., 1999). Older listeners also had larger GDTs than younger listeners when marker durations are less than 250-ms (Schneid er & Hamstra, 1999). Lister and Tarver (2004) found older listeners had larger GDTs as compared to the GDTs of younger listeners when presented with s ynthetic speech markers. They also found that the older listeners appeared t o take greater advantage of extraneous marker duration cues than young listener s in a fixed-duration condition (marker duration varied as gap duration v aried) as compared to a random-duration condition. Although age effects we re noted in both conditions, older listeners appeared to use extraneous marker d uration cues to achieve smaller GDTs in the fixed-duration condition as com pared to the random-duration condition.

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27 Age-related differences have been observed for temp oral cues in a speech context, specifically variances in speech si lence and transition durations (Gordon-Salant, Yeni-Komshian, Fitzgibbons, & Barre tt, 2006). All of the results suggest that older listeners experience an age-rela ted decrement in temporal resolution across a variety of stimuli and backgrou nd noise situations that is independent of hearing impairment. One of the fact ors that may influence performance on tasks of gap detection is temporal m asking. As stated earlier, marker onset cues are thought to be important for g ap detection. Increased temporal masking, due to sounds before and/or after the gap, may cause these cues to be obscured. Summary Older listeners more often than not report difficul ty understanding speech in background noise. Fine processing of temporal c ues is important for understanding specific cues in speech. Listeners a lso rely on temporal resolution to glean auditory glimpses of the signal available in the amplitude and frequency fluctuations inherent in background noise. Several studies suggest that decreased temporal resolution may contribute to the speech-in-noise difficulties reported by older listeners. Common measures of te mporal resolution include temporal masking, gap detection and speech understa nding in interrupted noise. One temporal masking paradigm, forward-backward mas king, is similar to gap detection and speech in interrupted noise parad igms. All three paradigms include relatively broad-band stimuli bounding a si lent interval. The difference among the paradigms is the psychophysical task. Ga p detection paradigms

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28 measure the shortest increment of silence detectabl e between two stimuli. Forward-backward masking paradigms establish a thre shold to a brief stimulus (e.g., a click) placed at various intervals within a silent interval between two maskers. Speech in interrupted noise paradigms est ablish a threshold to a speech stimulus presented in background noise conta ining repeated silent intervals. For both temporal masking and speech in interrupted noise tasks, the listener is asked to process information in the gap, rather than the timing of the gap. The two tasks differ in the type of informati on contained within the gap (i.e., click vs. speech). In separate studies, older listeners with hearing l oss have been found to have higher FM, BM, and FB-M thresholds (Oxenham & Moore, 1995), longer GDTs (Fitzgibbons & Gordon-Salant, 1987, Tyler et a l., 1982), and smaller release from masking during a speech-in-interrupted noise task (Wilson et al., 2010) as compared to younger listeners with normal hearing. In addition, young listeners with normal hearing experience excessive or additive masking in a FBM paradigm, while older listeners with hearing loss do not (Oxenham & Moore, 1995). The effects of age and hearing loss on all three types of tasks (temporal masking, gap detection, and speech understanding in interrupted noise) has not been examined in a single study using the same part icipants. The relations among forward-backward masked thresholds, gap detec tion, and performance on a speech-in-noise task are unknown. As a primary goal, this study sought to determine t he effects of age and hearing loss on all three measures: temporal maskin g, gap detection, and

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29 speech recognition in interrupted noise. As a seco ndary goal, this study sought to determine the relations among measures of tempor al resolution (gap detection, forward masking, backward masking, forwa rd-backward masking, and speech-in-noise performance) between older listener s with hearing loss for pure tones and younger listeners with normal hearing for pure tones. Although relations are thought to exist among performances o n these three measures in the temporal domain, they are rarely studied togeth er and have not been studied together in our population of interest, older adult s with hearing loss. Performance on tasks measuring temporal masking and gap detection may, in theory, predict performance on speech tasks in the presence of background noise. Driving this hypothetical relationship are the results of studies of one or more measures in specific populations (Dubno et al. 2002; Gifford & Bacon, 2005; Kidd et al., 1985; Oxenham & Moore, 1995; Smi arowski, 1970) and the notion that a common temporal processing ability ma y underlie performance on all three tasks. This idea is attractive because, if a single process is important for all three tasks in all potential patients, then rem ediation will be very straightforward. The extent to which performance o n the psychoacoustic tasks of temporal masking and gap detection is related to sp eech in noise performance is, as yet, unclear, and the importance of this potenti al relationship for clinical practice of audiology has not been determined. For a summary of relevant literature, see Table 1.

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30 Table 1 Summary of research assessing temporal resolution a bilities in a variety of populations Task Listeners Results Reference FM YNH YNH/YHI YNH/ONH YNH/OHI Decreasing threshold with increasing t Slope decays ~ 6-dB with each doubling of t Steeper slopes for YHI (0.58-dB/ms) Poorer FM thresholds for YNH OHI have poorer FM thresholds and linear masking functions Wilson & Carhart, 1971 Kidd, et al., 1984 Dubno et al., 2002, 2003; Gifford & Bacon, 2005 Oxenham & Moore, 1995 BM YNH YNH/ONH YNH/OHI Slope is bimodal ~ 1.7-dB/ms from -20-ms to the onset of the masker and very gradual ( ~ 0.02-dB/ms) from -20-ms to 250-ms ONH BM function slopes were found to be 0.9-dB/ms, higher BM thresholds. OHI have higher BM thresholds and linear masking functions Gehr & Sommers, 1999; Wilson & Carhart, 1971 Gehr & Sommers, 1999 Oxenham & Moore, 1995 FB-M YNH YNH YNH/OHI Increased thresholds at the extremes of the FB-M functions with lower thresholds towards the center of the ISI. Excessive masking for non-overlapping maskers (~9-dB) No excessive masking in FB-M condition for OHI listeners. Gaskell & Henning 1999; Penner, 1980; Wilson & Carhart, 1971 Wilson & Carhart, 1971; Penner, 1980 Oxenham & Moore, 1995 GDT YNH/YHI YNH/ONH YNH/OHI Smaller GDTs for YNH (3.3-ms) compared to YHI (7.3-ms) Larger GDTs for YNH compared to ONH; Larger GDTs for middle-aged compared to YNH on more difficult tasks Larger GDTs for OHI listeners Florentine & Buus,1984 Lister et al, 2002; Grose et al., 2006 Fitzgibbons & GordonSalant, 1987, Tyler et al., 1982 Speech in noise YNH/ONH/OHI YNH/OHI YNH-best performance, followed by ONH, worst performance by OHI YNH show more release from masking Stuart & Phillips, 1996 Wilson, et al., 2010

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31 Research Questions Hypotheses Previous research suggests that aging and hearing l oss may have an influence on temporal resolution. One alternative hypothesis (HA) is that older listeners with hearing loss will demonstrate higher temporal masking thresholds, longer GDTs, and poorer speech understanding in noi se than younger listeners with normal hearing. Longer recovery from temporal masking may cause larger GDTs. A moderate correlation between psychophysica l measures of temporal resolution (both temporal masking and GDTs) and spe ech understanding in noise has been demonstrated in specific contexts. Another hypothesis (HA), therefore, is that performance on tasks of temporal masking will significantly related to and predictive of performance on tasks r equiring speech understanding in noise. Questions Temporal resolution was measured using young listen ers with normal hearing and older listeners with hearing impairment Specifically, psychoacoustic temporal resolution measures included forward, back ward, and forwardbackward masking and GDTs to within-channel stimuli Speech understanding in interrupted noise was measured using the Words-In-N oise test (WIN) paradigm in which continuous and interrupted speech-spectrum noise (SSN) was substituted for the nominal multitalker babble used with the WIN (Wilson, 2003; Wilson, McArdle, Betancourt, Herring, Lipton, & Chi solm, 2010). This study was designed to answer the following questions:

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32 1. Do older listeners with hearing loss (OHI) exhib it higher temporal masking, longer gap detection, and poorer speech un derstanding in noise than younger listeners with normal hearing (Y NH)? a. Will OHI listeners have higher forward, backward and forwardbackward masked thresholds than the YNH listeners? b. Will OHI listeners have more threshold shift in the temporal masked conditions? c. Will only the YNH listeners have excess masking in the forwardbackward masked condition? d. Will the OHI listeners have larger GDTs than the YNH listeners? e. Will the YNH listeners have more release from ma sking in the speech in interrupted noise condition? 2. Will performance on tasks of temporal masking, g ap detection, and speech understanding in noise be strongly related? a. Will performance on temporal masking and GDT tas ks predict performance on speech understanding in interrupted noise tasks?

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33 Chapter Three Method This study was designed to investigate the differen ces in performance between young listeners with normal hearing (YNH) a nd older listeners with hearing impairment (OHI) on tasks of temporal maski ng thresholds, GDTs, and recognition performance on a speech-in-noise task. A secondary goal was to examine relationships among task performances withi n each group. The masking paradigms included traditional simultaneous masking, forward masking, and backward masking tasks as well as the temporal masking that occurs between two maskers, which is called forward-backwa rd masking. The gap detection paradigm consisted of a within-channel ga p detection task. Speechrecognition performance in noise was measured using the Words-In-Noise (WIN) test paradigm with speech-spectrum noise used as th e masker instead of multitalker babble. In addition to continuous noise, sp eech recognition was evaluated using three conditions of interrupted noise. Participants Participant Selection Listeners were recruited from undergraduate and gra duate classes in the Department of Communication Sciences and Disorders at the University of South Florida (USF), the USF Hearing Clinic, the communit y surrounding the USF Tampa and St. Petersburg Campuses, and from the Aud iology Clinic at Bay

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34 Pines VA Healthcare System (VAHCS). Students were given extra credit for participating at the discretion of their instructor Non-student participants were paid for their time. A total of 8 younger listener s with normal hearing (mean age = 25.4, SD = 2.2) and 9 older hearing impaired list eners (mean age = 72.1, SD = 5.3) participated in the study. Documented informe d consent was obtained from qualifying volunteers after an initial history was completed. Approval for this study was obtained from the Institutional Review Bo ard and the Research and Development Committee at Bay Pines, VAHCS, and the USF Institutional Review Board. Inclusion/Exclusion Criteria. The listeners had no history of middle-ear disease, stroke, history of neurological disease, o r exposure to ototoxic drugs. If a current audiogram (within 6 months) was available [including pure tone thresholds (250-8000 Hz), tympanometry, and ipsilat eral/contralateral reflex thresholds], then pure-tone thresholds at 500, 1000 and 2000 Hz were confirmed for the better ear, or right ear in cases of symmet rical hearing. If no current audiogram was available, then pure-tone thresholds (250-8000 Hz), tympanometry, and ipsilateral/contralateral reflex thresholds were obtained. Admittance tympanograms at 226 Hz were within norma l limits and reflex thresholds were appropriate for hearing thresholds. Listeners were divided into two groups: YNH and OHI. The YNH had hearing thres holds 20-dB HL from 250 to 8000 Hz. Table 2 shows the hearing threshold s for the YNH listeners. The OHI had hearing thresholds meeting the followin g criteria: (1) 30-dB HL (ANSI, 2004) at 500 Hz, (2) 40-dB HL at 1000 Hz, (3) 40-dB HL at 2000-8000

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35 Hz, and (4) a pure-tone average of < 40-dB HL at 500, 1000, and 2000 Hz. Table 3 shows the hearing thresholds for the OHI listener s. Table 2 Pure tone thresholds (in dB HL) and pure tone avera ge (PTA) for young listeners with normal hearing. PTA is the average of 500, 10 00, and 2000 Hz. Subject Age 250 Hz 500 Hz 1000 Hz 2000 Hz 3000 Hz 4000 Hz 6000 Hz 8000 Hz PTA Test Ear 1 22 15 10 10 5 0 0 15 0 8.3 Right 2 23 5 0 0 0 10 0 15 10 0.0 Right 3 25 15 10 15 15 15 15 15 10 13.3 Right 4 25 5 5 -5 -5 -5 0 0 5 1.7 Right 5 28 -5 -10 5 0 -5 5 5 -5 -8.3 Right 6 27 0 -5 0 -5 -5 10 10 5 -3.3 Right 7 27 0 0 0 -5 -5 0 0 -5 -1.7 Left 8 25 10 0 10 5 0 5 0 5 5.0 Right Table 3 Pure tone thresholds (in dB HL) and pure tone avera ge (PTA) for older listeners with hearing impairment. PTA is the average of 500 1000, and 2000 Hz. Subje ct Age 250 Hz 500 Hz 1000 Hz 2000 Hz 3000 Hz 4000 Hz 6000 Hz 8000 Hz PTA Test Ear 1 77 25 30 30 45 50 55 65 80 35.0 Right 2 64 30 30 35 55 60 65 70 75 40.0 Left 3 76 10 10 15 65 70 70 70 60 30.0 Right 4 71 20 15 20 40 40 45 40 40 25.0 Right 5 81 25 30 35 55 65 70 65 80 40.0 Right 6 73 35 25 20 60 90 100 105 100 35.0 Left 7 69 25 30 25 40 45 45 60 65 31.7 Left 8 67 25 20 20 40 40 40 55 65 26.7 Left 9 71 20 20 25 45 50 60 65 75 30.0 Right Stimuli Stimulus Generation Masker/Marker Stimuli. Broadband masker/marker stimuli were used for all temporal masking and gap detection measures. S timulus generation and presentation timing was controlled by Tucker Davis Technologies (TDT, Alachua, FL) software (Real Time Processor Visual Design Stu dio, RPvds Version 6.0) and hardware (Real Time Processor, Model RP2). Gau ssian noise was

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36 generated digitally with a sampling rate of 25000 H z and 0.1v root-mean-squared (rms) amplitude. Figure 1 shows a sample waveform of the broadband noise used to generate masker/marker stimuli as recorded through the computer sound card (A) and the headphones (B). Figure 1. Waveform of broadband noise used for maskers and ma rkers recorded through (A) the sound card and (B) the headphones. The noise was filtered using Butterworth filters wi th a low cut-off of 20 Hz and a high cut-off of 5500 Hz. The 250-ms broadband no ise was gated with a cosine2 function that produced 1-ms rise/fall times. Figu re 2 shows the spectrum of the noise as recorded through the computer sound card (A) and the headphones (B).

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37 Figure 2. Magnitude Spectrum of broadband noise used for ma skers and markers recorded through (A) the sound card and (B) the headphones. Click Stimuli. Click signals were used in the masking paradigms be cause clicks are essentially void of duration characteris tics, which eliminate probe duration effects. Clicks also have a broadband spe ctrum, which is similar to the spectrum of the masker. The click was generated di gitally using TDT RPvds with 0.4-ms duration, an instantaneous rise/fall time, a nd rms amplitude of 2.0 v. The click was then routed through an attenuator (TDT, M odel PA5) that varied the level adaptively during the temporal masking paradi gms to determine the click threshold. Figure 3 shows the waveform of the clic k stimulus as recorded through the computer sound card (A) and the headpho nes (B). Figure 4 shows the spectrum of the click as recorded through the c omputer sound card (A) and the headphones (B). The click stimulus was not use d during the gap detection paradigm. A screen shot of the RPvds programming f or stimulus generation is displayed in Appendix B. -10 0 10 20 30 40 50 60 70 010002000300040005000600070008000Magnitude Spectum of Broadband NoiseAmplitude (re: Maximum)Frequency (Hz) B: Headphones -10 0 10 20 30 40 50 60 70 010002000300040005000600070008000Magnitude Spectrum of Broadband NoiseAmplitude (re: Maximum)Frequency (Hz) A: Sound Card

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38 -15 -10 -5 0 5 10 15 0.5.10.15.20Click StimulusAmplitude (re: Maximum)Time (ms) A: Sound Card Figure 3. Waveform of click as recorded through (A) the sou nd card and (B) the headphones. -10 0 10 20 30 40 50 60 70 010002000300040005000600070008000Magnitude Spectrum of ClickAmplitude (re: Maximum)Frequency (Hz) A: Sound Card -10 0 10 20 30 40 50 60 70 010002000300040005000600070008000Magnitude Spectum of ClickAmplitude (re: Maximum)Frequency (Hz) B: Headphones Figure 4. Magnitude spectrum of click [headphones (a) and sou nd card (b)] Speech Understanding in Interrupted Noise. The Words-In Noise (WIN) Test paradigm (Wilson & Bu rks, 2005; Wilson, 2003) was used to assess speech understanding in no ise. The WIN uses monosyllabic words from the Northwestern University Auditory Test No. 6 (NU No. 6: Tillman & Carhart, 1966) spoken by a female speaker. The words were presented in the presence of speech-spectrum noise instead of the multi-talker babble usually used with the WIN (Wilson et al., 20 07). Performance on the -15 -10 -5 0 5 10 15 0.5.10.15.20Click StimulusAmplitude (re: Maximum)Time (ms) B: Headphones

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39 speech-in-noise task was measured in continuous noi se (i.e., 0 interruptions/second; IPS) and in noise interrupted 5, 10, and 20 times per second. The 70-word WIN can be divided into two, 35-word li sts, Lists 1 and 2 (Wilson and Burks, 2005) which, in a multiple condi tion experiment, spreads learning and fatigue effects across the conditions. Randomized versions of List 1 and List 2 were presented for each of the four list ening conditions, for a total of eight 35-word lists. One set of four interrupted n oise conditions was presented before the second set of interrupted conditions was presented. Both sets of four interrupted conditions were presented in random ord er. Five words were presented at each stimulus presentation level. The interrupted noise was created from speech spectrum noise with a sloping r esponse (+ 2-dB up to 1000 Hz, 12-dB/octave slope above 1000 Hz). Equal inter vals of noise were deleted using a waveform editor resulting in silence for ha lf of the interruption interval and noise for the other half of the same interval, a 50 % duty cycle. Each interruption resulted in 100-ms of silence and 100-ms of noise f or the 5 IPS condition; 50-ms of silence and 50-ms of noise for the 10 IPS condit ion; and 25-ms of silence and 25-ms of noise for the 20 IPS condition. The resul ting noise conditions, 5, 10, and 20 interruptions per second (IPS), correspond t o the forward-backward masking conditions of 100-, 50-, and 25-ms ISI, res pectively. The stimuli were the same as those used in Wilson et al., 2010). Fi gure 5 demonstrates presentation of the carrier phrase and stimulus wor d in the 5 IPS condition.

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40 Figure 5 Example of speech-in-noise stimuli. “Say the wo rd food” is presented while speech spectrum background noise is interrupt ed at a rate of 5 interruptions per second. The presentation level of the noise for the WIN was fixed at 80-dB SPL for both groups of listeners. The presentation level o f the words varied from 104-dB SPL (24-dB S/N) to 76-dB SPL (-4-dB S/N) in 4-dB decrements for the OHI listeners. Using the same stimuli, Wilson et al. ( 2010) found that YNH listeners require a poorer signal-to-noise ratio than the old er listeners to establish minimum and maximum performance on a psychometric f unction. Therefore, the presentation level of the noise and the signal-to-n oise ratios were adjusted accordingly, with a 20-dB S/N to -8 S/N for the 0 IPS condition, a -12 S/N to -40 S/N for the 5 IPS condition, and a -8 S/N to -36 S/N for the 10 and 20 IPS conditions (Wilson et al., 2010).

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41 Calibration The presentation level of the masker/marker noise w as calibrated using a sound-level meter (Brel and Kjr, Type 2250) and a 1/2 inch pressure-field microphone (Brel and Kjr, Type 4192) in a 6-cm3 coupler (Brel and Kjr, Type 4153) fitted with a flat plate adapter for the circumaural headphones. The level of the click was calibrated in peak-equivalen t sound-pressure level by recording the peak-to-peak amplitude of the click o n an oscilloscope (Tektronix, Model TDS 210). A 1000 Hz signal then was generate d to match the peak-topeak amplitude of the click. The level of the cali bration tone was calibrated to the peak amplitude of the click. The level of the cali bration tone was recorded as 102.8-dB SPL using the aforementioned calibration s ystem. The presentation level of the click was controlled by computer softw are and varied adaptively to establish click threshold. The timing of the stimu li (masker and click) was confirmed by recording the stimuli from the RP2 int o the computer (Lynx Studio Technology LynxONE Sound Card) using Adobe Audition 2.0. The intervals between the maskers and the click for forward and b ackward masking, between the maskers for forward-backward masking, the timin g of the click between maskers for forward-backward masking, and between m arkers for gap detection were also measured and confirmed using Adobe Auditi on 2.0. The duration of the maskers and markers was also measured and confi rmed in the same manner.

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42 Procedures Detection Thresholds Click and broadband masker thresholds were establi shed in quiet. The click or broadband masker was passed through a head phone buffer (TDT, Model HB7), and presented via Sennheiser HD 265 linear ci rcumaural headphones. The listeners were seated in an IAC double-walled s ound-attenuated booth (IAC, Model 120A) in front of a 43-cm LCD com puter monitor. All stimuli were presented to the better ear, or right ear in c ases of symmetrical hearing. Thresholds were established using a two-interval, t wo-alternative forced-choice procedure (2I/2AFC), targeting 70.7% correct (Levit t, 1971). In a 2I/2AFC paradigm, the listener is presented with two stimul i (i.e., intervals) in each trial. The click and masker threshold conditions included a target interval containing the click or masking noise and a standard interval of silence. Visually, a gray box labeled “A” appeared on the left side of the comput er screen accompanied by the first auditory stimulus interval. Then, 250-ms lat er, a gray box labeled “B” appeared on the right side of the computer screen a ccompanied by the second auditory stimulus interval. Both boxes remained di splayed on the computer screen until the end of each trial (see Appendix B for example screen shot). The timing of the visual boxes was controlled by the ma ster clock of the computer. The listener was instructed to choose the appropria te interval (i.e., the interval containing the click or masker) by using the comput er mouse to select the corresponding box displayed on the screen. A two-d own, one-up procedure was used; the level of the target stimulus was lowered one step for two consecutive

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43 correct answers and raised one step for every incor rect answer. During an adaptive procedure, the change in direction from as cending to descending or vice-versa is called a reversal. A 4-dB step size was used for the first four reversals, and then a 2-dB step size was used for t he last six reversals. Thus, in both conditions, a 70.7% correct “threshold” for th e stimulus was obtained. The stimulus presentation, recording responses, and cal culation of thresholds were controlled by locally developed computer software u sing Visual Basic 6.0 (Microsoft, Corp.). See Appendix C for Visual Basi c programming. This software calculated and displayed the geometric mean of the last 8 out of 10 reversals. The geometric mean (GM) was established by calculat ing the eighth root of the product of the last eight reversals of each run. T he GM reduces the effect of very high or very low values. Three runs were averaged to establish masked threshold. All thresholds in each condition were 5-dB of each other. If not, then thresholds were measured until three runs 5-dB were obtained. The three runs did not have to be consecutive. A maximum number o f five runs were completed for each listening condition. See Appendix D for a n example score sheet. Word Recognition Score in Quiet (WRS-Q) The listeners were seated in an IAC double-walled s ound-attenuated booth. Monosyllabic words from the Northwestern Un iversity Auditory Test No. 6 (NU No. 6: Tillman & Carhart, 1966) spoken by a fe male speaker were presented in quiet. The words were routed from a C D player (Marantz, Model CDR500/U1B at USF and RCA, Model RP8065A at Bay Pin es VAHCS), through an audiometer (Grason-Stadler, Model 61) to an inse rt earphone (Tone Ear 3-A).

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44 Testing was completed in the better ear, or right e ar in cases of symmetrical hearing. The listener was instructed to repeat the stimulus word. Percentage correct scores were obtained. The words were prese nted at 60-dB HL for the YNH listeners and at 84-dB HL for the OHI listeners Temporal Masking The masker and click were combined digitally to pro vide the required temporal alignment (t interval) (TDT, Model SM5), passed through a head phone buffer (TDT, Model HB7), and presented via Sennheis er HD 265 linear circumaural headphones. The masker was presented a t 80-dB SPL. The level of the click varied adaptively to establish thresho ld. The t intervals (the interval between the offset of the masker and onset of the click in FM or the interval between offset o f the click and onset of the masker in BM) and the interstimulus intervals (the interval between noise bursts in the FB-M paradigm: ISI) for this study were chos en based upon pilot data and the work of Wilson and Carhart (1971). A simultane ous masked threshold (SM) was obtained by placing the click at the middle of a 250-ms masker, i.e., at 125ms re: onset of the masker. Thresholds at ts of 1-, 5-, 10-, 20-, 40-, 80and 160-ms were established for the FM condition and at ts of -1-, -5-, -10-, -20and -40-ms for the BM condition. Thresholds for the for ward-backward masked conditions were established at the following ts: 1. t intervals of 5-, 10-, 15-, and 20-ms (25-ms ISI) 2. t intervals of 5-, 10-, 20-, 30-, 40-, and 45-ms (5 0-ms ISI) 3. t intervals of 5-, 10-, 20-, 40-, 60-, 80-, 90and 95-ms (100-ms ISI), and

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45 4. Forward-Backward at GDT (FB @ GDT): The ISI co rresponding to the GDT for each listener with a t interval placing the click at the middle of the ISI. Figure 6 shows example schematics for the simultane ous (A), forward (B), backward (C) and forward-backward (D) masked condit ions. Each listener was given practice at the largest tem poral masking intervals used in this study: (1) t of 160-ms for FM, (2) -40-ms for BM, and (3) a 100-ms ISI with a 40-ms t for FB-M. Practice continued until 75% correct ( intervals containing the click identified in 3 out of 4 attem pts) was obtained The procedure was identical to the procedures used for establishing detection thresholds. The simultaneous, forward, backward, a nd forward-backward masked conditions included a target interval that i ncluded a click and the broadband masking noise, whereas the standard inter val contained only the masking noise.

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46 A. B C. D. Figure 6. Examples of temporal masking stimuli. (A) Simult aneous masked, (B) Forward Masked, (C) Backward Masked, and (D) Forwar d Backward masked.

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47 Gap Detection Identical to the maskers in the temporal masking task, the markers were presented via Sennheiser HD 265 linear circumaural headphones at 80-dB SPL. The marker duration in the second interval varied i ndependently and randomly between 250to 350-ms to avoid duration cues (List er & Tarver, 2004). Figure 7 shows an example of the gap detection stimuli, with broadband noise markers presented on either side of a silent gap. Figure 7. Example of gap detection stimuli. The procedures for the gap detection paradigm were similar to the temporal masking paradigms. The two intervals, whi ch are designated the target and standard intervals, were each composed of two m arkers separated by a silent gap. In the target interval, the gap durati on varied adaptively by a factor of 1.2. In the standard interval, the markers were se parated by a 1-ms gap to preclude use of transient cues created by interrupt ion of the stimulus (Lister et al., 2002). The presentation order of the interval containing the target gap varied randomly. Each listener was given practice prior t o threshold measurement using large gaps (50-ms) until a score of 75% (thre e out of four attempts) was

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48 obtained to ensure that the task was understood. T he gap size of the initial test run was 20-ms, the initial gap size was reduced on subsequent runs to vary the starting gap size and reduce run time. As describe d in the masking paradigm, the listener used the computer mouse to select the interval containing the gap by clicking on the corresponding box labeled “A” or “B ”. The adaptive procedure continued until eight reversals occurred. The stim ulus presentation, recording responses, and calculation of thresholds were contr olled by locally developed software using Visual Basic 6.0. See Appendix C fo r Visual Basic programming. This software calculated and displayed the geometri c mean of the last 6 out of 8 reversals. Three runs were averaged to determine t he gap-detection threshold. All thresholds were required to be within a factor of 2. If not, then thresholds were measured until three runs within a factor of 2 were obtained. The runs did not have to be consecutive. A maximum of five runs completed per condition was completed. Speech Understanding in Interrupted Noise The listeners were seated in an IAC double-walled s ound-attenuated booth. The stimuli were routed from a CD player (Marantz, Model CDR500/U1B at USF and RCA, Model RP8065A at Bay Pines VAHCS), through an audiometer (Grason-Stadler, Model 61) to an insert earphone (T one Ear 3-A). Testing was completed in the better ear, or right ear in cases of symmetrical hearing. The listener was instructed to repeat the stimulus word Initially, percentage correct scores were obtained for words presented in quiet ( Northwestern University Auditory Test No. 6: Tillman & Carhart, 1966) to ea ch listener. The words were

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49 presented at 60-dB HL for the YNH listeners and at 84-dB HL for the OHI listeners. The four conditions of speech in interr upted noise were then presented. For the speech in interrupted noise con dition, the noise remained at a fixed level and the level of the speech varied (a s described previously). A set of four 35 word lists (one for each of the interrup ted noise conditions) was presented in random order, and then a second set of four word lists was presented in random order. A score sheet (see Appe ndix E) was provided with the WIN to track correct and incorrect responses. The test was terminated when the listener missed all of the words at one signalto-noise ratio (SNR). Test Sessions The listening paradigms were presented in four, 2-h our sessions. The listening sessions included audiometric and immitta nce testing, threshold measurement for the broadband masker/marker and cli ck stimuli, speech recognition in quiet, temporal masking paradigms, g ap detection paradigms, and speech recognition performance in noise paradigms. The temporal masking and gap detection paradigms were preceded by the practi ce trials previously described. All testing began with audiometric and immittance testing, if no current audiogram was available. Speech recognitio n performance in quiet was then assessed. The speech-in-interrupted noise tas ks were then completed. Threshold to the masker/marker stimulus was then ob tained. The rest of the study tasks (click threshold in quiet, SM, FM, BM, FB-M, and GDT) were presented in random order. The presentation order of the interrupted noise tasks within the speech performance in noise paradigm and the order of the t

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50 conditions within each of the temporal masked parad igms was varied randomly, as well. Each session stopped after 2 hours, with a continuation of the test order in the next session.

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51 Chapter Four Results The present study measured performance of younger listeners with normal hearing (YNH) and older listeners with heari ng loss (OHI) on three temporal resolution tasks: temporal masking (forwar d, backward and forwardbackward), gap detection, and speech understanding in interrupted noise. One purpose of the present study was to determine if ol der listeners with hearing loss demonstrated higher temporal masking thresholds, lo nger GDTs, and poorer speech understanding in noise than younger listener s with normal hearing. All three measures have not been investigated together in the same study. A second primary purpose was to examine relationships between measures of temporal resolution. A repeated measures multivariate analysis of varian ce (MANOVA) was used to investigate differences between performance for YNH and OHI listeners on the temporal tasks. The first step in the MANOV A tests for overall differences between groups. The Hotelling’s Trace Multivariate test for overall difference (overall F-test) was used to determine whether diff erences between groups were significant. In addition, partial eta squared valu es (percentage of variance and associated error explained by the independent varia ble) and overall power values (the probability of correctly accepting the null hy pothesis) were reported for each MANOVA. If overall differences between groups were significant, a second

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52 MANOVA step used univariate analysis of variance (U nivariate ANOVA) to determine which temporal tasks differed between the groups. Box’s M test was used to examine the covariance between groups and L evine’s Test of Homogeneity (or Equality) was used to determine if the variance between groups was equal. No significant difference between group s in covariance or variance was desired. If the equality of covariance or vari ance assumptions were violated (showing unequal variance between groups), non-para metric statistical tests were completed. Detection Thresholds and Word Recognition Scores To ensure audibility of the temporal masking and ga p detection stimuli, detection thresholds were established for a 0.4-ms broadband click and a 250ms broadband noise (BBN). Word recognition scores were also established in quiet (WRS-Q) at 60-dB HL for the YNH listeners and 84-dB HL for the OHI listeners. Word recognition testing in quiet was n ot performed for one of the older listeners due to patient fatigue. Average cl ick detection thresholds, BBN detection thresholds, and WRS-Q scores are listed i n Table 4. Individual thresholds are listed in Appendix A, Table A. Table 4 Mean click thresholds, broadband noise (BBN) thresh olds, and word recognition in quiet (WRS-Q) scores for both listener groups Group Click dB SPL BBN dB SPL WRS-Q % Correct YNH SD 29.0 3.6 8.2 4.3 98.5 3.0 OHI SD 48.2 12.4 38.7 5.9 90.3 7.6

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53 Due to Levine’s test of homogeneity indicating uneq ual variance between groups on all three tasks, Mann-Whitney U Rank-Sum tests were performed for each condition. The results are listed in Table 5. Table 5 Mann-Whitney U Rank-Sum test results measuring dete ction threshold and word recognition score differences between groups. Dependent Variable Z p WRS-Q -2.4 0.03 BBN Threshold -3.4 0.00 Click Threshold -3.2 0.00 The YNH listeners had significantly better word re cognition ability, lower broadband noise thresholds, and lower click thresho lds than the OHI listeners. A difference between groups on the detection and word recognition tasks was expected due to hearing sensitivity differences bet ween groups. Temporal Masking Simultaneous Masking. Simultaneous masked (SM) thresholds were establish ed to a 0.4-ms click inserted in the middle of a 250-m s broadband noise in order to measure thresholds in a standard masking condition with no temporal aspect and to provide a reference for calculation of masking r elease. A 2I/2AFC procedure was used; targeting 70.7% correct (Levitt, 1971). Mean SM thresholds and standard deviations are listed in Table 6. Individ ual SM thresholds are listed in Appendix A, Table B. An independent samples t-test revealed that the YNH listeners had significantly lower SM thresholds than the OHI list eners [t(15) = 16.717, p =

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54 .000]. Levine’s Test for Equality of Variances was not significant, suggesting equal variance between groups. Table 6 Simultaneous masked (SM) mean thresholds (in dB SPL ) and standard deviations for both listener groups. YNH OHI SM Threshold (dB SPL) 56.7 (4.44) 85.8 (2.60) Threshold shifts, or the difference between click threshold in quiet and SM click thresholds, were examined for both groups. T he YNH listeners had a mean 27.75-dB SPL (SD = 2.81) threshold shift, whereas t he OHI listeners had a mean 37.47-dB SPL (SD = 11.62) threshold shift. The dif ference in amount of threshold shift between groups was not expected. A s noted in the Method section, the masking noise had a high cutoff freque ncy of 5500 Hz, whereas the click was not filtered. However, Figure 4 does sho w some filtering effect for the click stimulus in the high frequencies due to the r esponse of the headphones. It is possible that the YNH listeners were able to tak e advantage of high frequency cues in the click stimulus that some OHI listeners were not able to use due to hearing loss, allowing the YNH to detect the click, in the presence of masking noise, at lower levels than expected. Pilot testin g with two listeners with normal hearing revealed reduced threshold shifts (41-dB SP L average unfiltered click threshold shift compared to a 57-dB SPL average fil tered click threshold shift) when the click was filtered with a high cutoff freq uency of 5500 Hz, supporting

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55 the possibility that the younger listeners used hig h frequency information in the click stimulus to establish lower-than-expected thr esholds for the SM condition. In addition to the SM condition, multiple measures were completed for three conditions of temporal masking: Forward Masking (FM ), Backward Masking (BM), and Forward-Backward Masking (FB-M). Forward Masking. FM thresholds were established to a click stimulus preceded by a 250-ms broadband masker. Delta t values indic ate the interval (in milliseconds) between the offset of the masker and the onset of the click. The mean thresholds and standard deviations for both gr oups for the seven FM conditions are listed in Table 7. Individual FM th resholds are listed in Appendix A, Table C. As the silent duration between the off set of the noise and the onset of the click increased (increasing t), the click threshold became lower for both groups. Table 7 Forward masking (FM) mean thresholds (in dB SPL)for both listener groups 1 t 5 t 10 t 20 t 40 t 80 t 160 t YNH SD 53.75 (4.18) 51.74 (3.73) 51.79 (3.60) 48.21 (2.68) 44.13 (2.91) 38.95 (2.38) 33.76 (1.74) OHI SD 83.01 (2.18) 81.37 (2.75) 77.86 (2.80) 70.16 (6.83) 62.73 (5.46) 62.24 (7.97) 57.01 (10.29) A Repeated Measures Multivariate Analysis of Varian ce (MANOVA), using listener group as the independent variable and t condition threshold as the dependent variable, showed the Hotelling’s Trace Mu ltivariate Test for overall differences between groups to be significant [F(1, 15) = 1187.24, p = .000; Partial Eta Squared = .973; observed power = 1.0]. Further GLM Univariate ANOVA

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56 showed the FM thresholds for the YNH listeners to b e significantly lower than the FM thresholds for the OHI listeners in all of the c onditions (see Table 8). Levine’s Test for Equality of Variances was not sig nificant, suggesting equal variance between groups for the 1 t through 10 t conditions. However, unequal variance between groups for the 20 t through 160 t conditions required non-parametric testing. Kruskal-Wallis te sts found significant differences between groups in all of the conditions tested (see Table 8). Table 8 ANOVA results for effect of group for each of the F M conditions. t Condition Statistic P value Partial Eta Squared Observed Power 1 t F = 339.383 .000 .958 1.000 5 t F = 353.765 .000 .959 1.000 10 t F = 280.871 .000 .949 1.000 20 t 2 = 12 .001 40 t 2 = 12 .001 80 t 2 = 12 .001 160 t 2 = 12 .000 Figure 8 shows FM thresholds plotted as a function of t. SM and quiet thresholds are also plotted. A third degree best f it polynomial was established for each masking function. The polynomial was used to establish line slopes for both listening groups. The slope of the FM functio n for the YNH was shallower (0.27-dB SPL/ms) than the slope for the OHI listene rs (0.95-dB SPL/ms).

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57 Figure 8. Mean forward masked (FM), quiet, and simultaneous thresholds (SM) for both listener groups. FM thresholds are repres ented by circles for the YNH listeners and squares for the OHI listeners. Quiet thresholds are represented by solid triangles (upward for YNH listeners and downw ard for OHI listeners). SM thresholds are represented by open triangles (upwar d for YNH listeners and downward for OHI listeners). Paired-samples t-tests showed significantly higher thresholds for the FM threshold established at 160 t than the quiet click threshold for the OHI listen ers [t(8) = -4.93, p = .001]. In addition, pair-samples t-tests showed significantly higher SM thresholds as compared to FM thresholds e stablished at 1 t for the OHI listeners [t(8) = 5.44, p = .001]. No significant difference was found for the YNH listeners for either comparison. A masking threshold shift was calculated for each l istener by subtracting the quiet threshold from the masked threshold at ea ch of the FM t conditions (see Table 9). A MANOVA with listener group as the independent variable and

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58 t conditions as the dependent variables showed the Hotelling’s Trace Multivariate Test for overall differences between g roups to be significant, with the YNH listeners having smaller masking threshold shif ts than the OHI listeners [F(1,15) = 28.79, p = .000, Partial Eta Squared = .957, Observed Power = 1.000]. Box’s Test of Equality of Covariance Matrices was n ot significant. Levine’s Test for Equality of Variances was not significant, sugg esting equal variance between groups. Table 9 FM threshold shift mean values (in dB) for both lis tener groups. 1 t 5 t 10 t 20 t 40 t 80 t 160 t YNH SD 24.76 (3.23) 22.75 (2.92) 22.81 (2.92) 19.22 (3.38) 15.14 (3.06) 9.97 (3.24) 4.90 (3.91) OHI SD 34.73 (11.83) 33.10 (11.58) 29.59 (10.90) 21.89 (7.09) 14.45 (9.22) 13.96 (8.11) 8.73 (4.97) A further GLM Univariate ANOVA showed the YNH liste ners to have significantly smaller masking threshold shifts than the OHI listeners at 1 t [F(1,15) = 5.300, p = .036, Partial Eta Squared = .261, Observed Power = .577] and 5 t [F(1,15) = 5.998, p = .027, Partial Eta Squared = .286, Observed Power = .629]. For the other t conditions, YNH and OHI listeners showed similar masking threshold shifts (values). Release from masking was calculated for each listen er by subtracting the FM threshold from the SM threshold at each of the F M t conditions (see Table 10). A MANOVA with listener group as the independe nt variable and t conditions as the dependent variables showed the Ho telling’s Trace Multivariate

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59 Test for overall differences between groups to be s ignificant, with the YNH listeners having less release from masking than the OHI listeners [F(1,15) = 65.56, p = .000, Partial Eta Squared = .980, Observed Power = 1.000]. Box’s Test of Equality of Covariance Matrices was not sig nificant. Levine’s Test for Equality of Variances was not significant, suggesti ng equal variance between groups. A further GLM Univariate ANOVA showed the YNH listeners to have significantly less release from masking than the OH I listeners at 20 t [F(1,15) = 5.92, p = .028, Partial Eta Squared = .283, Observed Power = .624], 40 t [F(1,15) = 5.998, p = .027, Partial Eta Squared = .613, Observed Power = .995] and 80 t [F(1,15) = 4.63, p = .048, Partial Eta Squared = .236, Observed Power = .521]. For the other t conditions, YNH and OHI listeners showed similar release from masking (values). Table 10 FM release from masking mean values (in dB) for bot h listener groups. 1 t 5 t 10 t 20 t 40 t 80 t 160 t YNH SD 2.99 (4.10) 5.00 (3.96) 4.98 (3.60) 8.53 (5.44) 12.61 (3.19) 17.79 (2.99) 22.85 (2.69) OHI SD 2.81 (1.55) 4.45 (2.91) 7.95 (2.46) 15.66 (6.50) 23.10 (5.28) 23.59 (7.06) 28.81 (9.11) In summary, the OHI had significantly higher thresh olds in all FM t conditions and a steeper overall FM function than t he YNH listeners. The OHI listeners had significantly larger masking threshol d shifts than the YNH listeners when the click and BBN were close together in time (1 t and 5 t). The OHI listeners experienced more release from masking tha n the YNH listeners when the time interval between the click and the masker was relatively large (20, 40

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60 and 80 t). In addition, the OHI listeners had higher thre sholds at 160 t compared to quiet thresholds and higher SM compared to thresholds established at 1 t. The YNH did not have significant differences be tween thresholds in either comparison. Backward Masking. The backward masking (BM) condition established thr eshold to a 0.4-ms click stimulus, which was followed by a 250-ms broadband masker. Delta t values indicate the interval (in millisecon ds) between the offset of the click and the onset of the masker. The mean thresholds a nd standard deviations for both groups are listed for the five ts in Table 11. As the silent duration between the offset of the click and the onset of the noise increased (increasing t), the click threshold diminished for both groups. Indivi dual BM thresholds are listed in Appendix A, Table D. Table 11 Backward masked (BM) mean thresholds (in dB SPL) fo r both listener groups. -1 t -5 t -10 t -20 t -40 t YNH SD 58.63 (6.22) 57.11 (3.16) 45.54 (5.48) 39.45 (4.30) 36.38 (5.17) OHI SD 83.75 (3.47) 83.50 (3.83) 80.11 (6.59) 72.45 (12.44) 66.25 (12.56) A MANOVA, using listener group as the independent v ariable and BM t conditions as the dependent variables showed the Ho telling’s Trace Multivariate Test for overall differences between groups to be s ignificant [F1, 15) = 156.97, p = .000; Partial Eta Squared = .986; Observed Power = 1.0]. Further GLM Univariate ANOVA showed significant differences bet ween groups at -1 t

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61 through -10 t. Unequal variance between groups for the -20 t and -40 t conditions required non-parametric testing. Non-pa rametric testing (KruskalWallis tests) found significant differences between groups (see Table 12). BM thresholds for the YNH listeners were significantly lower than BM thresholds for the OHI listeners for all of the conditions. Table 12 ANOVA Results for effect of group for each of the B M conditions. t Condition Statistic P value Partial Eta Squared Observed Power -1 t F = 109.27 .000 .879 1.000 -5 t F = 448.84 .000 .968 1.000 -10 t F = 136.21 .000 .901 1.00 -20 t 2 = 10.12 .001 -40 t 2 = 10.12 .001 Figure 9 shows BM thresholds plotted as a function of t. SM and quiet thresholds are also plotted. A third degree best f it polynomial was established for each masking function. The polynomial was used to establish line slopes for both listening groups. The slope of the BM functio n for the YNH was steeper (2.11-dB SPL/ms) than the slope for the OHI listene rs (0.82-dB SPL/ms). Pairedsamples t-tests showed significantly higher SM thre sholds than BM threshold established at -1 t for the OHI listeners [t(8) = 2.73, p = .026]. No significant difference was found for the YNH listeners. In add ition, paired-samples t-tests showed significantly higher BM thresholds establish ed at -40 t as compared to

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62 thresholds established in quiet for the OHI listene rs [t(8) = -4.21, p = .003] and the YNH listeners [t(7) = -3.99, p = .005]. Figure 9. Mean backward masked (BM), simultaneous masked (S M), and quiet thresholds for both listener groups. BM thresholds are represented by circles for the YNH listeners and squares for the OHI listeners Quiet thresholds are represented by solid triangles (upward for YNH list eners and downward for OHI listeners). SM thresholds are represented by open triangles (upward for YNH listeners and downward for OHI listeners). A masking threshold shift was calculated for each l istener by subtracting the quiet threshold from the masked threshold at ea ch of the BM t conditions (see Table 13). A MANOVA with listener group as th e independent variable and t conditions as the dependent variables showed the Hotelling’s Trace Multivariate Test for overall differences between g roups to be significant, with the YNH listeners having smaller masking threshold shif ts than the OHI listeners

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63 [F(1,15) = 30.757, p = .000, Partial Eta Squared = .933, Observed Power = 1.000]. Box’s Test of Equality of Covariance Matri ces was not significant. Further GLM Univariate ANOVA showed the YNH listene rs to have significantly smaller masking threshold shifts than the OHI liste ners for all of the backward masked conditions [-5 t [F(1,15) = 7.941, p = .013, Partial Eta Squared = .346, Observed Power = .750]; -10 t [F(1,15) = 12.723, p = .003, Partial Eta Squared = .459, Observed Power = .915]; -20 t [F(1,15) = 8.241, p = .012, Partial Eta Squared = .355, Observed Power = .765]; -40 t [F(1,15) = 4.720, p = .046, Partial Eta Squared = .239, Observed Power = .529]. Table 13 BM threshold shift mean values (in dB) for both lis tener groups. -1 t -5 t -10 t -20 t -40 t YNH SD 29.64 (4.16) 22.75 (3.59) 16.57 (4.17) 10.46 (2.71) 7.39 (5.25) OHI SD 35.48 (12.49) 35.22 (12.00) 31.84 (11.43) 24.18 (13.22) 17.98 (12.82) Release from masking was calculated for each listen er by subtracting the BM threshold from the SM threshold at each of the t conditions (see Table 14). A MANOVA with listener group as the independent var iable and t conditions as the dependent variables showed the Hotelling’s Trac e Multivariate Test for overall differences between groups to be significan t, with the YNH listeners having more release from masking than the OHI liste ners [F(1,15) = 20.72, p = .000, Partial Eta Squared = .902, Observed Power = 1.000]. Box’s Test of Equality of Covariance Matrices was not significant Levine’s Test for Equality of Variances was not significant, suggesting equal var iance between groups. A

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64 further GLM Univariate ANOVA showed the YNH listene rs to have significantly more release from masking than the OHI listeners at -5 t [F(1,15) = 5.43, p = .034, Partial Eta Squared = .266, Observed Power = .587] and -10 t [F(1,15) = 7.22, p = .017, Partial Eta Squared = .325, Observed Power = .710]. No significant difference between groups was found at any of the other conditions. Table 14 BM release from masking mean values (in dB) for bot h listener groups. -1 t -5 t -10 t -20 t -40 t YNH SD -1.88 (3.60) 5.00 (2.57) 11.19 (3.40) 17.29 (1.40) 20.36 (3.57) OHI SD 2.07 (2.27) 2.32 (2.17) 5.70 (4.80) 13.37 (10.92) 19.57 (11.89) In summary, the YNH had steeper backward masking fu nctions and significantly lower thresholds in all t conditions than the OHI listeners. The OHI also had significantly higher SM thresholds compare d to BM thresholds at -1 t, while both groups had significantly higher backward masked thresholds established at -40 t as compared to thresholds established in quiet. The OHI listeners had significantly larger masking threshol d shifts than the YNH listeners across most of the backward masked conditions. The YNH experienced more release from masking than the OHI listeners when th e click and the masker were close together in time (-5 and -10 t). Forward-Backward Masking. Forward-backward masking (FB-M) thresholds were established to a click stimulus preceded and f ollowed by a 250-ms broadband masker. Interstimulus intervals (ISI) ar e measured as the time between the offset of the first masker and the onse t of the second masker.

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65 FB-M thresholds were established at three ISIs: 100 -, 50-, and 25-ms. Delta t values indicate the interval (in milliseconds) betw een the offset of the first masker and the onset of the click. 100-ms ISI. Thresholds for both groups were established to a cl ick stimulus inserted in the silent interval between tw o 250-ms bands of broadband noise. The silent interval was 100-ms in duration. Delta t intervals of 5-, 1020-, 40-, 60-, 80-, 90and 95-ms were measured from the offset of the first noise burst to the onset of the click stimulus. Table 15 shows the average click threshold and standard deviations for both listener groups at each t interval. Individual thresholds for all 100-ms ISI forward-ba ckward masking (FB-M) conditions are listed in Appendix A, Table E. Table 15 100-ms ISI forward-backward masked (FB-M) mean thre sholds (in dB SPL) for both listener groups 5 t 10 t 20 t 40 t 60 t 80 t 90 t 95 t YNH SD 54.05 (5.56) 52.79 (6.30) 50.60 (4.87) 45.93 (4.36) 42.93 (5.31) 41.79 (4.98) 45.21 (4.96) 50.23 (3.36) OHI SD 84.55 (4.40) 80.26 (4.58) 72.25 (4.31) 67.25 (5.32) 64.25 (6.93) 67.53 (7.24) 73.45 (4.50) 79.67 (5.64) A Multivariate Analysis of Variance (MANOVA) showed the Hotelling’s Trace Multivariate Test for overall differences bet ween groups to be significant [F1, 15) = 431.60, p = .000; Partial Eta Squared = .998; Observed Power = 1.0]. Levine’s Test of Homogeneity was not significant, s uggesting equal variance between groups. GLM Univariate ANOVA showed click thresholds for the YNH listeners to be significantly lower than thresholds for the OHI listeners at all of the 100-ms ISI FB-M conditions (see Table 16).

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66 Table 16 ANOVA results for effect of group for each of the 1 00 ISI FB-M masking conditions t Condition F Statistic P Value Partial Eta Squared Observed Power 5 t 159.103 .000 .914 1.000 10 t 107.486 .000 .878 1.000 20 t 94.694 .000 .863 1.000 40 t 80.315 .000 .843 1.000 60 t 49.634 .000 .768 1.000 80 t 71.275 .000 .826 1.000 90 t 151.585 .000 .910 1.000 95 t 164.741 .000 .917 1.000 Average click thresholds for each group are shown f or all of the 100-ms ISI FB-M conditions in Figure 10. FM thresholds (5 t to 40 t) and BM thresholds (-40 t to -5 t) are also plotted. MANOVA showed significant differences between FBM and either FM or BM (as m easured by the forwardbackward t) at 60 t for the YNH [F(1,15) = 171.71, p = .025] and at 90 t for the OHI [F(1,17) = 200.02, p = .023, Partial Eta Squared = .282, Observed Power = .654]. SM and quiet thresholds are also plotted in Figure 10. Paired-samples t-tests showed significantly higher SM thresholds than FBM thresholds obtained at 95 t for both listener groups [YNH: t(7) = 4.22, p = .004; OHI: t(8) = 4.30, p = .003]. Paired-samples t-tests also showed signi ficantly higher thresholds at approximately the middle of th e 100-ms ISI interval (60 t) as compared to quiet thresholds for both groups [YN H: t(7) = -13.99, p = .000; OHI: t(8) = -4.80, p = .001].

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67 Figure 10 Mean 100-ms ISI Forward-Backward masked (FB-M) t hresholds for both listener groups. FB-M thresholds are represen ted by solid circles for the YNH listeners and solid squares for the OHI listene rs. Quiet thresholds are represented by solid triangles (upward for YNH list eners and downward for OHI listeners). SM thresholds are represented by open triangles (upward for YNH listeners and downward for OHI listeners). Smaller symbols (open circlesYNH listeners, open squares-OHI listeners) with dashed lines are forward masked thresholds (5 t to 40 t) and backward masked thresholds (-40 t to -5 t). A masking threshold shift was calculated for each l istener by subtracting quiet threshold from masked threshold at each of th e 100-ms ISI FB-M t conditions (see Table 17). During an initial MANOV A, Levine’s Test of Homogeneity indicated unequal variance between grou ps. A non-parametric Kruskal-Wallis test was completed. No significant difference between groups for masking threshold shift was found at any of the 100 -ms ISI t conditions.

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68 Table 17. 100-ms ISI FB-M threshold shift mean values (in dB ) for both listener groups. 5 t 10 t 20 t 40 t 60 t 80 t 90 t 95 t YNH SD 25.07 (4.85) 23.81 (4.81) 21.62 (3.72) 16.94 (2.33) 13.95 (2.82) 12.81 (3.43) 16.22 (4.26) 21.25 (3.55) OHI SD 36.27 (11.39) 31.98 (10.73) 23.98 (9.13) 18.97 (9.22) 15.98 (9.98) 19.31 (13.19) 25.17 (13.53) 31.39 (14.37) Release from masking was calculated for each listen er by subtracting the 100-ms ISI FB-M threshold from the SM threshold at each of the t conditions (see Table 18). A MANOVA with listener group as th e independent variable and t conditions as the dependent variables showed the Hotelling’s Trace Multivariate Test for overall differences between g roups to be significant, with the OHI listeners having more release from masking than the YNH listeners [F(1,15) = 32.491, p = .000, Partial Eta Squared = .970, Observed Power = 1.000]. Box’s Test of Equality of Covariance Matrices was not sig nificant. Levine’s Test for Equality of Variances was not significant, suggesti ng equal variance between groups. Table 18 100-ms ISI FB-M release from masking mean values (i n dB) for both listener groups. 5 t 10 t 20 t 40 t 60 t 80 t 90 t 95 t YNH SD 2.58 (5.54) 3.94 (6.02) 6.14 (5.10) 10.81 (3.25) 13.81 (3.16) 14.95 (3.44) 11.53 (5.25) 6.51 (4.36) OHI SD 1.27 (3.12) 5.57 (3.25) 13.57 (3.92) 18.58 (4.40) 21.57 (6.97) 18.24 (6.99) 12.38 (3.84) 6.16 (4.29) A further GLM Univariate ANOVA showed the OHI liste ners to have significantly more release from masking than the YN H listeners at 20 t [F(1,15) = 11.52, p = .004, Partial Eta Squared = .434, Observed Power = .887], 40 t

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69 [F(1,15) = 16.76, p = .001, Partial Eta Squared = .528, Observed Power = .969] and 60 t [F(1,15) = 8.35, p = .011, Partial Eta Squared = .358, Observed Power = .711]. No significant difference between groups was found at 5, 10, 80, 90 or 95 t. 50-ms ISI. Similar to the 100 ISI FB-M condition; thresholds f or both groups were established to a click stimulus inserte d in the silent interval between two 250-ms bands of broadband noise. The silent in terval was 50-ms in duration, as measured from the offset of the first noise burst to the onset of the second noise burst. Delta t intervals of 5-, 10-, 20-, 30-, 40-, and 55-ms were used. Table 19 shows the average click threshold a nd standard deviations for both listener groups at each t interval. Individual thresholds for all 50-ms IS I FB-M conditions are listed in Appendix A, Table F. Table 19 50-ms ISI FB-M mean thresholds (in dB SPL) for both listener groups. 5 t 10 t 20 t 30 t 40 t 45 t YNH SD 58.63 (4.57) 50.96 (4.88) 49.87 (5.48) 48.74 (4.10) 48.02 (3.42) 50.97 (3.78) OHI SD 84.48 (4.55) 79.15 (3.68) 71.77 (6.59) 71.51 (6.15) 75.98 (6.52) 80.50 (4.29) A Multivariate Analysis of Variance (MANOVA) showed the Hotelling’s Trace Multivariate Test for overall differences bet ween groups to be significant [F1, 15) = 1043.99, p = .000; Partial Eta Squared = .998; Observed Power = 1.0]. Levine’s Test of Homogeneity was not significant, s uggesting equal variance between groups. GLM Univariate ANOVA showed click thresholds for the YNH listeners to be significantly lower thresholds for the OHI listeners on all of the 50-

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70 ms ISI FB-M conditions (see Table 20). Average cli ck thresholds for each group are shown for all of the 50 ISI FB-M conditions in Figure 11. FM thresholds (5 t to 20 t) and BM thresholds (-20 t to -5 t) are also plotted. Table 20 ANOVA results for effect of group for each of the 5 0-ms ISI FB-M conditions. t Condition F Statistic P Value Partial Eta Squared Observed Power 5 t 204.029 .000 .932 1.000 10 t 183.050 .000 .924 1.000 20 t 79.355 .000 .841 1.000 30 t 78.218 .000 .839 1.000 40 t 117.593 .000 .887 1.000 45 t 223.275 .000 .937 1.000 MANOVA showed significantly higher FB-M thresholds as compared to backward masked thresholds (as measured by forwardbackward t) at 30 t for the YNH [F(1,15) = 21.502, p = .000, Partial Eta Squared = .606, Observed Power = .990]. Levine’s Test of Equality of Error Variances as not significant, indicating equal variance among conditions. No sig nificant differences were found for the OHI listeners. SM and quiet threshol ds are also plotted in Figure 11. Paired-samples t-tests showed significantly higher SM thresholds than thresholds obtained at 45 t for both listener groups [YNH: t(7) = 4.22, p = .004; OHI: t(8) = 4.89, p = .001]. Significantly higher SM thresholds than thresholds obtained at 5 t were also observed for the YNH listeners [t(7) = 2.47, p = .043]. Paired-samples t-tests also showed significantly hi gher thresholds at approximately the middle of the 50 ISI interval (30 t) as compared to quiet

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71 thresholds for both groups [YNH: t(7) = -32.47, p = .000; OHI: t(8) = -8.29, p = .000]. Figure 11 Mean 50-ms ISI Forward-Backward masked (FB-M) th resholds for both listener groups. FB-M thresholds are represen ted by solid circles for the YNH listeners and solid squares for the OHI listene rs. Quiet thresholds are represented by solid triangles (upward for YNH list eners and downward for OHI listeners). SM thresholds are represented by open triangles (upward for YNH listeners and downward for OHI listeners). Smaller symbols (open circlesYNH listeners, open squares-OHI listeners) with dashed lines are forward masked thresholds (5 t to 20 t) and backward masked thresholds (-20 t to -5 t). A masking threshold shift was calculated for each l istener by subtracting quiet threshold from masked threshold at each of th e 50-ms ISI FB-M t conditions (see Table 21). During an initial MANOV A, Levine’s Test of Equality of Variances indicated unequal variance between gro ups. A non-parametric

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72 Kruskal-Wallis test was completed. No significant difference between groups for masking threshold shift was found at any of the 50ms ISI t conditions. Table 21 50-ms ISI FB-M threshold shift mean values (in dB) for both listener groups. 5 t 10 t 20 t 30 t 40 t 45 t YNH SD 23.86 (3.51) 21.98 (3.37) 20.88 (2.59) 19.76 (1.72) 19.04 (3.25) 21.99 (2.42) OHI SD 30.21 (10.58) 30.88 (10.56) 23.50 (8.28) 23.23 (8.49) 27.70 (10.49) 32.23 (12.78) Release from masking was calculated for each listen er by subtracting the 50-ms ISI FB-M threshold from the SM threshold at e ach of the t conditions (see Table 22). A MANOVA with listener group as th e independent variable and t conditions as the dependent variables showed the Hotelling’s Trace Multivariate Test for overall differences between g roups to be significant, with the OHI listeners having more release from masking than the YNH listeners [F(1,15) = 16.827, p = .000, Partial Eta Squared = .910, Observed Power = 1.000]. Box’s Test of Equality of Covariance Matrices was not sig nificant. Levine’s Test for Equality of Variances was not significant, suggesti ng equal variance between groups. A further GLM Univariate ANOVA showed the OHI listeners to have significantly more release from masking than the YN H listeners at 20 t [F(1,15) = 8.98, p = .009, Partial Eta Squared = .374, Observed Power = .800 and 30 t [F(1,15) = 8.17, p = .012, Partial Eta Squared = .353, Observed Power = .762]. No significant difference between groups was found at 5, 10, 40 or 45 t.

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73 Table 22 50-ms ISI FB-M release from masking mean values (in dB) for both listener groups. 5 t 10 t 20 t 30 t 40 t 45 t YNH SD 3.89 (4.46) 5.78 (4.45) 6.87 (3.46) 7.99 (2.50) 8.72 (4.86) 5.76 (3.86) OHI SD 1.34 (3.04) 6.68 (2.35) 14.05 (5.93) 14.32 (5.78) 9.85 (4.93) 5.32 (3.26) 25-ms ISI. Similar to the previous FB masked conditions, thres holds for both groups were established to a click stimulus in serted in the silent interval between two 250-ms bands of broadband noise. The s ilent interval was 25-ms in duration, as measured from the offset of the first noise burst to the onset of the second noise burst. t intervals of 5-, 10-, 15-, and 20-ms were used. Table 23 shows the average click threshold and standard devi ations for both listener groups at each t interval. Individual thresholds for all 25-ms IS I forwardbackward masking conditions are listed in Appendix A, Table G. Table 23 25-ms ISI FB-M mean thresholds (in dB SPL) for both listener groups. 5 t 10 t 15 t 20 t YNH SD 53.15 (4.43) 52.29 (5.78) 51.11 (3.90) 53.16 (2.44) OHI SD 83.26 (2.44) 80.88 (5.42) 80.16 (5.23) 80.65 (3.81) A Multivariate Analysis of Variance (MANOVA) showed the Hotelling’s Trace Multivariate Test for overall differences bet ween groups to be significant [F(4,12) = 2399.99, p = .000; Partial Eta Squared = .999; Observed Power = 1.0]. GLM Univariate ANOVA showed click thresholds for th e YNH listeners to be

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74 significantly lower than click thresholds for the O HI listeners on all of the 25 ISI FB-M conditions (see Table 24). Levine’s Test of H omogeneity was not significant, suggesting equal error variance across groups. Table 24 ANOVA results for effect of group for each of the 2 5-ms ISI FB-M conditions. t Condition F Statistic P Value Partial Eta Squared Observed Power 5 t 311.657 .000 .954 1.000 10 t 110.788 .000 .881 1.000 15 t 164.777 .000 .917 1.000 20 t 304.496 .000 .953 1.000 Average click thresholds for each group are graphed for all of the 25 ISI FB-M conditions in Figure 12. FM thresholds (5 t to 10 t) and BM thresholds (-10 t to -5 t) are also plotted. MANOVA showed significant dif ferences between FB masked and either FM or BM thresholds (a s measured by forwardbackward t) at 15 t for the YNH [F(1,14) = 5.49, p = .034, Partial Eta Squared = .282, Observed Power = .587]. Levine’s Test of E quality of Error Variances as not significant, indicating equal variance among co nditions. No significant differences were found for the OHI listeners. Paired-samples t-tests showed significantly higher SM thresholds than thresholds obtained at 20 t for both listener groups [YNH: t(7) = 2.99, p = .023; OHI: t(8) = 8.92, p = .000]. Significantly higher SM thresholds than thresholds obtained at 5 t were also observed for both groups of listeners g roups [YNH: t(7) = 2.80, p = .027; OHI: t(8) = 5.63, p = .000]. Paired-samples t-tests also showed significantly higher thresholds at approxima tely the middle of the 25 ISI

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75 interval (15 t) as compared to quiet thresholds for both groups [YNH: t(7) = 19.61, p = .000; OHI: t(8) = -8.26, p = .000]. Figure 12. Mean 25-ms ISI Forward-Backward masked (FB-M) thr esholds for both listener groups. FB-M thresholds are represen ted by solid circles for the YNH listeners and solid squares for the OHI listene rs. Quiet thresholds are represented by solid triangles (upward for YNH list eners and downward for OHI listeners). SM thresholds are represented by open triangles (upward for YNH listeners and downward for OHI listeners). Smaller symbols (open circlesYNH listeners, open squares-OHI listeners) with dashed lines are forward masked thresholds (5 t to 10 t) and backward masked thresholds (-10 t to -5 t). A masking threshold shift was calculated for each l istener by subtracting quiet threshold from masked threshold at each of th e 25-ms ISI FB-M t Levine’s Test of Homogeneity indicated unequal variance betw een groups. A nonparametric Kruskal-Wallis test was completed. No s ignificant difference between

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76 groups for masking threshold shift was found at any of the 25-ms ISI t conditions (See Table 25). Table 25 25-ms ISI FB-M threshold shift mean values (in dB) for both listener groups. 5 t 10 t 15 t 20 t YNH SD 24.16 (2.99) 23.31 (4.00) 22.12 (3.19) 24.18 (2.74) OHI SD 34.98 (11.61) 32.61 (10.64) 31.89 (11.59) 32.37 (11.06) Release from masking was calculated for each listen er by subtracting the 25-ms ISI FB-M threshold from the SM threshold at e ach of the t conditions (see Table 26). A MANOVA with listener group as th e independent variable and t conditions as the dependent variables showed no s ignificant differences in release from masking between groups. Table 26 25-ms ISI FB-M release from masking mean values (n dB) for both listener groups. 5 t 10 t 15 t 20 t YNH SD 3.59 (3.64) 4.45 (5.15) 5.63 (4.13) 3.58 (3.50) OHI SD 2.57 (1.37) 4.94 (3.73) 5.66 (3.36) 5.18 (1.74) In summary, OHI listeners had higher masked thresho lds in all three of the FB-M conditions. SM masked thresholds were higher than FB-M thresholds at the longest t in all three conditions and at the shortest t for 25 ISI for both listener groups. SM thresholds were also higher th an the shortest t in the 50 ISI condition for the YNH listeners. Thresholds to the click established in quiet were

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77 lower than thresholds obtained at approximately the middle of the FB-M ISI for all three conditions for both groups. Interestingly, t hresholds established at approximately the middle of the FB-M ISI were highe r than the corresponding FM or BM masked threshold for the YNH listeners in the all of the FB-M conditions, but not for the OHI listeners. No significant diff erences in masking shift were found between groups in any of the FB-M conditions. The OHI were found to have significantly more release from masking than t he YNH listeners at 20, 40, and 60 t for the 100-ms ISI condition and at 20 and 30 t for the 50-ms ISI condition. No significant difference in release fr om masking between groups was found for the 25-ms ISI FB-M condition. Gap Detection Threshold The smallest detectable increment of silence inser ted between two stimuli is considered to be a gap detection thresho ld (GDT). Using a 250-ms broadband noise first marker and a randomly varied 250to 350-ms second marker, GDTs were established for both sets of list eners. Average GDTs are listed in Table 27. Individual GDTs are listed in Appendix A, Table H. An ANOVA showed the GDTs for the YNH listeners to be s ignificantly smaller than the GDTs for the OHI listeners [F(1,15) = 11.37, p = .004]. Levine’s Test of Homogeneity was not significant, suggesting equal e rror variance across groups. Table 27 Mean gap detection thresholds (GDT) in ms for both listener groups. YNH OHI GDT (ms) 3.53 6.22 SD (0.73) (2.14)

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78 Forward-Backward Threshold at GDT Thresholds were established to a click stimulus ins erted at the mid-point of a silent interval between two 200-ms broadband nois es. The length of the silent interval from the offset of the first masker to the onset of the second masker was equal to each individual’s GDT. Forward-backward a t GDT (FB at GDT) average click thresholds are listed in Table 28. Individua l thresholds are listed in Appendix A, Table I. An ANOVA showed masked thresh olds for the YNH listeners to be significantly lower than the masked thresholds for the OHI listeners [F(1,15) = 251.581, p = .000]. Levine’s Test of Homogeneity was not significant, suggesting equal error variance across groups. Paired samples ttests showed no significant differences between sim ultaneously masked thresholds and FB @ GDT for both listener groups, s uggesting that neither group experienced release from masking when the silent ga p was equal to GDT. See Figure 13. Table 28 Mean forward-backward at GDT (FB at GDT) masked thr esholds (in dB SPL). Thresholds were established with the ISI equivalent to each listener’s GDT. YNH OHI Threshold (dB SPL) 53.22 84.22 SD (3.71) (4.28)

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79 Figure 13. Forward-backward at GDT and simultaneous masked thresholds bar graph for both listener groups Forward-Backward Masked and Simultaneous Masked thresholds were significantly s maller for the YNH listeners than the OHI listeners ( p < .001). No significant difference was found betwe en conditions for either group As described above, a masking threshold shift was e stablished for each listener by subtracting quiet threshold from FB at GDT (see Table 29). A oneway ANOVA showed Levine’s Test of Homogeneity to be significant, suggesting unequal variance across groups. Non-parametric tes ting was performed. A Mann-Whitney U test showed no significant differenc e between groups. Release from masking was also calculated by subtracting FB at GDT from SM (see Table 29). A one-way ANOVA showed no significant differe nce in release from masking between groups.

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80 Table 29 FB at GDT mean masking threshold shift and release from masking values for both listener groups (in dB). Masking Threshold Shift Release from Masking YNH SD 24.24 (3.02) 3.51 (4.67) OHI SD 35.94 (12.06) 1.60 (2.74) Speech Understanding in Interrupted Noise Monosyllabic words were presented using the WIN te st paradigm (Wilson & Burks, 2005; Wilson, 2003). Continuous or interr upted speech spectrum noise was substituted for the multitalker babble usually used with the WIN. Four conditions were presented: 0, 5, 10, and 20 interru ptions per second (IPS). The signal-to-noise ratio (SNR) required for 50% correc t identification of the words was established for both groups of listeners in eac h condition. Probit analysis (Finney, 1964) was used to calcula te a probit function showing the proportion of correct responses at each SNR. The 50% probability point and the slope of the probit function (in prob it units/SNR dB change) were generated for each listener for the four interrupte d speech-in-noise conditions (see Figures 14 and 15). The 50% probability point on the probit analysis was used to estimate the SNR required for 50% correct i dentification of the monosyllabic words presented in the four interrupte d noise conditions. The figures show that the probit functions for the YNH listeners are clustered tightly, with the exception of one listener (S5). Nothing d istinguished this listener from the others in terms of audiometric test results or case history. The probit

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81 functions for the OHI listeners show a similar shap e but greater variability in range of SNR than the YNH probit functions. Values of the regression coefficients obtained duri ng the probit analysis were used to calculate the slope of the probit func tions. The 50% point was the estimated SNR required for each listener to correct ly identify 50% of the monosyllabic words. Grand average means for the es timated SNR (in dB HL) needed to achieve 50% correct word identification ( SNR-50) for the interrupted conditions are listed for both listening groups in Table 30. Because the temporal masking and gap detection tasks used a standard psy chophysical paradigm targeting 70.7% correct detection and the speech in noise task targeted a 50% SNR, as is traditional in the field, further analys es were conducted in an effort to determine the influence of differences in target pe rformance level on the speech in noise data. Using probit analysis results, grand average means for the estimated SNR (in dB HL) needed to achieve 70% correct word ident ification (SNR-70) for the interrupted conditions were calculated. Results ar e listed for both listening groups in Table 31. Individual thresholds are list ed in Appendix A, Table J. A one-way ANOVA with SNR condition as the independent variable and IPS as the dependent variables showed no significant differenc e in probit point (50% compared to 70%) for any of the IPS conditions, exc ept for the YNH listeners at 20 IPS [F(1,15) = 7.14, p = .02]. The SNR-50 was u sed in further statistical analyses, as the 50% point is commonly used with th e WIN test.

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82 Figure 14. Best fit lines from probit analysis for YNH liste ners in each of the four interrupted noise conditions. Individual data are plotted on each graph. Grand average data for each condition are plotted as a bo ld line.

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83 Figure 15. Best fit lines from probit analysis for OHI liste ners in each of the four interrupted noise conditions. Individual data are plotted on each graph. Grand average data for each condition are plotted as a bo ld line.

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84 Table 30 Average SNR needed for 50% correct speech intelligi bility in noise and slopes of probit functions for both groups. SNR for 50% Correct (SNR-50) Slope of Function 0 IPS 5 IPS 10 IPS 20 IPS 0 IPS 5 IPS 10 IPS 20 IPS YNH SD 8.3 (2.6) -33.1 (2.6) -27.6 (2.5) -27.1 (2.3) 0.36 (0.13) 0.27 (0.12) 0.38 (0.19) 0.37 (0.18) OHI SD 12.4 (4.6) 9.0 (7.1) 9.2 (6.3) 10.2 (7.4) 0.37 (0.10) 0.26 (0.06) 0.30 (0.09) 0.27 (0.08) Table 31 Average SNR needed for 70% correct speech intelligi bility in noise for both groups. SNR for 70% Correct (SNR-70) 0 IPS 5 IPS 10 IPS 20 IPS YNH SD 10.9 (3.0) -31.4 (2.8) -25.3 (2.2) -24.5 (1.6) OHI SD 14.8 (5.3) 12.5 (7.3) 12.2 (6.5) 13.6 (7.5) A MANOVA was completed with listener group as the independent variable and slope of probit function for each of t he interrupted conditions as the dependent variables. Results showed no significant difference in slope of function between groups in any of the interrupted c onditions. Another MANOVA was completed with listener group as the independen t variable and SNR-50 for each of the interrupted conditions as the dependent variables. Results showed the Hotelling’s Trace Multivariate Test for overall differences between groups to be significant [F(4,12) = 136.45, p = .000, Partial Eta Squared = .988, Observed Power = 1.000]. Box’s Test of Equality of Covarian ce Matrices was not considered to be significant. Further GLM Univaria te ANOVA showed the SNR50 to be significantly lower for the YNH listeners as compared to the OHI

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85 listeners in all four interrupted noise conditions (see Table 32). Levine’s Test of Equality of Error Variance was not considered to be significant. Table 32 ANOVA results for effect of group for each of the i nterrupted speech-in-noise conditions. Interrupted Noise Condition F Statistic P Value Partial Eta Squared Observed Power 0 IPS 4.734 .046 .240 .53 5 IPS 263.741 .000 .946 1.000 10 IPS 239.511 .000 .941 1.000 20 IPS 187.469 .000 .926 1.000 In order to investigate differences between interru pted noise conditions within each group, One-Way ANOVAs were completed fo r the YNH and separately for the OHI listeners with interrupted c ondition as the independent variable and SNR50 as the dependent variable. Re sults of the ANOVA for the YNH listeners had significant differences between i nterrupted noise conditions [F(3,28) = 466.913, p = .000]. Levine’s Statistic for Homogeneity of Va riances was not significant, suggesting equal variance betw een conditions. Post hoc analysis (Tukey HSD) showed scores for the 0 IPS co ndition to be significantly higher than the scores for the other three interrup tion conditions ( p = .000). The scores for the 5 IPS condition were significantly l ower than the scores for the 10 IPS and 20 IPS conditions ( p = .000). No significant differences were found between scores on the 10 IPS and 20 IPS conditions. Results of the ANOVA for the OHI listeners showed no significant differences between interrupted noise

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86 conditions. Figure 16 shows the grand mean estimat ed SNR required for 50% correct identification of the monosyllabic words pl otted as a function of interrupted noise condition. Escape from masking w as examined using SNR-50 for the 0 IPS condition as the reference and calcul ating the difference (SNR-50) of the other IPS conditions. YNH listeners experie nced more escape from masking in all of the conditions (41.4-dB: 5 IPS, 3 5.9-dB: 10 IPS, and 35.4-dB: 20 IPS) than the OHI listeners (3.4-dB: 5 IPS, 3.2dB: 10 IPS, and 2.2-dB: 20 IPS). Figure 16. Thresholds for both listener groups on all four I PS conditions. Circles represent estimates for YNH listeners and squares r epresent estimates for OHI listeners. In summary, the YNH listeners required a lower SNR than the OHI listeners in all of the interrupted speech conditio ns. In addition, the YNH listeners received a release from masking in the interrupted conditions whereas the OHI

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87 listeners did not. Greatest benefit from interrupt ions for the YNH listeners was observed in the condition which provided the larges t silent gaps in the noise Correlation Analysis The second goal of the study was to determine if st rong relationships exist between temporal masking, GDT, and speech understa nding in interrupted noise. In order to answer that question, correlati on analyses between measures were calculated. Correlation analyses identify rel ations among variables. A positive correlation indicates that the value of on e variable increases while the value of another variable also increases. A negati ve correlation indicates that the value of one variable increases while the other var iable decreases. The closer the coefficient is to 1 or -1, the stronger the rel ations between variables. Due to the small number of subjects in this study, Spearma n’s Rho correlations were used. Correlations between FB at GDT and temporal masked conditions, SM, and quiet threshold as well as correlations between GDT and temporal masked conditions, SM and quiet thresholds were calculated (see Tables 33 and 34.). Significant correlations were found between FG @ GD T and temporal masked conditions when the masker and the click wer e relatively close together in time for both groups of listeners (see Figures 1 7 and 18). Significant correlations were found between FB @ GDT masked thr eshold and SM threshold for the OHI listeners (see Figure 18). No signific ant correlations were found between FB @ GDT and SM threshold for the YNH liste ners. Significant correlations were found between GDT and several temporal masked conditions for the OHI listeners, specifical ly the FM and 50 ISI conditions

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88 (see Figure 19). No significant correlations were found between GDT and temporal masked conditions for the YNH listeners. Table 33 Spearman’s Rho correlation values for the YNH liste ner data. Significant correlations are bolded. Forward Masked Backward Masked 100 ISI FB Masked 50 ISI FB Masked 25 ISI FB Masked Quiet SM FB @ GDT (.667, .071) (.524, .183) 5 t (.548, .160) 10 t (.786, .021) 15 t (.667, .071 ) 20 t (.452, .260) 1 t (.690, .058) 5 t (.905, .002) 10 t (.643, .086) 20 t (.690, .058) 40 t (.214, .614) 80 t (.095, .823) 160 t (.214, .610) 1 t (.810, .015) 5 t (.381, .352) 10 t (.143, .716) 20 t (.333, .420) 40 t (.048, .911) 5 t (.786, .021) 10 t (.833, .010) 20 t (.762, .028) 40 t (.762, .028) 60 t (.595, .120) 80 t (.524, .183) 90 t 548, .160) 95 t (.738, .037) 5 t (.762, .028) 10 t (.881, .004) 20 t (.786, .021) 30 t (.810, .015) 40 t (.524, .183) 45 t (.690, .058) GDT 1 t (.333, .420) 5 t (.024, .955) 10 t (.286, .493) 20 t (.190, .651) 40 t (.357, .385) 80 t (.595, .120) 160 t (.667, .071) 1 t (.167, .693) 5 t (.429, .289) 10 t (.143, .736) 20 t (.238, .570) 40 t (.071, .867) 5 t (.238, .570) 10 t (.119, .779) 20 t (.238, .570) 40 t (.119, .779) 60 t (.167, .693) 80 t (.310, .456) 90 t (.310, .456) 95 t (.238, .570) 5 t (.071, .867) 10 t (.024, .955) 20 t (.238, .570) 30 t (.024, .955) 40 t (.119, .779) 45 t (.048, .911) 5 t (.262, .531) 10 t (.238, .570) 15 t (.119, .779) 20 t (.619, .102) (.143, .736) (.071, .867)

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89 Table 34 Spearman’s Rho correlation values for the OHI liste ner data. Forward Masked Backward Masked 100 ISI FB Masked 50 ISI FB Masked 25 ISI FB Masked Quiet SM FB @ GDT (.003, .932) (.967, .000) 1 t (.502, .168) 5 t (.600, .088 ) 10 t (.400, 286) 20 t (.117, .966) 40 t (.217, .516) 80 t (.083, .831) 160 t (.167, .688) 1 t (.517, .154) 5 t (.800, .010) 10 t (.667, .050) 20 t (.633, .067 ) 40 t (.450, .224) 5 t (.850, .004) 10 t (. 650, .058 ) 20 t (.200, .606) 40 t (.433, .244) 60 t (.267, .488) 80 t (.317, .406) 90 t (.750, .020) 95 t (.950, .000) 5 t (.900. .001) 10 t (.650, .058) 20 t (.100, .798) 30 t (.183, .637) 40 t (.650, .058) 45 t (.733, .025) 5 t (.800, .010) 10 t (.717, .030) 15 t (.817, .007) 20 t (.817, .007) GDT 1 t (.201, 604) 5 t (.550, .125) 10 t (.200, .606) 20 t (.583, .099) 40 t (.833, .005) 80 t (.667, .050) 160 t (.717, .030) 1 t (.050, .898) 5 t (.133, .732) 10 t (.200, .606) 20 t (.150, .700) 40 t (.217, .576) 5 t (.167, .668) 10 t (.050, .898) 20 t (.567,.112) 40 t (.333, .381) 60 t (.517, .154) 80 t (.333, .381) 90 t (.183, .637) 95 t (.067, .865) 5 t (.000, 1.00) 10 t (.467, .205) 20 t (.050, .898) 30 t (.700, .036) 40 t (.383, .308) 45 t (.250, .516) 5 t (.117, .765) 10 t (.183, .637) 15 t (.133, .732) 20 t (.083, .831) (.600, .088) (.117, .765)

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90 46 48 50 52 54 56 58 60 4446485052545658YNH FB @ GDT and FMFB @ GDT Threshold (dB SPL)FM 5 Delta t Threshold (dB SPL) 35404550556065 46 48 50 52 54 56 58 60YNH FB @ GDT and 100 ISI FB-M 100 ISI 5 Delta t 100 ISI 10 Delta t 100 ISI 20 Delta t 100 ISI 40 Delta t 100 ISI 95 Delta t FB-M Threshold (dB SPL)FB @ GDT Threshold (dB SPL) 404550556065 46 48 50 52 54 56 58 60YNH FB at GDT and 50 ISI FB-M 50 ISI 5 Delta t 50 ISI 10 Delta t 50 ISI 20 Delta t 50 ISI 30 Delta t FB-M Threshold (dB SPL)FB @ GDT Threshold (dB SPL) Figure 17 Scatter plots of FB @ GDT and temporally masked thresholds for the YNH listeners. Scatter plots between FB @ GDT thre sholds and FM, 100 ISI FBM, 50 ISI FB-M and 25 ISI FB-M are shown, 46 48 50 52 54 56 58 60 4045505560YNH FB at GDT and 25 ISI FB-MFB @ GDT Threshold (dB SPL)25 ISI 10 Delta t Threshold (dB SPL)

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91 707580859095 75 80 85 90OHI FB at GDT and 50 ISI FB-M 50 ISI 5 Delta t 50 ISI 45 Delta t FB-M Threshold (dB SPL)FB @ GDT Threshold (dB SPL) 828486889092 75 80 85 90OHI FB at GDT and SMSM Threshold (dB SPL)FB @ GDT Threshold (dB SPL) Figure 18. Scatter plots of FB @ GDT and temporally masked thr esholds for the OHI listeners. Scatter plots between FB @ GDT thre sholds and 50 ISI FB-M, 25 FB-M and SM thresholds are shown. 657075808590 75 80 85 90OHI FB at GDT and 25 ISI FB-M 25 ISI 5 Delta t 25 ISI 10 Delta t 25 ISI 15 Delta t 25 ISI 20 Delta t FB-M Threshold (dB SPL)FB @ GDT Theshold (dB SPL)

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92 788082848688909294 3 4 5 6 7 8 9 10 11OHI GDT and 50 ISI FB-M50 ISI 5 Delta t Threshold (dB SPL)GDT Threshold (ms) 4550556065707580 3 4 5 6 7 8 9 10 11OHI GDT and FM FM 40 Delta t FM 80 Delta t FM 160 Delta t FM Threshold (dB SPL)GDT Threshold (ms) Figure 19. Scatter plots of GDT and temporally masked threshol ds for OHI listeners. Scatter plots between GDT and 50 ISI FB -M thresholds and between GDT and FM thresholds are shown. Spearman’s Rho correlations were calculated to exa mine relationships between temporal measurements (FM, BM, FB-M, FB @ G DT, GDT, SM and quiet threshold) and speech-in-interrupted noise pe rformance. For the YNH listeners, significant negative correlations were f ound between temporally masked thresholds and 0 ips (see Table 35). Table 35. Spearman’s Rho correlation values for YNH temporal masking and SNR thresholds. Only significant correlations are show n. FM BM 100 ISI FB-M 50 ISI FB-M 25 ISI FB-M 0 ips 1 t (-.786,.021) 5 t (-.952, .000) 10 t (-.762, .028) -1 t (-.881,.004) 5 t (-.857,.007) 10 t (-.810, .015) 20 t (-.738, .037) 40 t (-.833, .010) 5 t (-.786, 021) 10 t (-.857, 007) 20 t (-.857,.007) 30 t (-.810, .015) 10 t (-857,.007)

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93 No significant correlations were found between temp orally masked thresholds and any of the other speech-in-interrupt ed noise conditions or between GDT and speech-in-interrupted noise performance. F or the OHI listeners, FM thresholds positively correlated with the 0 ips and 10 ips conditions. No other correlations were found. GDTs positively correlate d with all of the speech-ininterrupted noise conditions for the OHI listeners (see Table 36). Figure 20 shows scatter plots of GDT and speech-in-interrupted nois e conditions for the OHI listeners. Table 36 Spearman’s Rho correlation values for OHI temporal masking/GDT and SNR thresholds. Only significant correlations are show n. FM GDT 0 ips 5 t (.857, .007) (.833, .005) 5 ips (.833, .005) 10 ips 5 t (.783, .013) (.833, .005) 20 ips (.783. 013)

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94 -5 0 5 10 15 20 34567891011GDT and Speech-in-Interrupted Noise 0 ips 5 ips 10 ips 20 ipsSNR (dB)GDT Figure 20. Scatter plot of OHI gap detection thresholds and s peech-ininterrupted noise conditions. GDT is plotted on th e x-axis and SNR required for 50% correct identification of words is plotted on t he y-axis. Symbols represent the four speech-in-interrupted noise conditions. Multivariate Regression Analysis To further address the final research question, mul tivariate regression analyses were used to examine predictive value of t he psychoacoustic temporal measures for the speech in noise performance within each group. Multiple regression results and conclusions should be consid ered to be exploratory due to the small sample size was used for the present stud y (8 YNH and 9 OHI listeners). Stepwise regression analysis enters va riables into a model in an effort to predict values of the dependent variable (SNR-50 ) based on values of the independent variables or predictor variables (quiet SM, temporal masking thresholds and GDT). The resulting R value indicates the correlation coefficient for the predictor variables and the outcome. The R2 value indicates the amount of variability accounted by the predictor variables in the outcome. An F statistic is generated from an ANOVA to test how well the mod el predicts the outcome.

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95 Standardized Beta statistics indicate the amount of contribution each predictor variable makes to the model. Young Listeners with Normal Hearing (YNH). Multiple regression analysis was completed for the YNH data using each speech-in-noise condition as the dependent variable and quiet threshold, SM, all temporal masking conditions, and GDT as the depende nt variables. A stepwise method was used. Results showed models that signif icantly predicted performance for the 0, 5, and 10 IPS conditions (se e Table 37). No effective models were found for the 20 IPS condition, using b oth SNR-50 and SNR-70. A larger number of significant models were found for the 0 IPS condition than any other condition. Interestingly, models predicting performance for the 0 IPS condition predominantly used t conditions at the beginning of the silent interva l where the masker and the click were relatively clos e in time, except for the 25 ISI FB masked condition, which used a predictor towards the middle of the silent interval. Models predicting performance for the 5 and 10 IPS conditions used t conditions towards the end of the silent interval. The model for 100 ISI FB masked, predicting performance for the 0 IPS condit ion, was the only model incorporating FB @ GDT and GDT as predictor variabl es. Older Listeners with Hearing Impairment (OHI). As described above, multiple regression analyses we re completed on the OHI data. Results revealed models that significant ly predicted performance on all of the IPS conditions (see Table 38). Models f or all of the IPS conditions used FM thresholds established at 5 t as a significant predictor variable. Similar to

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96 the YNH regression results, t conditions at the beginning of the silent interva l, where the masker and the click were relatively clos e in time, were used. Significant models were also found for all IPS cond itions using GDT as the predictor variable.

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97 Table 37 Multivariate regression analysis results for YNH li stener data. O IPS 5 IPS 10 IPS 20 IPS FM Predictor: 5 t F(1,7) = 25.062, p = .002, R = 898, R2 = .807, beta = -.898 Predictors: 5 t and quiet F(2, 7) = 32.84, p = .001, R = 964, R2 = .929, beta = 5 (1.223), quiet (.477) None None None BM None None None None 100 ISI Predictors: 5 t, 10 t, FB @ GDT, 60 t, GDT F(5,7) = 7362.554, p = .000, R = 1.0, R2 = 1.0, beta = 5 t (-2.081), 10 t (2.096), FB @ GDT (-8.53), 60 t (-.109), GDT (0.095) Predictor : 90 t F(1,7) = 13.995, p = .010, R = 837, R2 = .700, beta = .837 None None 50 ISI Predictor: 5 t F(1,7) = 9.442, p = .022, R = 782, R2 = .611, beta = -.782 Predictor: 40 t F(1,7) = 19.205, p = .005, R = 873, R2 = .762, beta = .873 Predictors: 40 t, 10 t F(2,7) = 26.200, p = .002, R = 955, R2 = .913, beta = 40 t (1.349), 10 t (-.885) None 25 ISI Predictor: 15 t F(1,7) = 7.979, p = .030, R = 755, R2 = .571, beta = -.755 None None None FB@ GDT None None None None GDT None None None None

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98 Table 38 Multivariate regression analysis results for OHI li stener data. 0 IPS 5 IPS 10 IPS 20 IPS FM Predictor: 5 t F(1,8) = 125.194, p = .003, R = 856, R2 = .734, beta = .856 Predictor: 5 t F(1,8) = 10.305, p = .015, R = 772, R2 = .538, beta = .772 Predictor: 5 t F(1,8) = 18.992, p = .006, R = 885, R2 = .731, beta = .885 Predictor: 5 t F(1,8) = 193.266, p = .001, R = 941, R2 = .848, beta = .941 BM None None None None 100 ISI None None None None 50 ISI None None None None 25 ISI None None None None FB@ GDT None None None None GDT F(1,8) = 15.341, p = .006, R = 829, R2 = .687, beta = .829 F(1,8) = 21.360, p = .005, R = 834, R2 = .653, beta = .834 F(1,8) = 10.279, p = .015, R = 771, R2 = .595, beta = .771 F(1,8) = 14.015, p = .007, R = .817, R2 = .619, beta = -.817

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99 Chapter Five Discussion This study was designed to investigate the combine d effect of aging and hearing loss on three measures of temporal resoluti on: temporal masking, gap detection, and speech understanding in interrupted noise. All three measures use a silent gap as the cue of interest and all thr ee are commonly used to quantify temporal resolution, but the measures diff er in terms of stimuli and listener task. Temporal masking and speech in inte rrupted noise tasks require a listener to resolve a stimulus present in a silent gap but differ in target stimuli (i.e., non-speech vs. speech). Gap detection tasks require a listener to resolve the timing of the gap itself. It is thought that l isteners need to effectively glean speech information in moments of improved signal-to -noise ratio (gaps) as well as effectively resolve the gaps themselves to under stand speech in a background of everyday sounds. However, both kinds of temporal resolution abilities are rarely studied in the same listeners, so the relative importance of each is unknown. To address this issue, several sp ecific questions were posed and the results of this study are discussed in rela tion to the questions. To facilitate discussion of the research questions and the results of the present study in relation to previous research, Table 39 su mmarizes previous findings and compares and contrasts the present results. Table 39

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100 Comparison of study results and comparable literatu re. Task Results Reference Present Study Temporal Masking OHI have poorer FM, BM and FB-M thresholds than YNH Oxenham & Moore, 1995 In agreement FM Decreasing FM threshold with increasing t; FM Slope decays doubling of t for YNH Steeper FM slopes for YHI vs. YHI Wilson & Carhart, 1971 Kidd, et al., 1984 In agreement Differences may be due to populations and stimuli BM BM slope is bimodal from -20-ms to the onset of the masker and very gradual from -20-ms to -250-ms for YNH ONH BM function slopes were found to be 0.9-dB/ms. Gehr & Sommers, 1999 Wilson & Carhart, 1971 Gehr & Sommers, 1999 In agreement In agreement FB-M Increased thresholds at the extremes of the FB-M functions with lower thresholds towards the center of the ISI. Excessive masking for non-overlapping maskers for YNH No excessive masking for OHI Gaskell & Henning 1999; Penner, 1980; Wilson & Carhart, 1971 Cokely & Humes, 1993; Oxenham & Moore, 1995; Penner, 1980; Wilson & Carhart, 1971 Oxenham & Moore, 1995 In agreement In agreement In agreement GDT Larger GDTs for OHI listeners Fitzgibbons & GordonSalant, 1987, Tyler et al., 1982 In agreement Speech in noise YNH-best performance, followed by ONH, worst performance by OHI YNH show more release from masking Stuart & Phillips, 1996 Wilson, et al., 2010 In agreement Differences observed for YNH listeners Discussion of Findings in Relationship to Research Questions Will OHI Listeners Exhibit Greater Temporal Masking Larger Gap Detection Thresholds, and Poorer Speech Understanding in Nois e than YNH Listeners? Previous research indicates that OHI listeners wil l exhibit greater temporal masking (Oxenham & Moore, 1995), larger gap detecti on thresholds (Fitzgibbons

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101 & Gordon-Salant, 1987; Tyler, et al., 1982), and po orer speech understanding in noise than YNH listeners (Wilson et al., 2010). Wh ile it would be logical to predict that these findings would be supported by t he present study, no study to date had investigated differences between YNH and O HI listeners on all three temporal measures (temporal masking, gap detection and speech-in-interrupted noise) with the same listeners. Thus, the first re search question involved comparison of performance of the two groups on the measures utilized in the present investigation. The results for each measur e of temporal resolution are discussed. Will OHI Listeners Have Higher Forward, Backward an d Forward-Backward Masked Thresholds than the YNH Listeners? As was expected based on the results of Dubno et al 2003 and Oxenham and Moore (1995), the FM, BM and FB-M thresholds we re found to be higher for the OHI listeners than the YNH listeners. A contri buting factor may be hearing sensitivity differences between listener groups. T he OHI listeners had higher pure tone thresholds, which would logically result in higher thresholds to the click stimulus. Slope of masking function has been sugge sted as another way to identify differences between listener groups (Gehr & Sommers, 1995). Therefore, masking function slopes were also assess ed in the present study. Masking slope is calculated by plotting the masking data, fitting a line to the function, and calculating the slope of the fitt ed line. Masking function slope reflects the amount of recovery from masking experi enced by a listener as delta t increases (or as the distance between the masker an d the signal increase).

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102 Masked threshold would be expected to decrease as t he delta t increases (Gehr & Sommers, 1999; Wilson & Carhart, 1971). Differen ces between groups for masking function slope may identify differences not evident by examining absolute threshold data. Masking function slopes h ave been examined for FM in young listeners with normal hearing (YNH) and young listeners with hearing impairment (YHI) listeners (Kidd et al., 1984; Wils on & Carhart, 1971) and for BM in YNH and older listeners with normal hearing (ONH ) listeners (Gehr & Sommers, 1999). The present study examined FM and BM function slopes for both YNH and OHI listeners, a comparison which has not been previously examined. A comparison between the results of this study and previous studies (examining masking function slopes in other listene r populations) follows. In the FM condition, the YNH listeners were found t o have slope values similar to those found in the literature (Wilson & Carhart, 1971). The OHI listeners in the present study were found to have s teeper masking slopes as compared the YNH listeners. Kidd, Mason, and Feth (1984) found steeper slopes for YHI listeners as compared to YNH. Stimu lus, procedure, and group differences did exist between Kidd et al. and the p resent study, however. Kidd et al. examined FM thresholds in YNH listeners and YHI listeners, while participants in the present study were YNH and OHI listeners. K idd et al. used 3000 Hz tonal signals and maskers. The masker level was varied t o establish threshold, while the signal level remained stable. The present stud y used a click signal and a broadband noise masker. The masker level remained stable, while the click level varied to establish masked threshold. However, bot h studies show listeners with

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103 hearing loss to have steeper FM slopes than listene rs with normal hearing. Kidd et al (1984) suggest that differences in the proces sing of sequential sounds may account for differences between groups. In the BM condition, function slopes for the YNH li steners were found to be in agreement with the literature (Wilson & Carha rt). Gehr and Sommers (1999) established BM thresholds in YNH and ONH lis teners using a10-ms, 500 Hz signal and a 50-ms white noise masker. BM slope differences between groups were found, with steeper function slopes for the YNH listeners. In addition to the stimuli described above, BM functio ns in YNH listeners were measured with a 5 ms signal. BM thresholds in this paradigm were expected to be poorer than for the 10-ms signal for the YNH lis teners, enabling Gehr and Sommers to examine BM functions between two groups with elevated masked thresholds. The BM functions for the YNH using the 10-ms signal and the YNH listeners using the 5-ms signal were similar, sugge sting that age, not hearing loss, affected temporal processing in a BM task. B M function slopes were similar between the present study (using OHI listeners) and the study by Gehr and Sommers (using ONH listeners), supporting their con clusions that age, not hearing loss, may influence BM functions. Will the OHI Listeners Have More Threshold Shift in the Temporal Masked Conditions? Another method used to assess differences between g roups on temporal measures is to calculate the amount of threshold sh ift. Threshold shift is calculated as the difference between masked thresho ld and quiet threshold, or

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104 the increase in signal level required to establish threshold in the presence of a masking noise. Based upon previous research by Dub no et al., 2003, no difference between listener groups was expected for the FM condition. However, OHI listeners in the present study appeared to expe rience greater overall masking effect, with larger mean threshold shifts t han the YNH listeners when the masker and target were close together (1 and 5 t) for FM and across all of the BM conditions. Dubno et al. (2003) established forward masked thre sholds in YNH and OHI listeners using 20-ms tonal signals (500 and 40 00 Hz) and a 200-ms speech-shaped noise masker. No difference was foun d between groups when FM threshold shifts were calculated. Dubno et al. did not measure FM thresholds at ts smaller than 10-ms. Significant differences in mean FM threshold shifts (~10.16-dB) between groups were found in the presen t study at ts smaller than 10-ms, specifically 1and 5-ms. Considered togethe r, results of both Dubno et al. and the present study suggest OHI listeners have mo re difficulty recovering from forward masking at short time intervals. No studie s could be found that compared threshold shifts between YNH and OHI liste ners for BM or FB-M tasks. Will Only the YNH Listeners Have Excess Masking in the Forward-Backward Masked Condition? “Excess masking”, “additional masking”, or “masking additivity” are terms that have been used to describe the excess masking that occurs with nonoverlapping temporal maskers (i.e., FB-M) relative to the masking that occurs in a FM or BM paradigm alone (Cokely and Humes, 1993; Oxenham & Moore,

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105 1995; Penner, 1980; Wilson and Carhart, 1971). Thr esholds for a signal presented between two maskers (non-overlapping mask ers) have been found to be higher than thresholds established for a signal following (FM) or preceding (BM) a masker. As predicted, the present study found the YNH liste ners to show excessive masking while the OHI listeners did not, consistent with results reported by Oxenham and Moore (1995). These invest igators used a 2-ms 4000 Hz target signal and 200-ms broadband maskers to as sess excess masking effects in young listeners with normal hearing and older listeners with hearing loss. Forward (5, 10 and 25 t), backward (1 and 5 t), and forward-backward (combinations of the FM and BM conditions) threshol ds were established. The masker level was varied while the stimulus level re mained stable. Excess masking was found in all six FB masked conditions f or the YNH listeners (~9-dB), but limited excess masking was found for the OHI li steners for any condition (~1-dB). Differences in method (varying masker lev el: Oxenham and Moore; varying stimulus level: present study) may explain why Oxenham and Moore (1995) found excessive masking effects at short del ta ts, while the present study did not. The present study is the first to assess excessive masking in YNH and OHI listeners across the entire interval between no n-overlapping maskers. The present study showed more excessive masking at the middle of the silent gap for all of the FB-M ISI durations for the YNH listeners The OHI listeners did not show excessive masking in any of the ISI FB-M condi tions. Excessive masking at the middle of the gap may cause the YNH listener s to use cues at the

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106 beginning or end of the gap, while reduced excessiv e masking in the middle of the gap (and increased FM and BM effects at the beg inning and end of the gap) may cause OHI listeners to use cues at the middle o f the gap. Although not specifically addressed in the present study, there remains a question as to why younger listeners but not older listeners would show excess masking. As discussed in the literature review, th e auditory system responds in a compressive, non-linear way to intensities and fr equencies. The non-linearity of the auditory system has been suggested as a cont ributor to excess masking (Cokely and Humes, 1993; Oxenham and Moore, 1994; P enner, 1980). Although many questions still remain, factors in both the co chlea as well as the central auditory system have been suggested as contributors to reductions in excessive masking observed in older listeners (Gifford et al. 2007; Oxenham & Moore, 1995). Will the OHI Listeners Have Larger GDTs than the Y NH Listeners? As expected based on previous studies by Fitzgibbon s and Gordon-Salant (1987) and Tyler et al. (1982), results of the pres ent study showed GDTs to be significantly smaller for YNH listeners (3.53-ms) a s compared to OHI listeners (6.33-ms). YNH listeners are able to detect a smal ler silent gap inserted between two stimuli than OHI listeners. Will the YNH Listeners Have More Release from Maski ng in the Speech in Interrupted Noise Condition? YNH listeners had better SNR-50 scores across all o f the IPS conditions as compared to the OHI listeners. YNH listeners ex perienced a dramatic escape

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107 from masking when interruptions were inserted in sp eech spectrum noise relative to non-interrupted noise. Wilson et al., (2010) fo und similar results for YNH and OHI listeners, using similar stimuli. YNH listener s in their study showed 28.7 to 34.5-dB escape from masking across the interrupted conditions, whereas the OHI listeners only experienced a 2.4to 3.1-dB mas king escape. In the present study, YNH listeners experienced 35.4-dB to 41.4-dB improvement in SNR-50 across IPS conditions relative to the 0 IPS conditi on. OHI listeners had a very modest escape from masking, with only a 2to 3-dB improvement across all of the IPS conditions. Approximately 7 to 8-dB less e scape from masking for the YNH listeners was obtained by Wilson et al. as comp ared to this study, while escape from masking for the OHI listeners was very similar between studies. Differences may be due to the number of subjects us ed in each study. Wilson et al. used 24 YNH listeners, while 8 YNH listeners we re used in the present study. Another reason for YNH release of masking differenc es between Wilson et al. and the present study may be due to characteristics of the listeners. Wilson et al. used nave listeners, with no prior experience list ening in a temporal task. The present study used listeners who may have had prior temporal listening experience. Studies have suggested that training e ffects can influence performance on temporal tasks (Wright, Buonomano, M ahncke, & Merzenich, 1997; Wright & Sabin, 2007). Previous experience w ith temporal tasks for the listeners in this study may have influenced speech understanding in noise thresholds, resulting in smaller release from maski ng values.

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108 Will Performance on Tasks of Temporal Masking, Gap Detection, and Speech Understanding in Noise Be Highly Related? A second purpose of this study was to investigate t he relationship between performance on tasks of temporal masking, g ap detection, and speech in interrupted noise. The literature regarding cor relations between temporal masking and gap detection is sparse. The present s tudy is the first to examine correlations between temporal masking (FM, BM and F B-M) and GDT in both YNH and OHI listeners. Interestingly, no significa nt correlations were found between temporal masking performance and GDT perfor mance for the YNH listeners. However for the OHI listeners, temporal masking performance positively correlated with GDT performance. As an individual listener’s GDT increased, so did temporally masked threshold. The difference in correlations suggests that the YNH listeners may be using differ ent mechanisms to process information in the gap (temporal masking) versus re solving the gap itself (gap detection), while the OHI listeners may be using th e same mechanisms to process information within and across the gap. A f actor that may have contributed to a lack of correlations between the t emporal measures for the YNH was small variation between thresholds (average YNH temporal masking SDs were ~2-4 dB and GDT SD was 0.73 ms). When measure s result in data with a small variance, correlations are difficult to ident ify. Smiarowski (1970) used 500-ms broadband noise burst s to establish FM thresholds to a click stimulus and GDTs in 6 YNH li steners. FM thresholds to a click were established at ts of 0.1-, 1-, 2.5-, 5-, 10-, 20-, 40-, 80and 16 0-ms.

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109 GDT were established, using an atypical method, at fixed gap sizes of 5-, 10-, 20-, 40and 80-ms. The first marker level remained fixed at 60or 80-dB SPL and the gap size remained fixed, while the second m arker was varied to determine the level at which the gap was barely det ectable. Linear functions for both tasks were found (decreasing threshold with in creasing gap size or t). Smiarowski concluded that FM and GDT may use the sa me mechanisms for resolving the gap/click stimulus. Differing from S miarowksi’s study, the present study established GDT in the traditional manner, by varying the gap size and using stable marker levels. In addition, Smiarowsk i did not calculate correlations between temporal measures. Differences in method m ay explain why similarities between FM and GDT were noted for YNH listeners by Smiarowski and not noted in this study. Although GDT did not correlate with temporally mask ed conditions for the YNH listeners in this study, thresholds established at a silent interval equivalent to GDT in a FB masked paradigm correlated with all other temporally masked thresholds for both listener groups (FM, BM, and FB -M; see Tables 33 and 34). Correlations were found primarily when the temporal masker and click were close together in time, regardless of condition. In all conditions, listeners were detecting a signal in the gap rather than the silen t interval comprising the gap. Listeners may be using the same strategies to detec t the click in all of the conditions, especially at the shorter masker-click time intervals. Since correlations between temporally masked thresholds a nd FB at GDT occurred for both listener groups, but temporally masked thresho lds and GDT only correlated

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110 for the OHI listeners, it is possible that YNH and OHI listeners used different mechanisms to detect silent gaps as compared to sti muli inserted in the silent gap. OHI listeners may be attempting to use inform ation within the gap and, in addition, cues about the silent gap itself to compe nsate for lost cues due to decreased temporal resolution abilities (increased masked thresholds, reduced recovery from forward masking, increased GDTs, and reduced release from masking). YNH listeners may not have to compensate for lost temporal cues and may use information within the gap to process tempo ral information. In a recent study, Humes, Kewley-Port, Fogerty, & Kinney (in press), investigated FM, BM, GDT and temporal order identif ication in young (18-35 years), middle (40-55 years) and older (60-89 years ) adults. The temporal masking tasks used monosyllabic words (/p/-/vowel//t/) as the targets and either noise created from the vowel stimuli or speech-shap ed noise as the maskers. Two within-channel GDT tasks established threshold to a silent interval inserted in a 400-ms band of noise centered at 1000 Hz and a nother 400-ms noise band centered at 3500 Hz. Temporal order tasks used sti muli similar to the temporal masking tasks. The older listeners showed poorer p erformance than the young listeners on all of the tasks except for GDT at 100 0 Hz, were GDTs were not significantly different (the authors did note that results approached significance at p = .07). A principal components analysis showed in dividual performance on GDT tasks to be independent from all of the other t emporal measures used. The authors suggested that differences in stimuli (nois e markers for GDT and speech for temporal masking) may account for GDT independe nce. The prominent

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111 predictors of performance on temporal tasks were ag e and cognitive function, together only explaining 10-27% of the variance. T he small amount of variance explained in Humes et al. suggests that additional factors may influence temporal processing. The results of the present study are i n agreement with Humes et al., showing no relationship between temporally masked t hresholds (FM and BM) and GDT in YNH listeners. The present study extend ed the results of Humes et al., suggesting that FB @ GDT thresholds and GDT co rrelate for YNH listeners. In addition, the present study established relation ships between temporally masked thresholds and GDT for OHI listeners. The correlations seen in the present between tempo rally masked thresholds and GDTs for the OHI listeners and not f or the YNH listeners are interesting and suggest that the two groups of list eners may be using different temporal processing skills to resolve listening in the gap as compared to detecting a silent gap. Further studies investigat ing relationships and differences between measures of temporal masking and gap detect ion are needed. Although correlations between temporal measures are interesting and may provide information regarding underlying mechan isms, information regarding the relations between performance on psychophysical temporal measures (temporal masking and GDT) and performance on a spe ech-in-noise task may provide insight regarding the difficulties OHI list eners report understanding speech in background sounds. Thus, correlations be tween temporal measurements and speech understanding in interrupte d noise for both listener groups were calculated.

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112 Temporally masked thresholds (FM, BM and FB-M) nega tively correlated with speech-in-noise performance (0 IPS) for the YN H listeners; indicating that as SNR-50 (SNR required for 50% correct identification of the words) decreased, temporal masked thresholds increased. Dubno et al. (2003) found negative correlations between speech identification scores ( percent correct) in interrupted noise and FM thresholds, suggesting that as benefit from interrupted noise decreased, FM thresholds increased. Based on the r esults of Dubno et al., SNR would be expected to decrease as FM thresholds decr eased. The results of the present study are unexpected and may be due to form er experience the YNH listeners had with temporal listening tasks. For t he OHI listeners, FM thresholds correlated positively with SNR-50 for the 0 and 10 IPS conditions. As the SNR required for speech understanding decreased, tempor ally masked thresholds decreased. Interestingly, GDT did not correlate wi th any of the speech-ininterrupted noise tasks for the YNH listeners, but GDT positively correlated with all of the speech understanding in noise tasks for the OHI listeners. OHI listeners who required larger SNRs to understand sp eech-in-noise also had larger gap detection thresholds, supporting the not ion that the ability to resolve a silent gap contributes to speech understanding in i nterrupted noise for OHI listeners. Will Performance on Temporal Masking and GDT Tasks Predict Performance on Speech Understanding in Interrupted Noise Tasks? A second part of the second primary question addres sed the prediction of performance on speech understanding in interrupted noise using temporal

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113 measures. Multiple regression analysis was complet ed using quiet, SM, FM, BM, FB-M, FB @ GDT and GDTs as predictor variables for performance in the four speech-in-interrupted noise conditions (0, 5, 10, a nd 20 IPS). Due to the small sample size used in the present study, results of m ultiple regression analyses should be considered exploratory only. For both listening groups, models predicting perfo rmance on a speech task with no interruptions in the background noise (0 IPS) mainly used predictor variables comprised of temporally masked thresholds established at t towards the beginning of the silent interval. For the 5 an d 10 IPS speech-in-noise conditions, models for the YNH listeners used maske d thresholds towards the end of the silent interval as effective predictor v ariables, whereas models for the OHI used predictor variables similar to those effec tive for the 0 IPS condition (FM thresholds at short click-masker intervals). For t he OHI listeners only, GDT was a predictor variable for all of the speech-in-noise conditions. Although exploratory, these results do suggest that YNH listeners use different strategies for processing speech informat ion in noise, depending on whether glimpses of the speech signal were availabl e (5 and 10 IPS) or not (0 IPS). The OHI did not seem to make the same strate gy transition based upon available glimpses. The OHI listeners not only rel ied on processing information in the glimpses (temporal masking) but also process ing the gap itself (GDT). YNH listeners effectively used information availabl e within a glimpse or gap when attempting to understand speech-in-noise. The redundancy of cues available for the YNH listeners enables fast and ac curate processing of speech

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114 cues available in the glimpses. The cues available for OHI listeners may be degraded due to decreased temporal resolution. Dec reased recovery from masking, changes in auditory non-linearity, and red uced gap detection abilities may all contribute to a smearing of information acr oss the glimpse. The OHI listeners are then required to search for degraded information, primarily at the center of the gap where the critical speech glimpse may or may not be present, as well as process information regarding the gap it self. Summary Poorer performance by OHI listeners relative to YNH listeners were observed for all temporal measures used in the pres ent study: temporal masking, gap detection, and speech in interrupted noise. Th e OHI listeners were found to have higher temporal masking thresholds, larger gap detection thresholds, and poorer speech understanding in interrupted noise. The present study is the first to compare FM and BM masking slopes in OHI listeners. Masking function slopes may provide information not seen in threshold comparisons alone. Results of the presen t study support the hypothesis that age, not hearing loss, may have an influence on BM function slopes (Gehr & Sommers, 1999). The present study extended results of Dubno et al ( 2003), finding ~10dB more threshold shift for the OHI listeners at short delta t intervals. The results of the present study suggest that OHI listeners have m ore difficulty than YNH recovering from FM at short time intervals. When a ttempting to resolve temporal cues in speech or glimpses of a speech signal in ba ckground noise, slow

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115 recovery from masking may cause distortion of the i ntended signal or difficulty resolving information in brief gaps in background n oise. The present study was the first to examine excessiv e masking in OHI listeners across the entire gap between two non-sim ultaneous maskers. Results suggested that differences between YNH and OHI list eners occur at the middle of the gap, with YNH listeners showing more excessi ve masking than OHI listeners. The amount of excessive masking may inf luence location of available cues across a gap. The present study was the first to examine relation s between temporal masking and GDTs in OHI listeners. Correlation and multiple regression analyses suggest that YNH listeners and OHI listene rs may use different strategies when attempting to understand speech in interrupted noise. The YNH listeners are able to use various strategies when p rocessing speech-in-noise, due to redundant cues available. Specifically, YNH listeners appear to use information regarding the glimpse of the signal its elf, or information within the gap, to process speech-in-noise. The OHI listeners due to the increased amount of forward masking at the beginning of the g ap (due to reduced recovery from masking, distortion of the timing of the gap ( longer GDTs), and reduced excessive masking at the center of the gap, may rel y only on cues available at the center of the glimpse was well as information a bout the gap itself.

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116 Limitations of Study Although this study successfully addressed the spe cified research questions and replicated previous results in the li terature, the following limitations are noted: 1. Filtering differences between the click and mask er may have contributed to differences found between simultaneous and quiet thresholds. The masker was filtered, while the broadband click was not filtered. No other study has used filtered maskers and an unfiltered c lick. The stimuli and maskers in the present study were used in an attemp t to replicate stimuli used by Wilson and Carhart (1971). However, filter ed effects of the headphones used by Wilson and Carhart were not take n into account when generating the stimuli for the present study. Although the frequency response of the headphones may have produced a filt ering effect, high frequency cues present in the click may have given the YNH listeners an advantage during SM tasks, resulting in lower (more sensitive) thresholds than expected. SM thresholds were used in the calc ulation of release from masking. Some release from masking values for the YNH listeners may be smaller than expected as a result. SM Thres holds established with a filtered click in two listeners with normal hearing showed higher SM thresholds than thresholds established with an unfi ltered click. Future studies should use either unfiltered click and mask ing stimuli or filtered click and masking stimuli.

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117 2. All of the YNH listeners were recruited from the Department of Communication Sciences and Disorders at the Univers ity of South Florida. Some of the YNH listeners may have been experienced listeners, and may have participated in other research studies inv estigating temporal resolution. The OHI listeners were recruited from Bay Pines, VAMC and the USF community. The OHI listeners had no previo us experience with temporal resolution studies. Experience with the t asks may have reduced variability in the responses of the YNH listeners. 3. The literature discusses the possibility of ageand hearing loss-related effects on temporal resolution. This study used OH I listeners and YNH listeners as participants. Younger listeners with real or simulated hearing loss or older listeners with normal hearing would n eed to be studied in order to tease out age and hearing loss effects. 4. Temporal masking and gap detection thresholds we re established using 70.7% correct criteria, while speech in interrupted noise tasks established the SNR required for 50% correct word identificatio n. The present study used threshold criteria commonly used for each resp ective task in the literature. Significant differences were found bet ween SNR-50 and SNR70 thresholds at the 20 IPS speech in interrupted n oise condition for the YNH listeners. In addition, no significant tempora l predictor variables were found for the 20 IPS condition for the same gr oup. Further investigation is needed, using the same threshold c riteria for all tasks.

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118 5. A small sample size was used for both listener groups. Small sample size can increase variance and decrease precision o f measurement. Results of the multiple regression analysis should be considered exploratory due to the small sample size used in th e present study. Repeated measures were used when establishing thres holds (8-10 reversals, averaged 6-8 reversals for each run, and average of 3 runs for threshold) in an effort to reduce variance and incr ease precision of measurement. Implications of Study The ability to resolve fine temporal changes in speech and in noise is important for listening in everyday environments fi lled with complex background sounds. The results of this study suggest that OHI listeners have poorer temporal resolution abilities, compared to YNH list eners, as measured by temporal masking, GDT, and speech understanding in interrupted noise. The YNH listeners appear to process glimpses of a speec h signal using a variety of available redundant cues. The OHI listeners are re quired to sift through degraded cues, primarily at the center of a glimpse to piece together speech information. Sometimes, the pieces may be inaccura te, leading to misinterpretation of the speech signal. The three measures used in the present study provi de insight regarding different aspects of temporal processing. For a te mporal masking task, listeners use the onset of the click as a cue. So, temporal masking thresholds provide information about how listeners perceive onset cues in a non-speech context.

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119 For gap detection tasks, listeners use marker onset and offset cues. GDTs provide information regarding discontinuity of a si gnal, also in a non-speech context. For both temporal masking and gap detecti on, a listener must often accurately perceive a single acoustic cue. In cont rast, for speech-in-interrupted noise tasks, multiple onset and discontinuity cues are available, both in the speech and in the noise. Results of the present study suggest that performa nce on tasks measuring temporal masking thresholds at the beginning of a g limpse (5 t) and GDTs appear to be effective tools in assessing temporal resolution abilities of OHI listeners. Measurement of performance on these tas ks may help identify areas in need of rehabilitation in an effort to improve s peech-in-noise intelligibility in the OHI population. Future Research Although this study was successful in answering th e research questions, there are areas in need of further attention and re search. The separate contribution of age and hearing loss to decreased temporal resolution is one such area. Due to the population s used in this study, age and hearing loss related deficits could not be separate d. Future studies with similar study paradigms using younger listeners with real o r simulated hearing loss and older listeners with normal hearing would help dete rmine if age or hearing loss, or a combination of the two, contribute to temporal deficits found in the aging population. A future study of the temporal resolut ion abilities of middle-aged listeners may also be of interest, if aging alone i s found to be a significant factor.

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120 Additional masking was present for the YNH listene rs during the masked tasks, but not for the OHI listeners. Some authors have suggested that cochlear non-linearity may contribute to excess masking effe cts, while other authors have suggested that central auditory processes play a ma jor role. Future studies could incorporate additional ts at the middle of the 50 and 25 ISI FB masked conditions (where additional masking was noted) and measures of cochlear nonlinearity. Further research is needed to determine whether cochlear nonlinearity, central auditory processes, or a combina tion of the two contribute to excessive masking. Correlation results suggested that YNH listeners a nd OHI listeners may be using different processes when listening to the timing of a silent gap as compared to listening to a signal in the silent gap When listening to speech in a background of noise, listeners need to resolve timi ng cues available in the speech as well as the gaps created by the inherent fluctuations in the background noise. Not only do the gaps need to be resolved, but the glimpses of speech available in the gap need to be resolved as well. Further research is needed to determine if older listeners rely on diff erent temporal cues than younger listeners when processing glimpses of speec h.

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121 References Allen, P. D., Virag, T. M., & Ison, J. R. (2002). Humans detect gaps in broadband noise according to effective gap duratio n without additional cues from abrupt envelope changes. Journal of the Acoustical Society of America, 112 (6), 2967-2974. Bacon, S. P., Opie, J. M., & Montoya, D. Y. (1998). The effects of hearing loss and noise masking on the masking release for speech in temporally complex backgrounds. Journal of Speech, Language and Hearing Research 41 549.563. Cokely, C. G. & Humes, L.E. (1993). Two experiments on the temporal boundaries for the nonlinear additivity of masking. Journal of the Acoustical Society of America, 94 (5), 2553-2559. Cooke, M. (2006). A glimpsing model of speech perce ption in noise. Journal of the Acoustical Society of America, 119 (3), 1562-1573. DeFilippo, C. L., & Snell, K. B. (1986). Detection of a temporal gap in lowFrequency narrow-band signals by normal-hearing and hearing-impaired listeners. Journal of the Acoustical Society of America, 80 (5), 1354-1358. Dorman, M. T., Marton, K., Hannley, M. T., & Lindho lm, J. M. (1985). Phonetic identification by elderly and hearing-impaired list eners. Journal of the Acoustical Society of America, 77 (2), 664-670.

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134 APPENDICES

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135 Appendix A Individual Thresholds for Study Conditions A: Individual Detection and Discrimination Threshol ds Subject Number WRS (% Correct) BBN Threshold (dB SPL) Click Threshold (dB SPL) YNH 1 100 6.77 30.20 YNH 2 100 9.49 30.02 YNH 3 100 17.80 33.71 YNH 4 96 6.13 24.63 YNH 5 100 4.65 29.86 YNH 6 100 9.42 32.53 YNH 7 100 5.74 27.21 YNH 8 92 5.64 23.71 OHI 1 92 24.98 31.36 OHI 2 DNT 44.92 54.10 OHI 3 84 23.32 44.89 OHI 4 100 28.34 35.98 OHI 5 84 54.89 62.45 OHI 6 80 69.02 50.10 OHI 7 92 36.17 42.65 OHI 8 100 23.23 42.80 ONI 9 88 43.20 70.15

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136 B: Individual SM Thresholds C: Individual Forward Masked Thresholds Subject Threshold for Each Condition (dB SPL) 1 t 5 t 10 t 20 t 40 t 80 t 160 t YNH 1 53.24 51.90667 48.85667 50.17333 45.22333 36.4 30.1 YNH 2 58.78667 57.29333 56.92667 53.42333 41.33333 38.04667 33.55667 YNH 3 53.55333 52.51333 54.25667 49.43 47.16 42.72 36.82 YNH 4 51.9 47.75 50.03333 47.60333 39.62667 37.51 31.31667 YNH 5 58.77667 55.73667 55.36667 46.30333 47.85667 42.47667 42.12667 YNH 6 56.56333 51.89667 53.02667 47.43333 45.29667 38.70667 34.53667 YNH 7 50.36667 50.81667 49.05333 45.45333 41.85 37.12 30.91 YNH 8 46.78667 45.98667 46.80333 45.83333 44.67 38.63 31.73 OHI 1 84.49333 81.89667 77.75333 62.64 53.69 54.15 47.64333 OHI 2 82.46 81.00667 75.76333 68.85667 60.23333 56.36 56.1 OHI 3 82.05667 85.32333 77.85333 69.08333 70.05 68.22667 56.53333 OHI 4 80.57333 76.89333 73.74667 68.95 60.17 60.03667 50.27667 OHI 5 80.60667 82.66333 79.73667 78.21 68.35667 68.87667 65.96333 OHI 6 85.24 83.19667 78.99333 73.80667 64.37667 65.73333 54.73 OHI 7 82.05667 81.31667 80.27 63.22 60.04667 55.15 55.35 OHI 8 82.51 77.22667 74.42333 64.25667 58.99 54.95333 46.7 OHI 9 87.11667 82.82333 82.24 82.45333 68.62667 76.63667 79.79667 Subject Number Simultaneous Masked Threshold (dB SPL) YNH 1 53.686667 YNH 2 54.626667 YNH 3 62.316667 YNH 4 51.88 YNH 5 62.29 YNH 6 60.4 YNH 7 56.99 YNH 8 51.716667 OHI 1 86.206667 OHI 2 84.2 OHI 3 87.33 OHI 4 82.86 OHI 5 82.923333 OHI 6 85.53 OHI 7 85.926667 OHI 8 85.963333 OHI 9 91.476667

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137 D: Individual Backward Masked Thresholds Subject Threshold for Each Condition (dB SPL) -1 t -5 t -10 t -20 t -40 t YNH 1 57.18667 47.46667 38.47 38.16333 34.29667 YNH 2 61.02 53.16333 44.99333 37.57667 27.16667 YNH 3 68.01333 53.23 50.06 42.22 41.66333 YNH 4 50.33667 50.05333 41.08667 34.62667 31.39 YNH 5 64.7 58.41 46.36667 44.75667 41.54667 YNH 6 56.75333 54.05667 55.19333 42.81667 38.66333 YNH 7 60.29667 52.30667 47.37667 41.28667 40.00333 YNH 8 50.70667 45.21333 40.77667 34.14667 36.30333 OHI 1 81.76667 82.46667 74.74667 54.30333 42.8 OHI 2 82.19667 81.23667 77.99 59.93333 54.37333 OHI 3 88.78333 85.21 87.31667 88.72 78.65 OHI 4 83.96333 77.87 69.68333 61.76667 62.12667 OHI 5 78.25667 79.01 75.95667 69.72333 72.17667 OHI 6 82.21667 84.27667 76.77667 70.18 61.6 OHI 7 82.24 83.88 85.18333 77.52667 66.01 OHI 8 85.37 88.55667 84.18333 81.71 80.73 OHI 9 88.97667 88.97333 89.19 88.21667 77.82333 E: Individual 100 ISI Forward-Backward Masked Thres holds Subject Threshold For Each Condition (dB SPL) 5 t 10 t 20 t 40 t 60 t 80 t 90 t 95 t YNH 1 52.72 51.98 49.62 47.02 41.55 39.10 41.99 47.19 YNH 2 63.11 63.14 57.94 48.53 44.96 44.77 51.95 55.38 YNH 3 54.55 56.63 56.26 50.63 50.04 50.27 52.48 52.85 YNH 4 52.56 51.53 49.38 36.93 32.85 33.70 40.60 49.37 YNH 5 60.27 55.65 52.73 48.96 45.38 45.30 46.08 50.17 YNH 6 52.76 52.57 49.71 47.55 47.55 41.56 44.46 49.47 YNH 7 51.55 49.71 44.35 44.84 41.06 39.43 38.81 52.60 YNH 8 44.90 41.12 44.82 42.95 40.06 40.21 45.28 44.80 OHI 1 84.52 79.86 66.90 61.32 49.39 60.37 70.71 84.65 OHI 2 82.34 79.39 73.04 62.19 60.31 59.43 68.46 77.84 OHI 3 87.33 81.14 74.07 72.22 68.44 83.37 82.00 88.52 OHI 4 74.65 71.84 67.24 63.47 67.29 67.87 69.46 70.69 OHI 5 83.58 79.01 74.34 71.67 68.37 71.01 69.34 72.18 OHI 6 86.92 83.14 77.78 67.46 66.91 62.11 73.53 78.79 OHI 7 83.91 76.02 68.27 62.47 67.82 66.84 77.83 81.83 OHI 8 88.56 84.87 70.35 68.02 58.40 66.79 75.42 80.38 OHI 9 89.14 87.06 78.27 76.41 71.36 70.46 74.28 82.11

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138 F: Individual 50 ISI Forward-Backward Masked Thresh olds Subject Threshold For Each Condition (dB SPL) 5 t 10 t 20 t 30 t 40 t 45 t YNH 1 50.62 49.86 48.30 47.36 45.68 48.86 YNH 2 60.23 58.09 54.13 51.65 54.48 56.62 YNH 3 55.94 54.58 53.24 54.46 51.86 54.62 YNH 4 50.77 48.76 47.84 44.62 45.37 48.88 YNH 5 55.62 53.99 53.62 51.64 45.75 51.67 YNH 6 52.33 51.27 50.22 50.57 48.84 53.27 YNH 7 52.47 49.45 46.41 47.60 46.15 48.91 YNH 8 44.80 41.70 45.20 42.04 46.02 44.96 OHI 1 82.95 78.59 65.02 60.93 73.59 84.36 OHI 2 81.57 77.61 69.43 69.43 72.90 76.97 OHI 3 93.19 83.42 64.18 76.92 83.48 85.97 OHI 4 78.33 73.29 69.37 70.61 64.46 77.03 OHI 5 82.41 79.31 77.32 76.50 76.71 78.16 OHI 6 82.98 80.91 78.32 74.32 74.73 80.45 OHI 7 82.99 78.17 70.70 68.34 81.30 82.62 OHI 8 85.99 75.78 69.58 65.92 71.45 73.71 OHI 9 89.95 85.30 82.07 80.59 85.20 85.24 G: Individual 25 ISI Forward-Backward Masked Thresh olds Subject Threshold For Each Condition (dB SPL) 5 t 10 t 15 t 20 t YNH 1 50.29 52.61 49.28 51.30 YNH 2 58.20 59.76 56.79 55.91 YNH 3 55.38 55.72 54.56 54.23 YNH 4 51.16 50.87 50.47 52.04 YNH 5 55.82 55.97 53.91 56.15 YNH 6 56.67 53.30 50.31 54.98 YNH 7 53.17 49.77 49.29 50.12 YNH 8 44.48 40.33 44.29 50.55 OHI 1 81.78 78.48 81.48 78.37 OHI 2 82.25 80.82 77.90 78.23 OHI 3 87.36 85.43 84.77 84.51 OHI 4 80.22 69.66 69.58 76.29 OHI 5 81.33 78.18 76.64 77.20 OHI 6 82.90 82.90 83.15 82.27 OHI 7 83.20 79.50 82.38 79.47 OHI 8 83.21 84.65 78.37 81.50 OHI 9 87.06 88.32 87.21 88.02

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139 H: Individual Gap Detection Thresholds Subject GDT (ms) YNH 1 3.12 YNH 2 3.26 YNH 3 3.17 YNH 4 3.37 YNH 5 5.11 YNH 6 3.93 YNH 7 2.83 YNH 8 3.47 OHI 1 4.41 OHI 2 8.32 OHI 3 10.19 OHI 4 4.81 OHI 5 7.11 OHI 6 6.66 OHI 7 4.65 OHI 8 3.39 OHI 9 6.49 I: Individual Forward-Backward Masked Threshold at GDT ISI Subject Threshold (dB SPL) YNH 1 53.76 YNH 2 59.36 YNH 3 56.78 YNH 4 51.23 YNH 5 53.47 YNH 6 51.49 YNH 7 52.66 YNH 8 47.05 OHI 1 86.72 OHI 2 80.73 OHI 3 88.99 OHI 4 75.29 OHI 5 82.95 OHI 6 84.23 OHI 7 84.46 OHI 8 85.74 OHI 9 88.87

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140 J: Individual SNR values needed for 50% correct Spe ech in Noise Intelligibility and Probit analysis slopes SNR for 50% Correct Identification Slope of Function 0 IPS 5 IPS 10 IPS 20 IPS 0 IPS 5 IPS 10 IPS 20 IPS YNH 1 8.81 -38.44 -29.78 -28.47 .39 .26 .33 .37 YNH 2 4.79 -31.65 -25.61 -24.81 .36 .38 .58 .62 YNH 3 8.80 -30.41 -24.03 -24.81 .63 .45 .74 .62 YNH 4 10.48 -32.81 -29.37 -28.24 .31 .10 .33 .27 YNH 5 3.97 -36.22 -31.59 -29.98 .29 .12 .12 .08 YNH 6 10.05 -32.99 -27.26 -29.11 .37 .30 .36 .27 YNH 7 8.82 -35.17 -28.47 -27.22 .36 .29 .37 .42 YNH 8 10.98 -34.61 -25.80 -24.02 .18 .24 .23 .33 OHI 1 11.21 3.76 6.26 7.91 .32 .27 .25 .24 OHI 2 15.33 16.80 14.46 16.73 .29 .24 .33 .25 OHI 3 18.33 12.40 14.76 15.64 .29 .15 .21 .16 OHI 4 5.19 -1.07 0.86 2.31 .48 .30 .31 .39 OHI 5 14.84 15.28 13.27 16.15 .36 .33 .29 .30 OHI 6 17.99 15.95 16.47 17.99 .21 .23 .22 .21 OHI 7 8.79 7.49 5.87 6.77 .47 .24 .27 .34 OHI 8 7.59 -1.30 -0.54 -3.42 .46 .22 .30 .18 OHI 9 12.00 12.01 11.60 12.01 .42 .36 .52 .38 K: Individual SNR values needed for 70% correct Spe ech in Noise Intelligibility SNR Needed for 70% Correct Identification 0 IPS 5 IPS 10 IPS 20 IPS YNH 1 10.99 -35.19 -27.22 -26.205 YNH 2 7.131 -29.405 -24.145 -23.453 YNH 3 10.149 -28.516 -22.873 -23.453 YNH 4 13.251 -28.308 -26.765 -25.096 YNH 5 6.809 -34.98 -28.551 -25.002 YNH 6 12.568 -30.189 -24.869 -25.942 YNH 7 11.167 -33.225 -26.205 -25.205 YNH 8 15.672 -31.111 -22.132 -21.484 OHI 1 13.868 6.904 9.619 11.486 OHI 2 18.229 20.308 17.068 20.07 OHI 3 21.224 18.045 18.793 20.812 OHI 4 6.955 1.782 3.592 4.469 OHI 5 17.176 17.837 16.219 18.948 OHI 6 22.093 19.553 20.296 22.093 OHI 7 10.602 11.069 9.028 9.246 OHI 8 9.432 2.486 2.239 1.243 OHI 9 14.037 14.388 13.228 14.217

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141 Appendix B RPvds Programming for Temporal Masking and GDT Stim uli

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142

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143

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144 Appendix C Screen Shot of 2I/2AFC Response Screen

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145 Appendix D Visual Basic (v 6.0) Programming for Temporal Maski ng and GDT Tasks Forward, Backward, Forward-Backward, Simultaneous, and Quiet Thresholds Form Details Dim secs As Single Dim Start As Single Dim interval As Integer Sub CmdRun_Click() Dim e1 As Long 'generic variable for error checks Dim data(0 To 4999) As Single buffer for saving d ata Srate = 20 Call GetRunInfo Call InitAdaptive Call PA5x1.ConnectPA5("USB", 1) 'If PA5x1.ConnectPA5("USB", 1) Then 'MsgBox ("Connection established") 'Else 'MsgBox "Unable to connect" 'End If Dim ErrMess As String ErrMess = PA5x1.GetError If Len(ErrMess) > 0 Then MsgBox ErrMess End If Call PA5x1.SetAtten(StartAttn) 'error1 = PA5x1.GetError 'If error1 = "" Then 'MsgBox "Attenuation set correctly" 'Else 'MsgBox error1 'End If Call RPcoX1.ConnectRP2("USB", 1) Call RPcoX1.LoadCOF(RP2Filename) Call RPcoX1.Run If RPcoX1.GetStatus <> 7 Then MsgBox ("RP not running correctly") End If

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146 If Task = "Quiet" Then GoTo Spot: e1 = RPcoX1.SetTagVal("M1Duration", Masker1Dur) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("M2Duration", Masker2Dur) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("M2Delay", Masker2Delay) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("Masker1Amp", Masker1Amplitud e) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("Masker2Amp", Masker2Amplitud e) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("HighCut1", HighCutoff1) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("HighCut2", HighCutoff2) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("LowCut1", LowCutoff1) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("LowCut2", LowCutoff2) If e1 = 0 Then MsgBox ("error reading parameter") End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("M3Duration", Masker3Dur)

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147 If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("M4Duration", Masker4Dur) If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("M3Delay", Masker3Delay) If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("M4Delay", Masker4Delay) If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Backward" Or Task = "BackwardPractice" T hen e1 = RPcoX1.SetTagVal("M1Delay", Masker1Delay) If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("Masker3Amp", Masker3Amplitud e) If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("Masker4Amp", Masker4Amplitud e) If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("HighCut3", HighCutoff3) If e1 = 0 Then

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148 MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("HighCut4", HighCutoff4) If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("LowCut3", LowCutoff3) If e1 = 0 Then MsgBox ("error reading parameter") End If End If If Task = "Forward-Backward" Or Task = "ForwardBack wardPractice" Then e1 = RPcoX1.SetTagVal("LowCut4", LowCutoff4) If e1 = 0 Then MsgBox ("error reading parameter") End If End If e1 = RPcoX1.SetTagVal("TotalDuration", TotalDur) If e1 = 0 Then MsgBox ("error reading parameter") End If Spot: e1 = RPcoX1.SetTagVal("SignalDuration", SigDur) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("SignalAmp", SignalAmplitude) If e1 = 0 Then MsgBox ("error reading parameter") End If 'e1 = RPcoX1.SetTagVal("HighCutSig", HighCutoffSig) 'If e1 = 0 Then 'MsgBox ("error reading parameter") 'End If 'e1 = RPcoX1.SetTagVal("LowCutSig", LowCutoffSig) 'If e1 = 0 Then 'MsgBox ("error reading parameter") 'End If

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149 FRMINTERVAL.Show If Task = "ForwardPractice" Then Call ForwardPracti ce If Task = "BackwardPractice" Then Call BackwardPrac tice If Task = "ForwardBackwardPractice" Then Call Forwa rdBackwardPractice Do mclick = 0 I = GetRandom(2) Signal = I FRMINTERVAL!Text1.Text = Str$(I) + Str$(VariableA ttn) 'set attenuation = VariableAttn If I = 2 Then FRMINTERVAL!INTERVAL1.Visible = True SigDelay2 = SigDelay + Interval2Add e1 = RPcoX1.SetTagVal("SignalDelay", SigDelay 2) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call RPcoX1.SoftTrg(2) If Task = "Quiet" Then Call delay(0.75) If Task = "Simultaneous" Then Call delay(0.75 ) If Task = "Forward" Or Task = "Backward" Then Call delay(0.8) If Task = "Forward-Backward" Then Call delay( 1) FRMINTERVAL!INTERVAL2.Visible = True 'Call RPcoX1.SoftTrg(1) End If If I = 1 Then FRMINTERVAL!INTERVAL1.Visible = True e1 = RPcoX1.SetTagVal("SignalDelay", SigDelay ) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call RPcoX1.SoftTrg(2) If Task = "Quiet" Then Call delay(0.75) If Task = "Simultaneous" Then Call delay(0.75 ) If Task = "Forward" Or Task = "Backward" Then Call delay(0.8) If Task = "Forward-Backward" Then Call delay( 1) FRMINTERVAL!INTERVAL2.Visible = True

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150 'Call RPcoX1.SoftTrg(1) End If Do Until mclick = 1 'Pause program until get a res ponse DoEvents Loop FRMINTERVAL!INTERVAL1.Visible = False FRMINTERVAL!INTERVAL2.Visible = False RightWrong = Signal Choice Call Levitt Call delay(0.4) Trial = Trial + 1 Loop While ExitFlagR = 0 Call RPcoX1.Halt If ExitFlagR = 1 Then Call Finishup End If End Sub Sub CmdQuit_Click() End End Sub Sub Form_Load() 'Putting items in combo boxes CboISI.AddItem "25" CboISI.AddItem "50" CboISI.AddItem "100" CboISI.AddItem "200" CboISI.AddItem "300" CboISI.AddItem "400" CboISI.AddItem "500" CboDeltat.AddItem "1" CboDeltat.AddItem "5" CboDeltat.AddItem "10" CboDeltat.AddItem "20" CboDeltat.AddItem "40" CboDeltat.AddItem "80" CboDeltat.AddItem "160" CboDeltat.AddItem "250" CboDeltat.AddItem "340"

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151 CboDeltat.AddItem "420" CboDeltat.AddItem "460" CboDeltat.AddItem "480" CboDeltat.AddItem "490" CboDeltat.AddItem "495" CboStartAttn.AddItem "0" CboStartAttn.AddItem "20" CboStartAttn.AddItem "30" CboStartAttn.AddItem "40" CboStartAttn.AddItem "50" CboStartAttn.AddItem "60" CboStartAttn.AddItem "70" CboStartAttn.AddItem "80" CboStartAttn.AddItem "90" CboM1Freq.AddItem "Broad Band" CboM2Freq.AddItem "Broad Band" CboM3Freq.AddItem "Broad Band" CboM4Freq.AddItem "Broad Band" CboTask.AddItem "Forward-Backward" CboTask.AddItem "Forward" CboTask.AddItem "Backward" CboTask.AddItem "Simultaneous" CboTask.AddItem "Quiet" CboTask.AddItem "ForwardPractice" CboTask.AddItem "BackwardPractice" CboTask.AddItem "ForwardBackwardPractice" CboSimCond.AddItem "Onset" CboSimCond.AddItem "Middle" CboSimCond.AddItem "Offset" End Sub Sub InitAdaptive() 'Initialize the adaptive variables Bumptop = 0 Bumpbot = 0 RightWrong = 0 Trial = 1 NumReversal = 0 Slope = 1 Decision = 1

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152 AttnSum = 0 GapThresh = 0 AttnMult = 1 Signal = 0 Choice = 0 ExitFlagB = 0 ExitFlagR = 0 ExitFlagE = 0 'LeftStandard = 1 'RightStandard = 2 'LeftSignal = 3 'RightSignal = 4 SDSum = 0 FreqSum = 0 For I = 0 To 100 Responses(I) = 0 Reversals(I) = 0 Next I For I = 0 To 1 Attn(I) = StartAttn Next I End Sub Public Sub Levitt() If RightWrong <> 0 Then incorrect answer Responses(Trial) = RightWrong If NumReversal <= 4 Then Attn(Trial + 1) = Attn(Trial) 4 VariableAttn = VariableAttn 4 End If If NumReversal > 4 Then Attn(Trial + 1) = Attn(Trial) 2 VariableAttn = VariableAttn 2 End If Call Bumpcheck Call Reversecheck Else 'correct answer Responses(Trial) = RightWrong Lastlevel = Attn(Trial) Attn(Trial 1) If Lastlevel <> 0 Then Attn(Trial + 1) = Attn(T rial) If Lastlevel = 0 Then If NumReversal <= 4 Then Attn(Trial + 1) = Attn(Trial) + 4 VariableAttn = VariableAttn + 4 End If

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153 If NumReversal > 4 Then Attn(Trial + 1) = Attn(Trial) + 2 VariableAttn = VariableAttn + 2 End If End If Call Bumpcheck Call Reversecheck End If Call PA5x1.SetAtten(VariableAttn) End Sub Public Sub Bumpcheck() If Attn(Trial + 1) < 0 Then Attn(Trial + 1) = 0 Bumptop = Bumptop + 1 If Bumptop > 4 Then MsgBox "Hitting Top", 48, "Fulton T emporal Masking Program" ExitFlagB = 1 End If End If If Attn(Trial + 1) > 119 Then Attn(Trial + 1) = 119 Bumpbot = Bumpbot + 1 If Bumpbot > 4 Then MsgBox "Hitting Bottom", 48, "Fulto n Temporal Masking Program" ExitFlagB = 1 End If End If End Sub Public Sub Reversecheck() rcheck = (Attn(Trial + 1) Attn(Trial)) Slope If rcheck < 0 Then NumReversal = NumReversal + 1 Reversals(NumReversal) = Trial Slope = -1 Slope End If If NumReversal < 10 Then ExitFlagR = 0 End If If NumReversal >= 10 Then ExitFlagR = 1 End If

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154 End Sub Public Sub Finishup() Unload FRMINTERVAL For I = 1 To 10 WhichAttn = Reversals(I) ReversalResults(I) = Attn(WhichAttn) Next I For I = 5 To 10 WhichAttn = Reversals(I) AttnSum = AttnSum + Attn(WhichAttn) Next I FinalAttn = AttnSum / 6 For I = 5 To 10 WhichAttn = Reversals(I) StdDevSum = StdDevSum + (Attn(WhichAttn) FinalAttn) (Attn(WhichAttn) FinalAttn) StdDev = Sqr(StdDevSum) / 6 Next I AttnMult = 1 For I = 3 To 10 WhichAttn = Reversals(I) AttnMult = AttnMult Attn(WhichAttn) If AttnMult = 0 Then AttnMult = 1 Next I GMean = (AttnMult ^ 0.125) Threshold = Unatten FinalAttn GeoMean = Unatten GMean 'SD = ((SDSum / 6) ^ 0.5) FrmResults.Show FrmResults!TxtResultsName.Text = SubName FrmResults!TxtResultsISI.Text = ISI FrmResults!TxtResultsStartAttn.Text = StartAttn FrmResults!TxtResultsEndAttn.Text = VariableAtt n FrmResults!TxtResultsM1Freq.Text = Masker1Freq FrmResults!TxtResultsM2Freq.Text = Masker2Freq FrmResults!TxtResultsTask.Text = Task FrmResults!TxtResultsThresh.Text = Threshold FrmResults!TxtResultsSD.Text = StdDev FrmResults!TxtResultsGeoMean.Text = GeoMean FrmResults!DateLabel.Caption = Date & " & Tim e

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155 'Format(Now, "ddddd") FrmResults!TxtReversal1.Text = ReversalResults( 1) FrmResults!TxtReversal2.Text = ReversalResults( 2) FrmResults!TxtReversal3.Text = ReversalResults( 3) FrmResults!TxtReversal4.Text = ReversalResults( 4) FrmResults!TxtReversal5.Text = ReversalResults( 5) FrmResults!TxtReversal6.Text = ReversalResults( 6) FrmResults!TxtReversal7.Text = ReversalResults( 7) FrmResults!TxtReversal8.Text = ReversalResults( 8) FrmResults!TxtReversal9.Text = ReversalResults( 9) FrmResults!TxtReversal10.Text = ReversalResults (10) End Sub Public Sub ForwardPractice() Do mclick = 0 I = GetRandom(2) Signal = I FRMINTERVAL!Text1.Text = Str$(I) + Str$(VariableA ttn) 'set attenuation = VariableAttn If I = 2 Then FRMINTERVAL!INTERVAL1.Visible = True SigDelay2 = SigDelay + Interval2Add e1 = RPcoX1.SetTagVal("SignalDelay", SigDelay 2) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call RPcoX1.SoftTrg(2) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True 'Call RPcoX1.SoftTrg(1) End If If I = 1 Then FRMINTERVAL!INTERVAL1.Visible = True e1 = RPcoX1.SetTagVal("SignalDelay", SigDelay ) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call RPcoX1.SoftTrg(2) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True

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156 'Call RPcoX1.SoftTrg(1) End If Do Until mclick = 1 'Pause program until get a res ponse DoEvents Loop FRMINTERVAL!INTERVAL1.Visible = False FRMINTERVAL!INTERVAL2.Visible = False RightWrong = Signal Choice Call delay(0.4) Trial = Trial + 1 Loop While ExitFlagR = 0 Call RPcoX1.Halt If ExitFlagR = 1 Then Call Finishup End If End Sub Public Sub BackwardPractice() Do mclick = 0 I = GetRandom(2) Signal = I FRMINTERVAL!Text1.Text = Str$(I) + Str$(VariableA ttn) 'set attenuation = VariableAttn If I = 2 Then FRMINTERVAL!INTERVAL1.Visible = True SigDelay2 = SigDelay + Interval2Add e1 = RPcoX1.SetTagVal("SignalDelay", SigDelay 2) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call RPcoX1.SoftTrg(2) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True 'Call RPcoX1.SoftTrg(1) End If

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157 If I = 1 Then FRMINTERVAL!INTERVAL1.Visible = True e1 = RPcoX1.SetTagVal("SignalDelay", SigDelay ) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call RPcoX1.SoftTrg(2) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True 'Call RPcoX1.SoftTrg(1) End If Do Until mclick = 1 'Pause program until get a res ponse DoEvents Loop FRMINTERVAL!INTERVAL1.Visible = False FRMINTERVAL!INTERVAL2.Visible = False RightWrong = Signal Choice Call delay(0.4) Trial = Trial + 1 Loop While ExitFlagR = 0 Call RPcoX1.Halt If ExitFlagR = 1 Then Call Finishup End If End Sub Public Sub ForwardBackwardPractice() Do mclick = 0 I = GetRandom(2) Signal = I FRMINTERVAL!Text1.Text = Str$(I) + Str$(VariableA ttn) 'set attenuation = VariableAttn If I = 2 Then FRMINTERVAL!INTERVAL1.Visible = True SigDelay2 = SigDelay + Interval2Add e1 = RPcoX1.SetTagVal("SignalDelay", SigDelay 2) If e1 = 0 Then MsgBox ("error reading parameter")

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158 End If Call RPcoX1.SoftTrg(1) Call RPcoX1.SoftTrg(2) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True 'Call RPcoX1.SoftTrg(1) End If If I = 1 Then FRMINTERVAL!INTERVAL1.Visible = True e1 = RPcoX1.SetTagVal("SignalDelay", SigDelay ) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call RPcoX1.SoftTrg(2) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True 'Call RPcoX1.SoftTrg(1) End If Do Until mclick = 1 'Pause program until get a res ponse DoEvents Loop FRMINTERVAL!INTERVAL1.Visible = False FRMINTERVAL!INTERVAL2.Visible = False RightWrong = Signal Choice Call delay(0.4) Trial = Trial + 1 Loop While ExitFlagR = 0 Call RPcoX1.Halt If ExitFlagR = 1 Then Call Finishup End If End Sub Public Sub GetRunInfo() SubName = TxtName.Text

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159 ISI = Val(CboISI.Text) Deltat = Val(CboDeltat.Text) StartAttn = Val(CboStartAttn.Text) Task = CboTask.Text SimCond = CboSimCond.Text SigDur = 0.4 Masker1Dur = 250 Masker2Dur = 250 Masker3Dur = 250 Masker4Dur = 250 Masker1Amplitude = 0.1 Masker2Amplitude = 0.1 Masker3Amplitude = 0.1 Masker4Amplitude = 0.1 SignalAmplitude = 2 Masker1Freq = CboM1Freq.Text Masker2Freq = CboM2Freq.Text Masker3Freq = CboM3Freq.Text Masker4Freq = CboM4Freq.Text If Masker1Freq = "Broad Band" Then HighCutoff1 = 5500 LowCutoff1 = 20 End If If Masker2Freq = "Broad Band" Then HighCutoff2 = 5500 LowCutoff2 = 20 End If If Masker3Freq = "Broad Band" Then HighCutoff3 = 5500 LowCutoff3 = 20 End If If Masker4Freq = "Broad Band" Then HighCutoff4 = 5500 LowCutoff4 = 20 End If 'HighCutoffSig = 5500 'LowCutoffSig = 20 If Task = "Forward-Backward" Then Masker2Delay = Masker1Dur + ISI Masker3Delay = Masker1Dur + ISI + Masker2Dur + 500 Masker4Delay = Masker1Dur + ISI + Masker2Dur + 500 + Masker3Dur + ISI SigDelay = Masker1Dur + Deltat TotalDur = 1500 + ISI + ISI Interval2Add = (ISI Deltat SigDur) + Masker2Dur + 500 + Masker3Dur + Deltat End If If Task = "Forward" Then

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160 Masker2Delay = Masker1Dur + ISI + 500 SigDelay = Masker1Dur + ISI TotalDur = 1000 + ISI + ISI Interval2Add = 750 + ISI 'Masker3Dur = 0 'Masker4Dur = 0 'Masker3Amplitude = 0 'Masker4Amplitude = 0 End If If Task = "Backward" Then Masker1Delay = ISI Masker2Delay = ISI + 250 + 500 + ISI SigDelay = 1 TotalDur = ISI + ISI + 1000 Interval2Add = 750 + ISI 'Masker3Dur = 0 'Masker4Dur = 0 'Masker3Amplitude = 0 'Masker4Amplitude = 0 End If If Task = "Quiet" Then SigDelay = 1 Interval2Add = 750 'Masker1Dur = 0 'Masker2Dur = 0 'Masker3Dur = 0 'Masker4Dur = 0 'Masker1Amplitude = 0 'Masker2Amplitude = 0 'Masker3Amplitude = 0 'Masker4Amplitude = 0 End If If Task = "Simultaneous" Then Masker2Delay = 750 TotalDur = 1000 If SimCond = "Onset" Then SigDelay = 1 If SimCond = "Middle" Then SigDelay = Masker1Dur / 2 If SimCond = "Offset" Then SigDelay = Masker1Dur 0.4 Interval2Add = 750 'Masker3Dur = 0 'Masker4Dur = 0 'Masker3Amplitude = 0 'Masker4Amplitude = 0 End If If Task = "ForwardPractice" Then Masker2Delay = Masker1Dur + ISI + 500

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161 SigDelay = Masker1Dur + ISI TotalDur = 1000 + ISI + ISI Interval2Add = 750 + ISI End If If Task = "BackwardPractice" Then Masker1Delay = ISI Masker2Delay = ISI + Masker1Dur + 500 + ISI SigDelay = 1 TotalDur = ISI + ISI + 1000 Interval2Add = 750 + ISI End If If Task = "ForwardBackwardPractice" Then Masker2Delay = Masker1Dur + ISI Masker3Delay = Masker1Dur + ISI + Masker2Dur + 500 Masker4Delay = Masker1Dur + ISI + Masker2Dur + 500 + Masker3Dur + ISI SigDelay = Masker1Dur + Deltat TotalDur = 1500 + ISI + ISI Interval2Add = (ISI Deltat SigDur) + Masker2Dur + 500 + Masker3Dur + Deltat End If I = 1 J = 1 ExitFlag = 0 If Task = "Forward" Then RP2Filename = "I:\psycholab\Fulton\Fulton\FultonFB_ForwardCond" If Task = "Backward" Then RP2Filename = "I:\psycholab\Fulton\Fulton\FultonFB_BackwardCond" If Task = "Forward-Backward" Then RP2Filename = "I:\psycholab\Fulton\Fulton\FultonFB_ForwardBackwar dCond_TDT" If Task = "Simultaneous" Then RP2Filename = "I:\psycholab\Fulton\Fulton\FultonFB_SimultaneousCo nd" If Task = "Quiet" Then RP2Filename = "I:\psycholab\Fulton\Fulton\FultonFB_QuietCond_Filt ered" If Task = "ForwardPractice" Then RP2Filename = "I:\psycholab\Fulton\Fulton\FultonFB_ForwardCond" If Task = "BackwardPractice" Then RP2Filename = "I:\psycholab\Fulton\Fulton\FultonFB_BackwardCond" If Task = "ForwardBackwardPractice" Then RP2Filenam e = "I:\psycholab\Fulton\Fulton\FultonFB_ForwardBackwar dCond_TDT" Unatten = 102.8 VariableAttn = StartAttn End Sub

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162 Form Interval (Similar Programming was used for Mas ker Threshold and Gap Detection) Private Sub Command1_Click() mclick = 1 ExitFlagB = 1 Unload FRMINTERVAL End Sub Private Sub HappyFace1() For I = 1 To 2 Shape1.Visible = True Call delay(0.3) Shape1.Visible = False Call delay(0.3) Next I End Sub Private Sub HappyFace2() For I = 1 To 2 Shape2.Visible = True Call delay(0.3) Shape2.Visible = False Call delay(0.3) Next I End Sub Private Sub Form_Load() End Sub Private Sub INTERVAL1_Click() Choice = 1 mclick = 1 If Signal = 1 Then Call HappyFace1 If Signal = 2 Then Call HappyFace2 'If Signal = 3 Then Call HappyFace3 INTERVAL1.Visible = False INTERVAL2.Visible = False

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163 'INTERVAL3.Visible = False End Sub Private Sub INTERVAL2_Click() Choice = 2 mclick = 1 If Signal = 1 Then Call HappyFace1 If Signal = 2 Then Call HappyFace2 'If Signal = 3 Then Call HappyFace3 INTERVAL1.Visible = False INTERVAL2.Visible = False 'INTERVAL3.Visible = False End Sub Form Results (Similar programming was used for Mask er thresholds and Gap Detection) Private Sub CmdPrint_Click() ExitFlagR = 1 FrmResults.PrintForm Printer.EndDoc Unload FrmResults End Sub Private Sub CmdEnd_Click() End End Sub Private Sub CmdRepeat_Click() Unload FrmResults End Sub Private Sub Form_Load() TxtResultsName.Text = SubName TxtResultsISI.Text = ISI TxtResultsStartAttn.Text = StartAttn TxtResultsEndAttn.Text = VariableAttn TxtResultsM1Freq.Text = Masker1Freq TxtResultsM2Freq.Text = Masker2Freq TxtResultsTask.Text = Task TxtResultsThresh.Text = Threshold TxtResultsGeoMean.Text = GeoMean

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164 TxtResultsSD.Text = StdDev TxtResultsSimCond = SimCond TxtResultsDeltat = Deltat DateLabel.Caption = Date & " & Time 'Format(Now, "ddddd") TxtReversal1.Text = ReversalResults(1) TxtReversal2.Text = ReversalResults(2) TxtReversal3.Text = ReversalResults(3) TxtReversal4.Text = ReversalResults(4) TxtReversal5.Text = ReversalResults(5) TxtReversal6.Text = ReversalResults(6) TxtReversal7.Text = ReversalResults(7) TxtReversal8.Text = ReversalResults(8) TxtReversal9.Text = ReversalResults(9) TxtReversal10.Text = ReversalResults(10) End Sub Module 1 ForwardBackward Sub Routines (Similar Prog ramming used for Masker Threshold and Gap Detection) Sub ErrorCheck() 'this is a tucker routine to d eliver an error message 'to the screen. These are for hardware errors Dim LongRet As Long Dim bytErrMsg(255) As Byte Dim strErrMsg As String LongRet = getS2err(bytErrMsg(0)) If LongRet > 0 Then strErrMsg = StrConv(bytErrMsg, vbUnicode) Call MsgBox(strErrMsg) End If End Sub Module 2 ForwardBackward Declarations (Similar Prog ramming used for Masker Threshold and Gap Detection) Public Declare Sub AD1clear Lib "s2drv32s.dll" Alia s "_AD1clear@4" (ByVal lngdin As Long) Public Declare Sub AD1go Lib "s2drv32s.dll" Alias _AD1go@4" (ByVal lngdin As Long) Public Declare Sub AD1stop Lib "s2drv32s.dll" Alias "_AD1stop@4" (ByVal lngdin As Long) Public Declare Sub AD1arm Lib "s2drv32s.dll" Alias "_AD1arm@4" (ByVal lngdin As Long) Public Declare Sub AD1mode Lib "s2drv32s.dll" Alias "_AD1mode@8" (ByVal lngdin As Long, ByVal lngmcode As Long) Public Declare Sub AD1srate Lib "s2drv32s.dll" Alia s "_AD1srate@8" (ByVal lngdin As Long, ByVal sngsrate As Single) Public Declare Function AD1speriod Lib "s2drv32s.dl l" Alias "_AD1speriod@8" (ByVal lngdin As Long, ByVal sngsper As Single) As Single

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165 Public Declare Sub AD1clkin Lib "s2drv32s.dll" Alia s "_AD1clkin@8" (ByVal lngdin As Long, ByVal lngscode As Long) Public Declare Sub AD1clkout Lib "s2drv32s.dll" Ali as "_AD1clkout@8" (ByVal lngdin As Long, ByVal lngdcode As Long) Public Declare Sub AD1npts Lib "s2drv32s.dll" Alias "_AD1npts@8" (ByVal lngdin As Long, ByVal lngnpts As Long) Public Declare Sub AD1mtrig Lib "s2drv32s.dll" Alia s "_AD1mtrig@4" (ByVal lngdin As Long) Public Declare Sub AD1strig Lib "s2drv32s.dll" Alia s "_AD1strig@4" (ByVal lngdin As Long) Public Declare Function AD1status Lib "s2drv32s.dll Alias "_AD1status@4" (ByVal lngdin As Long) As Long Public Declare Sub AD1reps Lib "s2drv32s.dll" Alias "_AD1reps@8" (ByVal lngdin As Long, ByVal lngnreps As Long) Public Declare Function AD1clip Lib "s2drv32s.dll" Alias "_AD1clip@4" (ByVal lngdin As Long) As Long Public Declare Sub AD1clipon Lib "s2drv32s.dll" Ali as "_AD1clipon@ 4" (ByVal lngdin As Long) Public Declare Sub AD1tgo Lib "s2drv32s.dll" Alias "_AD1tgo@4" (ByVal lngdin As Long) Public Declare Sub AD2clear Lib "s2drv32s.dll" Alia s "_AD2clear@4" (ByVal lngdin As Long) Public Declare Sub AD2go Lib "s2drv32s.dll" Alias _AD2go@4" (ByVal lngdin As Long) Public Declare Sub AD2stop Lib "s2drv32s.dll" Alias "_AD2stop@4" (ByVal lngdin As Long) Public Declare Sub AD2arm Lib "s2drv32s.dll" Alias "_AD2arm@4" (ByVal lngdin As Long) Public Declare Sub AD2mode Lib "s2drv32s.dll" Alias "_AD2mode@8" (ByVal lngdin As Long, ByVal lngmcode As Long) Public Declare Sub AD2srate Lib "s2drv32s.dll" Alia s "_AD2srate@8" (ByVal lngdin As Long, ByVal sngsrate As Single) Public Declare Function AD2speriod Lib "s2drv32s.dl l" Alias "_AD2speriod@8" (ByVal lngdin As Long, ByVal sngsper As Single) As Single Public Declare Sub AD2clkin Lib "s2drv32s.dll" Alia s "_AD2clkin@8" (ByVal lngdin As Long, ByVal lngscode As Long) Public Declare Sub AD2clkout Lib "s2drv32s.dll" Ali as "_AD2clkout@8" (ByVal lngdin As Long, ByVal lngdcode As Long) Public Declare Sub AD2npts Lib "s2drv32s.dll" Alias "_AD2npts@8" (ByVal lngdin As Long, ByVal lngnpts As Long) Public Declare Sub AD2mtrig Lib "s2drv32s.dll" Alia s "_AD2mtrig@4" (ByVal lngdin As Long) Public Declare Sub AD2strig Lib "s2drv32s.dll" Alia s "_AD2strig@4" (ByVal lngdin As Long) Public Declare Function AD2status Lib "s2drv32s.dll Alias "_AD2status@4" (ByVal lngdin As Long) As Long Public Declare Sub AD2reps Lib "s2drv32s.dll" Alias "_AD2reps@8" (ByVal lngdin As Long, ByVal lngnreps As Long) Public Declare Function AD2clip Lib "s2drv32s.dll" Alias "_AD2clip@4" (ByVal lngdin As Long) As Long

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166 Public Declare Sub AD2gain Lib "s2drv32s.dll" Alias "_AD2gain@12" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal lnggain As Long) Public Declare Sub AD2sh Lib "s2drv32s.dll" Alias _AD2sh@8" (ByVal lngdin As Long, ByVal lngoocode As Long) Public Declare Sub AD2sampsep Lib "s2drv32s.dll" Al ias "_AD2sampsep@8" (ByVal lngdin As Long, ByVal sngsep As Single) Public Declare Sub AD2xchans Lib "s2drv32s.dll" Ali as "_AD2xchans@8" (ByVal lngdin As Long, ByVal lngnchans As Long) Public Declare Sub AD2tgo Lib "s2drv32s.dll" Alias "_AD2tgo@4" (ByVal lngdin As Long) Public Declare Sub ADclear Lib "s2drv32s.dll" Alias "_ADclear@4" (ByVal lngdin As Long) Public Declare Sub ADgo Lib "s2drv32s.dll" Alias "_ ADgo@4" (ByVal lngdin As Long) Public Declare Sub ADtgo Lib "s2drv32s.dll" Alias _ADtgo@4" (ByVal lngdin As Long) Public Declare Sub ADstop Lib "s2drv32s.dll" Alias "_ADstop@4" (ByVal lngdin As Long) Public Declare Sub ADarm Lib "s2drv32s.dll" Alias _ADarm@4" (ByVal lngdin As Long) Public Declare Sub ADmode Lib "s2drv32s.dll" Alias "_ADmode@8" (ByVal lngdin As Long, ByVal lngmcode As Long) Public Declare Sub ADsrate Lib "s2drv32s.dll" Alias "_ADsrate@8" (ByVal lngdin As Long, ByVal sngsrate As Single) Public Declare Function ADsperiod Lib "s2drv32s.dll Alias "_ADsperiod@8" (ByVal lngdin As Long, ByVal sngsper As Single) As Single Public Declare Sub ADclkin Lib "s2drv32s.dll" Alias "_ADclkin@8" (ByVal lngdin As Long, ByVal lngscode As Long) Public Declare Sub ADclkout Lib "s2drv32s.dll" Alia s "_ADclkout@8" (ByVal lngdin As Long, ByVal lngdcode As Long) Public Declare Sub ADnpts Lib "s2drv32s.dll" Alias "_ADnpts@8" (ByVal lngdin As Long, ByVal lngnpts As Long) Public Declare Sub ADmtrig Lib "s2drv32s.dll" Alias "_ADmtrig@4" (ByVal lngdin As Long) Public Declare Sub ADstrig Lib "s2drv32s.dll" Alias "_ADstrig@4" (ByVal lngdin As Long) Public Declare Function ADstatus Lib "s2drv32s.dll" Alias "_ADstatus@4" (ByVal lngdin As Long) As Long Public Declare Sub ADreps Lib "s2drv32s.dll" Alias "_ADreps@8" (ByVal lngdin As Long, ByVal lngnreps As Long) Public Declare Function APlock Lib "s2drv32s.dll" A lias "_APlock@8" (ByVal lngmtry As Long, ByVal lngfstart As Long) As Long Public Declare Sub APunlock Lib "s2drv32s.dll" Alia s "_APunlock@4" (ByVal lngfend As Long) Public Declare Function APactive Lib "s2drv32s.dll" Alias "_APactive@0" () As Long Public Declare Function APinit Lib "s2drv32s.dll" A lias "_APinit@12" (ByVal lngdn As Long, ByVal lngimode As Long, ByVal lngapt As Long) As Long Public Declare Sub CG1go Lib "s2drv32s.dll" Alias _CG1go@4" (ByVal lngdin As Long) Public Declare Sub CG1stop Lib "s2drv32s.dll" Alias "_CG1stop@4" (ByVal lngdin As Long) Public Declare Sub CG1reps Lib "s2drv32s.dll" Alias "_CG1reps@8" (ByVal lngdin As Long, ByVal lngreps As Long)

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167 Public Declare Sub CG1trig Lib "s2drv32s.dll" Alias "_CG1trig@8" (ByVal lngdin As Long, ByVal lngttype As Long) Public Declare Sub CG1period Lib "s2drv32s.dll" Ali as "_CG1period@8" (ByVal lngdin As Long, ByVal sngperiod As Single) Public Declare Sub CG1pulse Lib "s2drv32s.dll" Alia s "_CG1pulse@12" (ByVal lngdin As Long, ByVal sngon_t As Single, ByVal sngoff_t As Single) Public Declare Function CG1active Lib "s2drv32s.dll Alias "_CG1active@4" (ByVal lngdin As Long) As Long Public Declare Sub CG1patch Lib "s2drv32s.dll" Alia s "_CG1patch@8" (ByVal lngdin As Long, ByVal lngpcode As Long) Public Declare Sub CG1tgo Lib "s2drv32s.dll" Alias "_CG1tgo@4" (ByVal lngdin As Long) Public Declare Sub DB4clear Lib "s2drv32s.dll" Alia s "_DB4clear@4" (ByVal lngdin As Long) Public Declare Function DB4setgain Lib "s2drv32s.dl l" Alias "_DB4setgain@12" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal snggai n As Single) As Single Public Declare Function DB4selgain Lib "s2drv32s.dl l" Alias "_DB4selgain@12" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal lnggs As Long) As Single Public Declare Function DB4setfilt Lib "s2drv32s.dl l" Alias "_DB4setfilt@16" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal lngfty pe As Long, ByVal sngffreq As Single) As Single Public Declare Function DB4selfilt Lib "s2drv32s.dl l" Alias "_DB4selfilt@16" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal lngfty pe As Long, ByVal lngfs As Long) As Single Public Declare Sub DB4userfilt Lib "s2drv32s.dll" A lias "_DB4userfilt@16" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal lngfn As Long ByRef sngcoef As Single) Public Declare Sub DB4setIT Lib "s2drv32s.dll" Alia s "_DB4setIT@12" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal lngit As Long) Public Declare Sub DB4nchan Lib "s2drv32s.dll" Alia s "_DB4nchan@8" (ByVal lngdin As Long, ByVal lngnc As Long) Public Declare Sub DB4setTS Lib "s2drv32s.dll" Alia s "_DB4setTS@12" (ByVal lngdin As Long, ByVal sngamp As Single, ByVal sngfreq As S ingle) Public Declare Sub DB4onTS Lib "s2drv32s.dll" Alias "_DB4onTS@4" (ByVal lngdin As Long) Public Declare Sub DB4offTS Lib "s2drv32s.dll" Alia s "_DB4offTS@4" (ByVal lngdin As Long) Public Declare Sub DB4startIM Lib "s2drv32s.dll" Al ias "_DB4startIM@8" (ByVal lngdin As Long, ByVal lngchan As Long) Public Declare Sub DB4stopIM Lib "s2drv32s.dll" Ali as "_DB4stopIM@4" (ByVal lngdin As Long) Public Declare Function DB4readIM Lib "s2drv32s.dll Alias "_DB4readIM@8" (ByVal lngdin As Long, ByVal lngpc As Long) As Long Public Declare Function DB4getclip Lib "s2drv32s.dl l" Alias "_DB4getclip@4" (ByVal lngdin As Long) As Long Public Declare Function DB4getstat Lib "s2drv32s.dl l" Alias "_DB4getstat@4" (ByVal lngdin As Long) As Long Public Declare Sub DB4powdown Lib "s2drv32s.dll" Al ias "_DB4powdown@4" (ByVal lngdin As Long) Public Declare Function DB4impscan Lib "s2drv32s.dl l" Alias "_DB4impscan@8" (ByVal lngdin As Long, ByVal lngtochan As Long) As Long

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168 Public Declare Function DB4getgain Lib "s2drv32s.dl l" Alias "_DB4getgain@12" (ByVal lngdin As Long, ByVal lngchan As Long, ByRef lngsel As Long) As Single Public Declare Function DB4getfilt Lib "s2drv32s.dl l" Alias "_DB4getfilt@16" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal lngft As Long, ByRef lngsel As Long) As Single Public Declare Function DB4getIT Lib "s2drv32s.dll" Alias "_DB4getIT@8" (ByVal lngdin As Long, ByVal lngchan As Long) As Long Public Declare Function DB4getchmode Lib "s2drv32s. dll" Alias "_DB4getchmode@4" (ByVal lngdin As Long) As Long Public Declare Function DB4getmud Lib "s2drv32s.dll Alias "_DB4getmud@4" (ByVal lngdin As Long) As Long Public Declare Function DB4getconst Lib "s2drv32s.d ll" Alias "_DB4getconst@8" (ByVal lngcc As Long, ByVal lngsel As Long) As Long Public Declare Sub DD1clear Lib "s2drv32s.dll" Alia s "_DD1clear@4" (ByVal lngdin As Long) Public Declare Sub DD1go Lib "s2drv32s.dll" Alias _DD1go@4" (ByVal lngdin As Long) Public Declare Sub DD1stop Lib "s2drv32s.dll" Alias "_DD1stop@4" (ByVal lngdin As Long) Public Declare Sub DD1arm Lib "s2drv32s.dll" Alias "_DD1arm@4" (ByVal lngdin As Long) Public Declare Sub DD1mode Lib "s2drv32s.dll" Alias "_DD1mode@8" (ByVal lngdin As Long, ByVal lngmcode As Long) Public Declare Sub DD1srate Lib "s2drv32s.dll" Alia s "_DD1srate@8" (ByVal lngdin As Long, ByVal sngsrate As Single) Public Declare Function DD1speriod Lib "s2drv32s.dl l" Alias "_DD1speriod@8" (ByVal lngdin As Long, ByVal sngsper As Single) As Single Public Declare Sub DD1clkin Lib "s2drv32s.dll" Alia s "_DD1clkin@8" (ByVal lngdin As Long, ByVal lngscode As Long) Public Declare Sub DD1clkout Lib "s2drv32s.dll" Ali as "_DD1clkout@8" (ByVal lngdin As Long, ByVal lngdcode As Long) Public Declare Sub DD1npts Lib "s2drv32s.dll" Alias "_DD1npts@8" (ByVal lngdin As Long, ByVal lngnpts As Long) Public Declare Sub DD1mtrig Lib "s2drv32s.dll" Alia s "_DD1mtrig@4" (ByVal lngdin As Long) Public Declare Sub DD1strig Lib "s2drv32s.dll" Alia s "_DD1strig@4" (ByVal lngdin As Long) Public Declare Function DD1status Lib "s2drv32s.dll Alias "_DD1status@4" (ByVal lngdin As Long) As Long Public Declare Sub DD1reps Lib "s2drv32s.dll" Alias "_DD1reps@8" (ByVal lngdin As Long, ByVal lngnreps As Long) Public Declare Function DD1clip Lib "s2drv32s.dll" Alias "_DD1clip@4" (ByVal lngdin As Long) As Long Public Declare Sub DD1clipon Lib "s2drv32s.dll" Ali as "_DD1clipon@4" (ByVal lngdin As Long) Public Declare Sub DD1echo Lib "s2drv32s.dll" Alias "_DD1echo@4" (ByVal lngdin As Long) Public Declare Sub DD1tgo Lib "s2drv32s.dll" Alias "_DD1tgo@4" (ByVal lngdin As Long) Public Declare Sub DA1clear Lib "s2drv32s.dll" Alia s "_DA1clear@4" (ByVal lngdin As Long)

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169 Public Declare Sub DA1go Lib "s2drv32s.dll" Alias _DA1go@4" (ByVal lngdin As Long) Public Declare Sub DA1stop Lib "s2drv32s.dll" Alias "_DA1stop@4" (ByVal lngdin As Long) Public Declare Sub DA1arm Lib "s2drv32s.dll" Alias "_DA1arm@4" (ByVal lngdin As Long) Public Declare Sub DA1mode Lib "s2drv32s.dll" Alias "_DA1mode@8" (ByVal lngdin As Long, ByVal lngmcode As Long) Public Declare Sub DA1srate Lib "s2drv32s.dll" Alia s "_DA1srate@8" (ByVal lngdin As Long, ByVal sngsrate As Single) Public Declare Function DA1speriod Lib "s2drv32s.dl l" Alias "_DA1speriod@8" (ByVal lngdin As Long, ByVal sngsper As Single) As Single Public Declare Sub DA1clkin Lib "s2drv32s.dll" Alia s "_DA1clkin@8" (ByVal lngdin As Long, ByVal lngscode As Long) Public Declare Sub DA1clkout Lib "s2drv32s.dll" Ali as "_DA1clkout@8" (ByVal lngdin As Long, ByVal lngdcode As Long) Public Declare Sub DA1npts Lib "s2drv32s.dll" Alias "_DA1npts@8" (ByVal lngdin As Long, ByVal lngnpts As Long) Public Declare Sub DA1mtrig Lib "s2drv32s.dll" Alia s "_DA1mtrig@4" (ByVal lngdin As Long) Public Declare Sub DA1strig Lib "s2drv32s.dll" Alia s "_DA1strig@4" (ByVal lngdin As Long) Public Declare Function DA1status Lib "s2drv32s.dll Alias "_DA1status@4" (ByVal lngdin As Long) As Long Public Declare Sub DA1reps Lib "s2drv32s.dll" Alias "_DA1reps@8" (ByVal lngdin As Long, ByVal lngnreps As Long) Public Declare Function DA1clip Lib "s2drv32s.dll" Alias "_DA1clip@4" (ByVal lngdin As Long) As Long Public Declare Sub DA1clipon Lib "s2drv32s.dll" Ali as "_DA1clipon@4" (ByVal lngdin As Long) Public Declare Sub DA1tgo Lib "s2drv32s.dll" Alias "_DA1tgo@4" (ByVal lngdin As Long) Public Declare Sub DA3clear Lib "s2drv32s.dll" Alia s "_DA3clear@4" (ByVal lngdin As Long) Public Declare Sub DA3go Lib "s2drv32s.dll" Alias _DA3go@4" (ByVal lngdin As Long) Public Declare Sub DA3stop Lib "s2drv32s.dll" Alias "_DA3stop@4" (ByVal lngdin As Long) Public Declare Sub DA3arm Lib "s2drv32s.dll" Alias "_DA3arm@4" (ByVal lngdin As Long) Public Declare Sub DA3mode Lib "s2drv32s.dll" Alias "_DA3mode@8" (ByVal lngdin As Long, ByVal lngcmask As Long) Public Declare Sub DA3srate Lib "s2drv32s.dll" Alia s "_DA3srate@8" (ByVal lngdin As Long, ByVal sngsrate As Single) Public Declare Function DA3speriod Lib "s2drv32s.dl l" Alias "_DA3speriod@8" (ByVal lngdin As Long, ByVal sngsper As Single) As Single Public Declare Sub DA3clkin Lib "s2drv32s.dll" Alia s "_DA3clkin@8" (ByVal lngdin As Long, ByVal lngscode As Long) Public Declare Sub DA3clkout Lib "s2drv32s.dll" Ali as "_DA3clkout@8" (ByVal lngdin As Long, ByVal lngdcode As Long) Public Declare Sub DA3npts Lib "s2drv32s.dll" Alias "_DA3npts@8" (ByVal lngdin As Long, ByVal lngnpts As Long)

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170 Public Declare Sub DA3mtrig Lib "s2drv32s.dll" Alia s "_DA3mtrig@4" (ByVal lngdin As Long) Public Declare Sub DA3strig Lib "s2drv32s.dll" Alia s "_DA3strig@4" (ByVal lngdin As Long) Public Declare Function DA3status Lib "s2drv32s.dll Alias "_DA3status@4" (ByVal lngdin As Long) As Long Public Declare Sub DA3reps Lib "s2drv32s.dll" Alias "_DA3reps@8" (ByVal lngdin As Long, ByVal lngnreps As Long) Public Declare Function DA3clip Lib "s2drv32s.dll" Alias "_DA3clip@4" (ByVal lngdin As Long) As Long Public Declare Sub DA3clipon Lib "s2drv32s.dll" Ali as "_DA3clipon@4" (ByVal lngdin As Long) Public Declare Sub DA3tgo Lib "s2drv32s.dll" Alias "_DA3tgo@4" (ByVal lngdin As Long) Public Declare Sub DA3setslew Lib "s2drv32s.dll" Al ias "_DA3setslew@8" (ByVal lngdin As Long, ByVal lngslcode As Long) Public Declare Sub DA3zero Lib "s2drv32s.dll" Alias "_DA3zero@4" (ByVal lngdin As Long) Public Declare Sub DAclear Lib "s2drv32s.dll" Alias "_DAclear@4" (ByVal lngdin As Long) Public Declare Sub DAgo Lib "s2drv32s.dll" Alias "_ DAgo@4" (ByVal lngdin As Long) Public Declare Sub DAtgo Lib "s2drv32s.dll" Alias _DAtgo@4" (ByVal lngdin As Long) Public Declare Sub DAstop Lib "s2drv32s.dll" Alias "_DAstop@4" (ByVal lngdin As Long) Public Declare Sub DAarm Lib "s2drv32s.dll" Alias _DAarm@ 4" (ByVal lngdin As Long) Public Declare Sub DAmode Lib "s2drv32s.dll" Alias "_DAmode@8" (ByVal lngdin As Long, ByVal lngmcode As Long) Public Declare Sub DAsrate Lib "s2drv32s.dll" Alias "_DAsrate@8" (ByVal lngdin As Long, ByVal sngsrate As Single) Public Declare Function DAsperiod Lib "s2drv32s.dll Alias "_DAsperiod@8" (ByVal lngdin As Long, ByVal sngsper As Single) As Single Public Declare Sub DAclkin Lib "s2drv32s.dll" Alias "_DAclkin@8" (ByVal lngdin As Long, ByVal lngscode As Long) Public Declare Sub DAclkout Lib "s2drv32s.dll" Alia s "_DAclkout@8" (ByVal lngdin As Long, ByVal lngdcode As Long) Public Declare Sub DAnpts Lib "s2drv32s.dll" Alias "_DAnpts@8" (ByVal lngdin As Long, ByVal lngnpts As Long) Public Declare Sub DAmtrig Lib "s2drv32s.dll" Alias "_DAmtrig@4" (ByVal lngdin As Long) Public Declare Sub DAstrig Lib "s2drv32s.dll" Alias "_DAstrig@4" (ByVal lngdin As Long) Public Declare Function DAstatus Lib "s2drv32s.dll" Alias "_DAstatus@4" (ByVal lngdin As Long) As Long Public Declare Sub DAreps Lib "s2drv32s.dll" Alias "_DAreps@8" (ByVal lngdin As Long, ByVal lngnreps As Long) Public Declare Sub ET1clear Lib "s2drv32s.dll" Alia s "_ET1clear@4" (ByVal lngdin As Long) Public Declare Sub ET1mult Lib "s2drv32s.dll" Alias "_ET1mult@4" (ByVal lngdin As Long)

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171 Public Declare Sub ET1compare Lib "s2drv32s.dll" Al ias "_ET1compare@4" (ByVal lngdin As Long) Public Declare Sub ET1evcount Lib "s2drv32s.dll" Al ias "_ET1evcount@4" (ByVal lngdin As Long) Public Declare Sub ET1go Lib "s2drv32s.dll" Alias _ET1go@4" (ByVal lngdin As Long) Public Declare Sub ET1stop Lib "s2drv32s.dll" Alias "_ET1stop@4" (ByVal lngdin As Long) Public Declare Function ET1active Lib "s2drv32s.dll Alias "_ET1active@4" (ByVal lngdin As Long) As Long Public Declare Sub ET1blocks Lib "s2drv32s.dll" Ali as "_ET1blocks@8" (ByVal lngdin As Long, ByVal lngnblocks As Long) Public Declare Sub ET1xlogic Lib "s2drv32s.dll" Ali as "_ET1xlogic@8" (ByVal lngdin As Long, ByVal lnglmask As Long) Public Declare Function ET1report Lib "s2drv32s.dll Alias "_ET1report@4" (ByVal lngdin As Long) As Long Public Declare Function ET1read32 Lib "s2drv32s.dll Alias "_ET1read32@4" (ByVal lngdin As Long) As Long Public Declare Function ET1read16 Lib "s2drv32s.dll Alias "_ET1read16@4" (ByVal lngdin As Long) As Long Public Declare Sub ET1drop Lib "s2drv32s.dll" Alias "_ET1drop@4" (ByVal lngdin As Long) Public Declare Sub HTIclear Lib "s2drv32s.dll" Alia s "_HTIclear@4" (ByVal lngdin As Long) Public Declare Sub HTIgo Lib "s2drv32s.dll" Alias _HTIgo@4" (ByVal lngdin As Long) Public Declare Sub HTIstop Lib "s2drv32s.dll" Alias "_HTIstop@4" (ByVal lngdin As Long) Public Declare Sub HTIreadAER Lib "s2drv32s.dll" Al ias "_HTIreadAER@16" (ByVal lngdin As Long, ByRef sngaz As Single, ByRef sngel As Single, ByRef sngroll As Single) Public Declare Sub HTIreadXYZ Lib "s2drv32s.dll" Al ias "_HTIreadXYZ@16" (ByVal lngdin As Long, ByRef sngx As Single, ByRef sngy As Single, ByRef sngz As Single) Public Declare Sub HTIwriteraw Lib "s2drv32s.dll" A lias "_HTIwriteraw@8" (ByVal lngdin As Long, ByRef bytcmdstr As Byte) Public Declare Sub HTIsetraw Lib "s2drv32s.dll" Ali as "_HTIsetraw@10" (ByVal lngdin As Long, ByVal lngnbytes As Long, ByVal bytc1 As By te, ByVal bytc2 As Byte) Public Declare Sub HTIreadraw Lib "s2drv32s.dll" Al ias "_HTIreadraw@12" (ByVal lngdin As Long, ByVal lngmaxchars As Long, ByRef by tbuf As Byte) Public Declare Sub HTIboresight Lib "s2drv32s.dll" Alias "_HTIboresight@4" (ByVal lngdin As Long) Public Declare Sub HTIreset Lib "s2drv32s.dll" Alia s "_HTIreset@4" (ByVal lngdin As Long) Public Declare Sub HTIshowparam Lib "s2drv32s.dll" Alias "_HTIshowparam@8" (ByVal lngdin As Long, ByVal lngpid As Long) Public Declare Function HTIreadone Lib "s2drv32s.dl l" Alias "_HTIreadone@8" (ByVal lngdin As Long, ByVal lngpid As Long) As Single Public Declare Sub HTIfastAER Lib "s2drv32s.dll" Al ias "_HTIfastAER@16" (ByVal lngdin As Long, ByRef lngaz As Long, ByRef lngel As Long, ByRef lngroll As Long) Public Declare Sub HTIfastXYZ Lib "s2drv32s.dll" Al ias "_HTIfastXYZ@16" (ByVal lngdin As Long, ByRef lngx As Long, ByRef lngy As L ong, ByRef lngz As Long) Public Declare Function HTIgetecode Lib "s2drv32s.d ll" Alias "_HTIgetecode@4" (ByVal lngdin As Long) As Long

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172 Public Declare Sub HTIisISO Lib "s2drv32s.dll" Alia s "_HTIisISO@4" (ByVal lngdin As Long) Public Declare Function LoadHRTFFile Lib "s2drv32s. dll" Alias "_LoadHRTFFile@8" (ByRef hrtf As Variant, ByRef fname As Byte) As Lon g Public Declare Sub MC1clear Lib "s2drv32s.dll" Alia s "_MC1clear@4" (ByVal lngdin As Long) Public Declare Sub MC1pos Lib "s2drv32s.dll" Alias "_MC1pos@8" (ByVal lngdin As Long, ByVal lngpos As Long) Public Declare Sub MC1vel Lib "s2drv32s.dll" Alias "_MC1vel@12" (ByVal lngdin As Long, ByVal lngvel As Long, ByVal lngperm As Long) Public Declare Sub MC1acc Lib "s2drv32s.dll" Alias "_MC1acc@12" (ByVal lngdin As Long, ByVal lngacc As Long, ByVal lngperm As Long) Public Declare Sub MC1move Lib "s2drv32s.dll" Alias "_MC1move@4" (ByVal lngdin As Long) Public Declare Sub MC1syncmove Lib "s2drv32s.dll" A lias "_MC1syncmove@4" (ByVal lngdin As Long) Public Declare Sub MC1gear Lib "s2drv32s.dll" Alias "_MC1gear@8" (ByVal lngdin As Long, ByVal snggratio As Single) Public Declare Sub MC1home Lib "s2drv32s.dll" Alias "_MC1home@8" (ByVal lngdin As Long, ByVal lnghome As Long) Public Declare Sub MC1boundry Lib "s2drv32s.dll" Al ias "_MC1boundry@12" (ByVal lngdin As Long, ByVal lngminp As Long, ByVal lngmax p As Long) Public Declare Sub MC1reference Lib "s2drv32s.dll" Alias "_MC1reference@16" (ByVal lngdin As Long, ByVal lngrefmode As Long, ByVal lng srchvel As Long, ByVal lngrefpos As Long) Public Declare Sub MC1filter Lib "s2drv32s.dll" Ali as "_MC1filter@12" (ByVal lngdin As Long, ByVal lngpar As Long, ByVal lngv As Long) Public Declare Function MC1status Lib "s2drv32s.dll Alias "_MC1status@4" (ByVal lngdin As Long) As Long Public Declare Function MC1curpos Lib "s2drv32s.dll Alias "_MC1curpos@4" (ByVal lngdin As Long) As Long Public Declare Function MC1curvel Lib "s2drv32s.dll Alias "_MC1curvel@4" (ByVal lngdin As Long) As Long Public Declare Sub MC1go Lib "s2drv32s.dll" Alias _MC1go@4" (ByVal lngdin As Long) Public Declare Sub MC1stop Lib "s2drv32s.dll" Alias "_MC1stop@4" (ByVal lngdin As Long) Public Declare Sub MC1kill Lib "s2drv32s.dll" Alias "_MC1kill@4" (ByVal lngdin As Long) Public Declare Sub MC1zero Lib "s2drv32s.dll" Alias "_MC1zero@4" (ByVal lngdin As Long) Public Declare Sub MC1gohome Lib "s2drv32s.dll" Ali as "_MC1gohome@4" (ByVal lngdin As Long) Public Declare Sub MC1goref Lib "s2drv32s.dll" Alia s "_MC1goref@4" (ByVal lngdin As Long) Public Declare Function MC1getparam Lib "s2drv32s.d ll" Alias "_MC1getparam@8" (ByVal lngdin As Long, ByVal lngparcode As Long) As Long Public Declare Sub PA4atten Lib "s2drv32s.dll" Alia s "_PA4atten@8" (ByVal lngdin As Long, ByVal sngatten As Single) Public Declare Sub PA4setup Lib "s2drv32s.dll" Alia s "_PA4setup@12" (ByVal lngdin As Long, ByVal sngbase As Single, ByVal sngstep As Sin gle)

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173 Public Declare Sub PA4mute Lib "s2drv32s.dll" Alias "_PA4mute@4" (ByVal lngdin As Long) Public Declare Sub PA4nomute Lib "s2drv32s.dll" Ali as "_PA4nomute@4" (ByVal lngdin As Long) Public Declare Sub PA4ac Lib "s2drv32s.dll" Alias _PA4ac@4" (ByVal lngdin As Long) Public Declare Sub PA4dc Lib "s2drv32s.dll" Alias _PA4dc@4" (ByVal lngdin As Long) Public Declare Sub PA4man Lib "s2drv32s.dll" Alias "_PA4man@4" (ByVal lngdin As Long) Public Declare Sub PA4auto Lib "s2drv32s.dll" Alias "_PA4auto@4" (ByVal lngdin As Long) Public Declare Function PA4read Lib "s2drv32s.dll" Alias "_PA4read@4" (ByVal lngdin As Long) As Single Public Declare Sub PI2clear Lib "s2drv32s.dll" Alia s "_PI2clear@4" (ByVal lngdin As Long) Public Declare Sub PI2outs Lib "s2drv32s.dll" Alias "_PI2outs@8" (ByVal lngdin As Long, ByVal lngomask As Long) Public Declare Sub PI2logic Lib "s2drv32s.dll" Alia s "_PI2logic@12" (ByVal lngdin As Long, ByVal lnglogout As Long, ByVal lnglogin As Lo ng) Public Declare Sub PI2write Lib "s2drv32s.dll" Alia s "_PI2write@8" (ByVal lngdin As Long, ByVal lngbitcode As Long) Public Declare Function PI2read Lib "s2drv32s.dll" Alias "_PI2read@4" (ByVal lngdin As Long) As Long Public Declare Sub PI2debounce Lib "s2drv32s.dll" A lias "_PI2debounce@8" (ByVal lngdin As Long, ByVal lngdbtime As Long) Public Declare Sub PI2autotime Lib "s2drv32s.dll" A lias "_PI2autotime@12" (ByVal lngdin As Long, ByVal lngbitn As Long, ByVal lngdur As Long) Public Declare Sub PI2setbit Lib "s2drv32s.dll" Ali as "_PI2setbit@8" (ByVal lngdin As Long, ByVal lngbitmask As Long) Public Declare Sub PI2clrbit Lib "s2drv32s.dll" Ali as "_PI2clrbit@8" (ByVal lngdin As Long, ByVal lngbitmask As Long) Public Declare Sub PI2zerotime Lib "s2drv32s.dll" A lias "_PI2zerotime@8" (ByVal lngdin As Long, ByVal lngbitmask As Long) Public Declare Function PI2gettime Lib "s2drv32s.dl l" Alias "_PI2gettime@8" (ByVal lngdin As Long, ByVal lngbitn As Long) As Long Public Declare Sub PI2latch Lib "s2drv32s.dll" Alia s "_PI2latch@8" (ByVal lngdin As Long, ByVal lnglmask As Long) Public Declare Sub PI2map Lib "s2drv32s.dll" Alias "_PI2map@12" (ByVal lngdin As Long, ByVal lngbitn As Long, ByVal lngmmask As Long ) Public Declare Sub PI2outsX Lib "s2drv32s.dll" Alia s "_PI2outsX@8" (ByVal lngdin As Long, ByVal lngpnum As Long) Public Declare Sub PI2writeX Lib "s2drv32s.dll" Ali as "_PI2writeX@12" (ByVal lngdin As Long, ByVal lngpnum As Long, ByVal lngval As Long) Public Declare Function PI2readX Lib "s2drv32s.dll" Alias "_PI2readX@8" (ByVal lngdin As Long, ByVal lngpnum As Long) As Long Public Declare Sub PI2toggle Lib "s2drv32s.dll" Ali as "_PI2toggle@8" (ByVal lngdin As Long, ByVal lngtmask As Long) Public Declare Sub PF1type Lib "s2drv32s.dll" Alias "_PF1type@12" (ByVal lngdin As Long, ByVal lngtype As Long, ByVal lngntaps As Long ) Public Declare Sub PF1begin Lib "s2drv32s.dll" Alia s "_PF1begin@4" (ByVal lngdin As Long)

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174 Public Declare Sub PF1bypass Lib "s2drv32s.dll" Ali as "_PF1bypass@4" (ByVal lngdin As Long) Public Declare Sub PF1nopass Lib "s2drv32s.dll" Ali as "_PF1nopass@4" (ByVal lngdin As Long) Public Declare Sub PF1b16 Lib "s2drv32s.dll" Alias "_PF1b16@8" (ByVal lngdin As Long, ByVal lngbcoe As Long) Public Declare Sub PF1a16 Lib "s2drv32s.dll" Alias "_PF1a16@8" (ByVal lngdin As Long, ByVal lngacoe As Long) Public Declare Sub PF1b32 Lib "s2drv32s.dll" Alias "_PF1b32@8" (ByVal lngdin As Long, ByVal lngbcoe As Long) Public Declare Sub PF1a32 Lib "s2drv32s.dll" Alias "_PF1a32@8" (ByVal lngdin As Long, ByVal lngacoe As Long) Public Declare Sub PF1freq Lib "s2drv32s.dll" Alias "_PF1freq@12" (ByVal lngdin As Long, ByVal lnglpfreq As Long, ByVal lnghpfreq As L ong) Public Declare Sub PF1gain Lib "s2drv32s.dll" Alias "_PF1gain@12" (ByVal lngdin As Long, ByVal lnglpgain As Long, ByVal lnghpgain As L ong) Public Declare Sub PF1fir16 Lib "s2drv32s.dll" Alia s "_PF1fir16@12" (ByVal lngdin As Long, ByRef sngbcoes As Single, ByVal lngntaps As L ong) Public Declare Sub PF1fir32 Lib "s2drv32s.dll" Alia s "_PF1fir32@12" (ByVal lngdin As Long, ByRef sngbcoes As Single, ByVal lngntaps As L ong) Public Declare Sub PF1iir32 Lib "s2drv32s.dll" Alia s "_PF1iir32@16" (ByVal lngdin As Long, ByRef sngbcoes As Single, ByRef sngacoes As S ingle, ByVal lngntaps As Long) Public Declare Sub PF1biq16 Lib "s2drv32s.dll" Alia s "_PF1biq16@16" (ByVal lngdin As Long, ByRef sngbcoes As Single, ByRef sngacoes As S ingle, ByVal lngnbiqs As Long) Public Declare Sub PF1biq32 Lib "s2drv32s.dll" Alia s "_PF1biq32@16" (ByVal lngdin As Long, ByRef sngbcoes As Single, ByRef sngacoes As S ingle, ByVal lngnbiqs As Long) Public Declare Sub PM1clear Lib "s2drv32s.dll" Alia s "_PM1clear@4" (ByVal lngdin As Long) Public Declare Sub PM1config Lib "s2drv32s.dll" Ali as "_PM1config@8" (ByVal lngdin As Long, ByVal lngconfig As Long) Public Declare Sub PM1mode Lib "s2drv32s.dll" Alias "_PM1mode@8" (ByVal lngdin As Long, ByVal lngcmode As Long) Public Declare Sub PM1spkon Lib "s2drv32s.dll" Alia s "_PM1spkon@8" (ByVal lngdin As Long, ByVal lngsn As Long) Public Declare Sub PM1spkoff Lib "s2drv32s.dll" Ali as "_PM1spkoff@8" (ByVal lngdin As Long, ByVal lngsn As Long) Public Declare Sub PD1clear Lib "s2drv32s.dll" Alia s "_PD1clear@4" (ByVal lngdin As Long) Public Declare Sub PD1go Lib "s2drv32s.dll" Alias _PD1go@4" (ByVal lngdin As Long) Public Declare Sub PD1stop Lib "s2drv32s.dll" Alias "_PD1stop@4" (ByVal lngdin As Long) Public Declare Sub PD1arm Lib "s2drv32s.dll" Alias "_PD1arm@4" (ByVal lngdin As Long) Public Declare Sub PD1nstrms Lib "s2drv32s.dll" Ali as "_PD1nstrms@12" (ByVal lngdin As Long, ByVal lngnDAC As Long, ByVal lngnADC As Lo ng) Public Declare Sub PD1srate Lib "s2drv32s.dll" Alia s "_PD1srate@8" (ByVal lngdin As Long, ByVal sngsrate As Single) Public Declare Function PD1speriod Lib "s2drv32s.dl l" Alias "_PD1speriod@8" (ByVal lngdin As Long, ByVal sngsper As Single) As Single

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175 Public Declare Sub PD1clkin Lib "s2drv32s.dll" Alia s "_PD1clkin@8" (ByVal lngdin As Long, ByVal lngscode As Long) Public Declare Sub PD1clkout Lib "s2drv32s.dll" Ali as "_PD1clkout@8" (ByVal lngdin As Long, ByVal lngdcode As Long) Public Declare Sub PD1npts Lib "s2drv32s.dll" Alias "_PD1npts@8" (ByVal lngdin As Long, ByVal lngnpts As Long) Public Declare Sub PD1mtrig Lib "s2drv32s.dll" Alia s "_PD1mtrig@4" (ByVal lngdin As Long) Public Declare Sub PD1strig Lib "s2drv32s.dll" Alia s "_PD1strig@4" (ByVal lngdin As Long) Public Declare Function PD1status Lib "s2drv32s.dll Alias "_PD1status@4" (ByVal lngdin As Long) As Long Public Declare Sub PD1reps Lib "s2drv32s.dll" Alias "_PD1reps@8" (ByVal lngdin As Long, ByVal lngnreps As Long) Public Declare Sub PD1tgo Lib "s2drv32s.dll" Alias "_PD1tgo@4" (ByVal lngdin As Long) Public Declare Sub PD1zero Lib "s2drv32s.dll" Alias "_PD1zero@4" (ByVal lngdin As Long) Public Declare Sub PD1xcmd Lib "s2drv32s.dll" Alias "_PD1xcmd@16" (ByVal lngdin As Long, ByRef intv As Integer, ByVal lngn As Long, By Ref bytcaller As Byte) Public Declare Sub PD1xdata Lib "s2drv32s.dll" Alia s "_PD1xdata@8" (ByVal lngdin As Long, ByVal lngdata_id As Long) Public Declare Sub PD1xboot Lib "s2drv32s.dll" Alia s "_PD1xboot@4" (ByVal lngdin As Long) Public Declare Function PD1checkDSPS Lib "s2drv32s. dll" Alias "_PD1checkDSPS@4" (ByVal lngdin As Long) As Long Public Declare Function PD1what Lib "s2drv32s.dll" Alias "_PD1what@16" (ByVal lngdin As Long, ByVal lngdcode As Long, ByVal lngdnum As L ong, ByRef bytcaller As Byte) As Long Public Declare Sub PD1mode Lib "s2drv32s.dll" Alias "_PD1mode@8" (ByVal lngdin As Long, ByVal lngmode As Long) Public Declare Function PD1export Lib "s2drv32s.dll Alias "_PD1export@8" (ByVal lngvarcode As Long, ByRef lngindicies As Long) As L ong Public Declare Sub PD1resetRTE Lib "s2drv32s.dll" A lias "_PD1resetRTE@4" (ByVal lngdin As Long) Public Declare Sub PD1nstrmsRTE Lib "s2drv32s.dll" Alias "_PD1nstrmsRTE@12" (ByVal lngdin As Long, ByVal lngnIC As Long, ByVal lngnOG As Long) Public Declare Sub PD1flushRTE Lib "s2drv32s.dll" A lias "_PD1flushRTE@4" (ByVal lngdin As Long) Public Declare Sub PD1clrIO Lib "s2drv32s.dll" Alia s "_PD1clrIO@4" (ByVal lngdin As Long) Public Declare Sub PD1setIO Lib "s2drv32s.dll" Alia s "_PD1setIO@20" (ByVal lngdin As Long, ByVal sngdt1 As Single, ByVal sngdt2 As Singl e, ByVal sngat1 As Single, ByVal sngat2 As Single) Public Declare Sub PD1clrDEL Lib "s2drv32s.dll" Ali as "_PD1clrDEL@20" (ByVal lngdin As Long, ByVal lngch1 As Long, ByVal lngch2 As Long ByVal lngch3 As Long, ByVal lngch4 As Long) Public Declare Sub PD1setDEL Lib "s2drv32s.dll" Ali as "_PD1setDEL@12" (ByVal lngdin As Long, ByVal lngtap As Long, ByVal lngdly As Long)

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176 Public Declare Sub PD1latchDEL Lib "s2drv32s.dll" A lias "_PD1latchDEL@4" (ByVal lngdin As Long) Public Declare Sub PD1flushDEL Lib "s2drv32s.dll" A lias "_PD1flushDEL@4" (ByVal lngdin As Long) Public Declare Sub PD1interpDEL Lib "s2drv32s.dll" Alias "_PD1interpDEL@8" (ByVal lngdin As Long, ByVal lngifact As Long) Public Declare Sub PD1clrsched Lib "s2drv32s.dll" A lias "_PD1clrsched@4" (ByVal lngdin As Long) Public Declare Sub PD1addsimp Lib "s2drv32s.dll" Al ias "_PD1addsimp@12" (ByVal lngdin As Long, ByVal lngsrc As Long, ByVal lngdes As Long) Public Declare Sub PD1addmult Lib "s2drv32s.dll" Al ias "_PD1addmult@20" (ByVal lngdin As Long, ByRef lngsrc As Long, ByRef sngsf A s Single, ByVal lngnsrcs As Long, ByVal lngdes As Long) Public Declare Sub PD1specIB Lib "s2drv32s.dll" Ali as "_PD1specIB@12" (ByVal lngdin As Long, ByVal lngIBnum As Long, ByVal lngdesaddr A s Long) Public Declare Sub PD1specOB Lib "s2drv32s.dll" Ali as "_PD1specOB@12" (ByVal lngdin As Long, ByVal lngOBnum As Long, ByVal lngsr caddr As Long) Public Declare Sub PD1idleDSP Lib "s2drv32s.dll" Al ias "_PD1idleDSP@8" (ByVal lngdin As Long, ByVal lngdmask As Long) Public Declare Sub PD1resetDSP Lib "s2drv32s.dll" A lias "_PD1resetDSP@8" (ByVal lngdin As Long, ByVal lngdmask As Long) Public Declare Sub PD1bypassDSP Lib "s2drv32s.dll" Alias "_PD1bypassDSP@8" (ByVal lngdin As Long, ByVal lngdmask As Long) Public Declare Sub PD1lockDSP Lib "s2drv32s.dll" Al ias "_PD1lockDSP@8" (ByVal lngdin As Long, ByVal lngdmask As Long) Public Declare Sub PD1interpDSP Lib "s2drv32s.dll" Alias "_PD1interpDSP@12" (ByVal lngdin As Long, ByVal lngifact As Long, ByVal lngdm ask As Long) Public Declare Sub PD1bootDSP Lib "s2drv32s.dll" Al ias "_PD1bootDSP@12" (ByVal lngdin As Long, ByVal lngdmask As Long, ByRef bytfn ame As Byte) Public Declare Sub PD1syncall Lib "s2drv32s.dll" Al ias "_PD1syncall@4" (ByVal lngdin As Long) Public Declare Function PD1whatDEL Lib "s2drv32s.dl l" Alias "_PD1whatDEL@4" (ByVal lngdin As Long) As Long Public Declare Function PD1whatIO Lib "s2drv32s.dll Alias "_PD1whatIO@4" (ByVal lngdin As Long) As Long Public Declare Function PD1whatDSP Lib "s2drv32s.dl l" Alias "_PD1whatDSP@8" (ByVal lngdin As Long, ByVal lngdn As Long) As Long Public Declare Function PreLoadRaw Lib "s2drv32s.dl l" Alias "_PreLoadRaw@36" (ByVal lngdin As Long, ByVal lngdspn As Long, ByVal lngopmode As Long, ByVal lngstype As Long, ByRef bytsrc_lm As Byte, ByRef by tsrc_r As Byte, ByVal sngsf_lm As Single, ByVal sngsf_r As Single, ByVal lnglock As L ong) As Long Public Declare Function PreLoadHRTF Lib "s2drv32s.d ll" Alias "_PreLoadHRTF@36" (ByVal lngdin As Long, ByVal lngdspn As Long, ByVal lngctype As Long, ByRef bytfname As Byte, ByVal sngaz As Single, ByVal snge l As Single, ByVal sngsf_l As Single, ByVal sngsf_r As Single, ByVal lnglock As L ong) As Long Public Declare Sub PushHRTF Lib "s2drv32s.dll" Alia s "_PushHRTF@20" (ByRef hrtf As Variant, ByVal faz As Single, ByVal fel As Single, ByVal lrs As Long, ByVal DBN As Long) Public Declare Sub PD1fixbug Lib "s2drv32s.dll" Ali as "_PD1fixbug@4" (ByVal lngdin As Long)

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177 Public Declare Sub SW2on Lib "s2drv32s.dll" Alias _SW2on@4" (ByVal lngdin As Long) Public Declare Sub SW2off Lib "s2drv32s.dll" Alias "_SW2off@4" (ByVal lngdin As Long) Public Declare Sub SW2ton Lib "s2drv32s.dll" Alias "_SW2ton@4" (ByVal lngdin As Long) Public Declare Sub SW2toff Lib "s2drv32s.dll" Alias "_SW2toff@4" (ByVal lngdin As Long) Public Declare Sub SW2rftime Lib "s2drv32s.dll" Ali as "_SW2rftime@8" (ByVal lngdin As Long, ByVal sngrftime As Single) Public Declare Sub SW2shape Lib "s2drv32s.dll" Alia s "_SW2shape@8" (ByVal lngdin As Long, ByVal lngshcode As Long) Public Declare Sub SW2trig Lib "s2drv32s.dll" Alias "_SW2trig@8" (ByVal lngdin As Long, ByVal lngtcode As Long) Public Declare Sub SW2dur Lib "s2drv32s.dll" Alias "_SW2dur@8" (ByVal lngdin As Long, ByVal lngdur As Long) Public Declare Function SW2status Lib "s2drv32s.dll Alias "_SW2status@4" (ByVal lngdin As Long) As Long Public Declare Sub SW2clear Lib "s2drv32s.dll" Alia s "_SW2clear@4" (ByVal lngdin As Long) Public Declare Sub SD1go Lib "s2drv32s.dll" Alias _SD1go@4" (ByVal lngdin As Long) Public Declare Sub SD1stop Lib "s2drv32s.dll" Alias "_SD1stop@4" (ByVal lngdin As Long) Public Declare Sub SD1use_enable Lib "s2drv32s.dll" Alias "_SD1use_enable@4" (ByVal lngdin As Long) Public Declare Sub SD1no_enable Lib "s2drv32s.dll" Alias "_SD1no_enable@4" (ByVal lngdin As Long) Public Declare Sub SD1hoop Lib "s2drv32s.dll" Alias "_SD1hoop@28" (ByVal lngdin As Long, ByVal lngnum As Long, ByVal lngslope As Long, ByVal sngdly As Single, ByVal sngwidth As Single, ByVal sngupper As Single, ByVal snglower As Single) Public Declare Sub SD1numhoops Lib "s2drv32s.dll" A lias "_SD1numhoops@8" (ByVal lngdin As Long, ByVal lngnh As Long) Public Declare Function SD1count Lib "s2drv32s.dll" Alias "_SD1count@4" (ByVal lngdin As Long) As Long Public Declare Sub SD1up Lib "s2drv32s.dll" Alias _SD1up@8" (ByVal lngdin As Long, ByRef bytcbuf As Byte) Public Declare Sub SD1down Lib "s2drv32s.dll" Alias "_SD1down@8" (ByVal lngdin As Long, ByRef bytcbuf As Byte) Public Declare Sub SS1clear Lib "s2drv32s.dll" Alia s "_SS1clear@4" (ByVal lngdin As Long) Public Declare Sub SS1gainon Lib "s2drv32s.dll" Ali as "_SS1gainon@4" (ByVal lngdin As Long) Public Declare Sub SS1gainoff Lib "s2drv32s.dll" Al ias "_SS1gainoff@4" (ByVal lngdin As Long) Public Declare Sub SS1mode Lib "s2drv32s.dll" Alias "_SS1mode@8" (ByVal lngdin As Long, ByVal lngmcode As Long) Public Declare Sub SS1select Lib "s2drv32s.dll" Ali as "_SS1select@12" (ByVal lngdin As Long, ByVal lngchan As Long, ByVal lnginpn As Lo ng) Public Declare Function S2init Lib "s2drv32s.dll" A lias "_S2init@12" (ByVal lngdn As Long, ByVal lngmode As Long, ByVal lngapt As Long) As Long

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178 Public Declare Sub S2close Lib "s2drv32s.dll" Alias "_S2close@0" () Public Declare Sub TG6clear Lib "s2drv32s.dll" Alia s "_TG6clear@4" (ByVal lngdin As Long) Public Declare Sub TG6arm Lib "s2drv32s.dll" Alias "_TG6arm@8" (ByVal lngdin As Long, ByVal lngsnum As Long) Public Declare Sub TG6go Lib "s2drv32s.dll" Alias _TG6go@4" (ByVal lngdin As Long) Public Declare Sub TG6tgo Lib "s2drv32s.dll" Alias "_TG6tgo@4" (ByVal lngdin As Long) Public Declare Sub TG6stop Lib "s2drv32s.dll" Alias "_TG6stop@4" (ByVal lngdin As Long) Public Declare Sub TG6baserate Lib "s2drv32s.dll" A lias "_TG6baserate@8" (ByVal lngdin As Long, ByVal lngbrcode As Long) Public Declare Sub TG6new Lib "s2drv32s.dll" Alias "_TG6new@16" (ByVal lngdin As Long, ByVal lngsnum As Long, ByVal lnglgth As Long, ByVal lngdmask As Long) Public Declare Sub TG6high Lib "s2drv32s.dll" Alias "_TG6high@20" (ByVal lngdin As Long, ByVal lngsnum As Long, ByVal lng_beg As Long, ByVal lng_end As Long, ByVal lnghmask As Long) Public Declare Sub TG6low Lib "s2drv32s.dll" Alias "_TG6low@20" (ByVal lngdin As Long, ByVal lngsnum As Long, ByVal lng_beg As Long, ByVal lng_end As Long, ByVal lnglmask As Long) Public Declare Sub TG6value Lib "s2drv32s.dll" Alia s "_TG6value@20" (ByVal lngdin As Long, ByVal lngsnum As Long, ByVal lng_beg As Long, ByVal lng_end As Long, ByVal lngval As Long) Public Declare Sub TG6dup Lib "s2drv32s.dll" Alias "_TG6dup@28" (ByVal lngdin As Long, ByVal lngsnum As Long, ByVal lngs_beg As Long ByVal lngs_end As Long, ByVal lngd_beg As Long, ByVal lngndups As Long, ByV al lngdmask As Long) Public Declare Sub TG6reps Lib "s2drv32s.dll" Alias "_TG6reps@12" (ByVal lngdin As Long, ByVal lngrmode As Long, ByVal lngrcount As Lo ng) Public Declare Function TG6status Lib "s2drv32s.dll Alias "_TG6status@4" (ByVal lngdin As Long) As Long Public Declare Sub WG1on Lib "s2drv32s.dll" Alias _WG1on@4" (ByVal lngdin As Long) Public Declare Sub WG1off Lib "s2drv32s.dll" Alias "_WG1off@4" (ByVal lngdin As Long) Public Declare Sub WG1clear Lib "s2drv32s.dll" Alia s "_WG1clear@4" (ByVal lngdin As Long) Public Declare Sub WG1amp Lib "s2drv32s.dll" Alias "_WG1amp@8" (ByVal lngdin As Long, ByVal sngamp As Single) Public Declare Sub WG1freq Lib "s2drv32s.dll" Alias "_WG1freq@8" (ByVal lngdin As Long, ByVal sngfreq As Single) Public Declare Sub WG1swrt Lib "s2drv32s.dll" Alias "_WG1swrt@8" (ByVal lngdin As Long, ByVal sngswrt As Single) Public Declare Sub WG1phase Lib "s2drv32s.dll" Alia s "_WG1phase@8" (ByVal lngdin As Long, ByVal sngphase As Single) Public Declare Sub WG1dc Lib "s2drv32s.dll" Alias _WG1dc@8" (ByVal lngdin As Long, ByVal sngdc As Single) Public Declare Sub WG1shape Lib "s2drv32s.dll" Alia s "_WG1shape@8" (ByVal lngdin As Long, ByVal lngscon As Long) Public Declare Sub WG1dur Lib "s2drv32s.dll" Alias "_WG1dur@8" (ByVal lngdin As Long, ByVal sngdur As Single)

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179 Public Declare Sub WG1rf Lib "s2drv32s.dll" Alias _WG1rf@8" (ByVal lngdin As Long, ByVal sngrf As Single) Public Declare Sub WG1trig Lib "s2drv32s.dll" Alias "_WG1trig@8" (ByVal lngdin As Long, ByVal lngtcode As Long) Public Declare Sub WG1seed Lib "s2drv32s.dll" Alias "_WG1seed@8" (ByVal lngdin As Long, ByVal lngseed As Long) Public Declare Sub WG1delta Lib "s2drv32s.dll" Alia s "_WG1delta@8" (ByVal lngdin As Long, ByVal lngdelta As Long) Public Declare Sub WG1wave Lib "s2drv32s.dll" Alias "_WG1wave@12" (ByVal lngdin As Long, ByRef intwave As Integer, ByVal lngnpts As Long) Public Declare Function WG1status Lib "s2drv32s.dll Alias "_WG1status@4" (ByVal lngdin As Long) As Long Public Declare Sub WG1ton Lib "s2drv32s.dll" Alias "_WG1ton@4" (ByVal lngdin As Long) Public Declare Sub WG2on Lib "s2drv32s.dll" Alias _WG2on@4" (ByVal lngdin As Long) Public Declare Sub WG2off Lib "s2drv32s.dll" Alias "_WG2off@4" (ByVal lngdin As Long) Public Declare Sub WG2clear Lib "s2drv32s.dll" Alia s "_WG2clear@4" (ByVal lngdin As Long) Public Declare Sub WG2amp Lib "s2drv32s.dll" Alias "_WG2amp@8" (ByVal lngdin As Long, ByVal sngamp As Single) Public Declare Sub WG2freq Lib "s2drv32s.dll" Alias "_WG2freq@8" (ByVal lngdin As Long, ByVal sngfreq As Single) Public Declare Sub WG2swrt Lib "s2drv32s.dll" Alias "_WG2swrt@8" (ByVal lngdin As Long, ByVal sngswrt As Single) Public Declare Sub WG2phase Lib "s2drv32s.dll" Alia s "_WG2phase@8" (ByVal lngdin As Long, ByVal sngphase As Single) Public Declare Sub WG2dc Lib "s2drv32s.dll" Alias _WG2dc@8" (ByVal lngdin As Long, ByVal sngdc As Single) Public Declare Sub WG2shape Lib "s2drv32s.dll" Alia s "_WG2shape@8" (ByVal lngdin As Long, ByVal lngscon As Long) Public Declare Sub WG2dur Lib "s2drv32s.dll" Alias "_WG2dur@8" (ByVal lngdin As Long, ByVal sngdur As Single) Public Declare Sub WG2rf Lib "s2drv32s.dll" Alias _WG2rf@8" (ByVal lngdin As Long, ByVal sngrf As Single) Public Declare Sub WG2trig Lib "s2drv32s.dll" Alias "_WG2trig@8" (ByVal lngdin As Long, ByVal lngtcode As Long) Public Declare Sub WG2seed Lib "s2drv32s.dll" Alias "_WG2seed@8" (ByVal lngdin As Long, ByVal lngseed As Long) Public Declare Sub WG2delta Lib "s2drv32s.dll" Alia s "_WG2delta@8" (ByVal lngdin As Long, ByVal lngdelta As Long) Public Declare Sub WG2wave Lib "s2drv32s.dll" Alias "_WG2wave@12" (ByVal lngdin As Long, ByRef intwave As Integer, ByVal lngnpts As Long) Public Declare Function WG2status Lib "s2drv32s.dll Alias "_WG2status@4" (ByVal lngdin As Long) As Long Public Declare Sub WG2ton Lib "s2drv32s.dll" Alias "_WG2ton@4" (ByVal lngdin As Long) Public Declare Function XB1init Lib "s2drv32s.dll" Alias "_XB1init@4" (ByVal lngmode As Long) As Long

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180 Public Declare Sub XB1close Lib "s2drv32s.dll" Alia s "_XB1close@0" () Public Declare Sub XB1flush Lib "s2drv32s.dll" Alia s "_XB1flush@0" () Public Declare Sub XB1rawout Lib "s2drv32s.dll" Ali as "_XB1rawout@4" (ByVal lngv As Long) Public Declare Function XB1rawin Lib "s2drv32s.dll" Alias "_XB1rawin@4" (ByVal lngwait As Long) As Long Public Declare Function XB1device Lib "s2drv32s.dll Alias "_XB1device@8" (ByVal lngdevcode As Long, ByVal lngdn As Long) As Long Public Declare Function XB1getdevice Lib "s2drv32s. dll" Alias "_XB1getdevice@16" (ByVal lngrn As Long, ByVal lngpn As Long, ByRef by tdtxt As Byte, ByRef lngrdin As Long) As Long Public Declare Sub XB1gtrig Lib "s2drv32s.dll" Alia s "_XB1gtrig@0" () Public Declare Sub XB1ltrig Lib "s2drv32s.dll" Alia s "_XB1ltrig@4" (ByVal lngrn As Long) Public Declare Function XB1version Lib "s2drv32s.dl l" Alias "_XB1version@8" (ByVal lngdevcode As Long, ByVal lngdn As Long) As Long Public Declare Function XBlock Lib "s2drv32s.dll" A lias "_XBlock@8" (ByVal lngmtry As Long, ByVal lngfstart As Long) As Long Public Declare Sub XBunlock Lib "s2drv32s.dll" Alia s "_XBunlock@4" (ByVal lngfend As Long) Public Declare Function UB_allotf Lib "s2drv32s.dll Alias "__allotf@4" (ByVal lngnpts As Long) As Long Public Declare Function UB_allot16 Lib "s2drv32s.dl l" Alias "__allot16@4" (ByVal lngnpts As Long) As Long Public Declare Sub UB_iir Lib "s2drv32s.dll" Alias "__iir@0" () Public Declare Sub UB_fir Lib "s2drv32s.dll" Alias "__fir@0" () Public Declare Sub allotf Lib "s2drv32s.dll" Alias "_allotf@8" (ByVal lngbid As Long, ByVal lngnpts As Long) Public Declare Sub allot16 Lib "s2drv32s.dll" Alias "_allot16@8" (ByVal lngbid As Long, ByVal lngnpts As Long) Public Declare Sub alogten Lib "s2drv32s.dll" Alias "_alogten@0" () Public Declare Sub aloge Lib "s2drv32s.dll" Alias _aloge@0" () Public Declare Sub add Lib "s2drv32s.dll" Alias "_a dd@0" () Public Declare Sub absval Lib "s2drv32s.dll" Alias "_absval@0" () Public Declare Sub acosine Lib "s2drv32s.dll" Alias "_acosine@0" () Public Declare Sub asine Lib "s2drv32s.dll" Alias _asine@0" () Public Declare Sub atangent Lib "s2drv32s.dll" Alia s "_atangent@0" () Public Declare Sub atantwo Lib "s2drv32s.dll" Alias "_atantwo@0" () Public Declare Function average Lib "s2drv32s.dll" Alias "_average@0" () As Single Public Declare Sub block Lib "s2drv32s.dll" Alias _block@8" (ByVal lngsp As Long, ByVal lngep As Long) Public Declare Sub cat Lib "s2drv32s.dll" Alias "_c at@0" () Public Declare Sub catn Lib "s2drv32s.dll" Alias "_ catn@4" (ByVal lngn As Long) Public Declare Sub cmult Lib "s2drv32s.dll" Alias _cmult@0" () Public Declare Sub cadd Lib "s2drv32s.dll" Alias "_ cadd@0" () Public Declare Sub cinv Lib "s2drv32s.dll" Alias "_ cinv@0" () Public Declare Sub cfft Lib "s2drv32s.dll" Alias "_ cfft@0" () Public Declare Sub cift Lib "s2drv32s.dll" Alias "_ cift@0" () Public Declare Sub cosine Lib "s2drv32s.dll" Alias "_cosine@0" ()

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181 Public Declare Sub chgplay Lib "s2drv32s.dll" Alias "_chgplay@4" (ByVal lngdbn As Long) Public Declare Sub cumsum Lib "s2drv32s.dll" Alias "_cumsum@0" () Public Declare Sub dpush Lib "s2drv32s.dll" Alias _dpush@4" (ByVal lngnpts As Long) Public Declare Sub drop Lib "s2drv32s.dll" Alias "_ drop@0" () Public Declare Sub dropall Lib "s2drv32s.dll" Alias "_dropall@0" () Public Declare Sub dupn Lib "s2drv32s.dll" Alias "_ dupn@4" (ByVal lngn As Long) Public Declare Sub dama2disk16 Lib "s2drv32s.dll" A lias "_dama2disk16@12" (ByVal lngbid As Long, ByRef bytfname As Byte, ByVal lngca tflag As Long) Public Declare Sub disk2dama16 Lib "s2drv32s.dll" A lias "_disk2dama16@12" (ByVal lngbid As Long, ByRef bytfname As Byte, ByVal lngse ekpos As Long) Public Declare Sub deallot Lib "s2drv32s.dll" Alias "_deallot@4" (ByVal lngbid As Long) Public Declare Sub divide Lib "s2drv32s.dll" Alias "_divide@0" () Public Declare Sub dplay Lib "s2drv32s.dll" Alias _dplay@8" (ByVal lngdbn1 As Long, ByVal lngdbn2 As Long) Public Declare Sub drecord Lib "s2drv32s.dll" Alias "_drecord@8" (ByVal lngdbn1 As Long, ByVal lngdbn2 As Long) Public Declare Sub decimate Lib "s2drv32s.dll" Alia s "_decimate@4" (ByVal lngfact As Long) Public Declare Sub extract Lib "s2drv32s.dll" Alias "_extract@0" () Public Declare Sub fill Lib "s2drv32s.dll" Alias "_ fill@8" (ByVal sngstart As Single, ByVal sngstep As Single) Public Declare Sub flat Lib "s2drv32s.dll" Alias "_ flat@0" () Public Declare Function freewords Lib "s2drv32s.dll Alias "_freewords@0" () As Long Public Declare Sub fir Lib "s2drv32s.dll" Alias "_f ir@0" () Public Declare Sub fastrecord Lib "s2drv32s.dll" Al ias "_fastrecord@4" (ByVal lngdbn As Long) Public Declare Sub foldnadd Lib "s2drv32s.dll" Alia s "_foldnadd@4" (ByVal lngartflag As Long) Public Declare Function getS2err Lib "s2drv32s.dll" Alias "_getS2err@4" (ByRef byterr As Byte) As Long Public Declare Function getS2primary Lib "s2drv32s. dll" Alias "_getS2primary@0" () As Long Public Declare Function getAPlockstatus Lib "s2drv3 2s.dll" Alias "_getAPlockstatus@0" () As Long Public Declare Function getXBlockstatus Lib "s2drv3 2s.dll" Alias "_getXBlockstatus@0" () As Long Public Declare Function getaddr Lib "s2drv32s.dll" Alias "_getaddr@4" (ByVal lngbid As Long) As Long Public Declare Sub gauss Lib "s2drv32s.dll" Alias _gauss@0" () Public Declare Function getnarts Lib "s2drv32s.dll" Alias "_getnarts@0" () As Long Public Declare Sub hann Lib "s2drv32s.dll" Alias "_ hann@0" () Public Declare Sub hamm Lib "s2drv32s.dll" Alias "_ hamm@0" () Public Declare Function hiblock Lib "s2drv32s.dll" Alias "_hiblock@0" () As Long Public Declare Sub inv Lib "s2drv32s.dll" Alias "_i nv@0" () Public Declare Sub iir Lib "s2drv32s.dll" Alias "_i ir@0" () Public Declare Sub interpol Lib "s2drv32s.dll" Alia s "_interpol@4" (ByVal lngfact As Long) Public Declare Sub logten Lib "s2drv32s.dll" Alias "_logten@0" () Public Declare Sub loge Lib "s2drv32s.dll" Alias "_ loge@0" ()

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182 Public Declare Sub logn Lib "s2drv32s.dll" Alias "_ logn@4" (ByVal sngbase As Single) Public Declare Function lowblock Lib "s2drv32s.dll" Alias "_lowblock@0" () As Long Public Declare Sub makedama16 Lib "s2drv32s.dll" Al ias "_makedama16@12" (ByVal lngbid As Long, ByVal lngind As Long, ByVal lngv As Long) Public Declare Sub makedamaf Lib "s2drv32s.dll" Ali as "_makedamaf@12" (ByVal lngbid As Long, ByVal lngind As Long, ByVal sngv As Single) Public Declare Sub make Lib "s2drv32s.dll" Alias "_ make@8" (ByVal lngind As Long, ByVal sngv As Single) Public Declare Sub mult Lib "s2drv32s.dll" Alias "_ mult@0" () Public Declare Sub maxlim Lib "s2drv32s.dll" Alias "_maxlim@4" (ByVal sngmax As Single) Public Declare Sub minlim Lib "s2drv32s.dll" Alias "_minlim@4" (ByVal sngmin As Single) Public Declare Sub maglim Lib "s2drv32s.dll" Alias "_maglim@4" (ByVal sngmax As Single) Public Declare Function maxval Lib "s2drv32s.dll" A lias "_maxval@0" () As Single Public Declare Function minval Lib "s2drv32s.dll" A lias "_minval@0" () As Single Public Declare Function maxmag Lib "s2drv32s.dll" A lias "_maxmag@0" () As Single Public Declare Function maxval_ Lib "s2drv32s.dll" Alias "_maxval_@0" () As Long Public Declare Function minval_ Lib "s2drv32s.dll" Alias "_minval_@0" () As Long Public Declare Function maxmag_ Lib "s2drv32s.dll" Alias "_maxmag_@0" () As Long Public Declare Sub mrecord Lib "s2drv32s.dll" Alias "_mrecord@4" (ByVal lngdbn As Long) Public Declare Sub mplay Lib "s2drv32s.dll" Alias _mplay@4" (ByVal lngdbn As Long) Public Declare Sub noblock Lib "s2drv32s.dll" Alias "_noblock@0" () Public Declare Sub optest Lib "s2drv32s.dll" Alias "_optest@0" () Public Declare Sub push16 Lib "s2drv32s.dll" Alias "_push16@8" (ByRef intbuf As Integer, ByVal lngnpts As Long) Public Declare Sub pushf Lib "s2drv32s.dll" Alias _pushf@8" (ByRef sngbuf As Single, ByVal lngnpts As Long) Public Declare Sub pop16 Lib "s2drv32s.dll" Alias _pop16@4" (ByRef intbuf As Integer) Public Declare Sub popf Lib "s2drv32s.dll" Alias "_ popf@4" (ByRef sngbuf As Single) Public Declare Sub popdisk16 Lib "s2drv32s.dll" Ali as "_popdisk16@4" (ByRef bytfname As Byte) Public Declare Sub popdiskf Lib "s2drv32s.dll" Alia s "_popdiskf@4" (ByRef bytfname As Byte) Public Declare Sub popdiska Lib "s2drv32s.dll" Alia s "_popdiska@4" (ByRef bytfname As Byte) Public Declare Sub pushdisk16 Lib "s2drv32s.dll" Al ias "_pushdisk16@4" (ByRef bytfname As Byte) Public Declare Sub pushdiskf Lib "s2drv32s.dll" Ali as "_pushdiskf@4" (ByRef bytfname As Byte) Public Declare Sub pushdiska Lib "s2drv32s.dll" Ali as "_pushdiska@4" (ByRef bytfname As Byte) Public Declare Sub parse Lib "s2drv32s.dll" Alias _parse@4" (ByRef byts As Byte) Public Declare Sub polar Lib "s2drv32s.dll" Alias _polar@0" () Public Declare Sub power Lib "s2drv32s.dll" Alias _power@4" (ByVal sngpw As Single) Public Declare Sub play Lib "s2drv32s.dll" Alias "_ play@4" (ByVal lngdbn As Long) Public Declare Function playseg Lib "s2drv32s.dll" Alias "_playseg@4" (ByVal lngchan As Long) As Long

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183 Public Declare Function playcount Lib "s2drv32s.dll Alias "_playcount@4" (ByVal lngchan As Long) As Long Public Declare Sub pfireone Lib "s2drv32s.dll" Alia s "_pfireone@4" (ByVal lngdbn As Long) Public Declare Sub pfireall Lib "s2drv32s.dll" Alia s "_pfireall@0" () Public Declare Function ppausestat Lib "s2drv32s.dl l" Alias "_ppausestat@4" (ByVal lngdbn As Long) As Long Public Declare Sub plotmap Lib "s2drv32s.dll" Alias "_plotmap@16" (ByVal lngxx1 As Long, ByVal lngyy1 As Long, ByVal lngxx2 As Long, B yVal lngyy2 As Long) Public Declare Sub plotwith Lib "s2drv32s.dll" Alia s "_plotwith@24" (ByVal lngxx1 As Long, ByVal lngyy1 As Long, ByVal lngxx2 As Long, B yVal lngyy2 As Long, ByVal sngymin As Single, ByVal sngymax As Single) Public Declare Sub plotwithCS Lib "s2drv32s.dll" Al ias "_plotwithCS@24" (ByVal lngxx1 As Long, ByVal lngyy1 As Long, ByVal lngxx2 As Long ByVal lngyy2 As Long, ByVal sngymin As Single, ByVal sngymax As Single) Public Declare Sub qdup Lib "s2drv32s.dll" Alias "_ qdup@0" () Public Declare Sub qpopf Lib "s2drv32s.dll" Alias _qpopf@4" (ByVal lngbid As Long) Public Declare Sub qpushf Lib "s2drv32s.dll" Alias "_qpushf@4" (ByVal lngbid As Long) Public Declare Sub qpop16 Lib "s2drv32s.dll" Alias "_qpop16@4" (ByVal lngbid As Long) Public Declare Sub qpush16 Lib "s2drv32s.dll" Alias "_qpush16@4" (ByVal lngbid As Long) Public Declare Sub qpushpart16 Lib "s2drv32s.dll" A lias "_qpushpart16@12" (ByVal lngbid As Long, ByVal lngspos As Long, ByVal lngnpt s As Long) Public Declare Sub qpushpartf Lib "s2drv32s.dll" Al ias "_qpushpartf@12" (ByVal lngbid As Long, ByVal lngspos As Long, ByVal lngnpts As Lo ng) Public Declare Sub qpoppart16 Lib "s2drv32s.dll" Al ias "_qpoppart16@8" (ByVal lngbid As Long, ByVal lngspos As Long) Public Declare Sub qpoppartf Lib "s2drv32s.dll" Ali as "_qpoppartf@8" (ByVal lngbid As Long, ByVal lngspos As Long) Public Declare Sub qrand Lib "s2drv32s.dll" Alias _qrand@0" () Public Declare Sub qwind Lib "s2drv32s.dll" Alias _qwind@8" (ByVal sngtrf As Single, ByVal sngsr As Single) Public Declare Sub reduce Lib "s2drv32s.dll" Alias "_reduce@0" () Public Declare Sub rect Lib "s2drv32s.dll" Alias "_ rect@0" () Public Declare Sub radd Lib "s2drv32s.dll" Alias "_ radd@0" () Public Declare Sub rfft Lib "s2drv32s.dll" Alias "_ rfft@0" () Public Declare Sub rift Lib "s2drv32s.dll" Alias "_ rift@0" () Public Declare Sub reverse Lib "s2drv32s.dll" Alias "_reverse@0" () Public Declare Sub record Lib "s2drv32s.dll" Alias "_record@4" (ByVal lngdbn As Long) Public Declare Function recseg Lib "s2drv32s.dll" A lias "_recseg@4" (ByVal lngchan As Long) As Long Public Declare Function reccount Lib "s2drv32s.dll" Alias "_reccount@4" (ByVal lngchan As Long) As Long Public Declare Sub swap Lib "s2drv32s.dll" Alias "_ swap@0" () Public Declare Sub setaddr Lib "s2drv32s.dll" Alias "_setaddr@8" (ByVal lngbid As Long, ByVal lngaddr As Long) Public Declare Sub seed Lib "s2drv32s.dll" Alias "_ seed@4" (ByVal lngsval As Long) Public Declare Sub shuf Lib "s2drv32s.dll" Alias "_ shuf@0" () Public Declare Sub split Lib "s2drv32s.dll" Alias _split@0" ()

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184 Public Declare Sub qscale Lib "s2drv32s.dll" Alias "_scale@4" (ByVal sngsf As Single) Public Declare Sub shift Lib "s2drv32s.dll" Alias _shift@4" (ByVal sngsf As Single) Public Declare Sub subtract Lib "s2drv32s.dll" Alia s "_subtract@0" () Public Declare Sub sqroot Lib "s2drv32s.dll" Alias "_sqroot@0" () Public Declare Sub square Lib "s2drv32s.dll" Alias "_square@0" () Public Declare Sub seperate Lib "s2drv32s.dll" Alia s "_seperate@0" () Public Declare Sub sine Lib "s2drv32s.dll" Alias "_ sine@0" () Public Declare Function sum Lib "s2drv32s.dll" Alia s "_sum@0" () As Single Public Declare Function stackdepth Lib "s2drv32s.dl l" Alias "_stackdepth@0" () As Long Public Declare Sub seqplay Lib "s2drv32s.dll" Alias "_seqplay@4" (ByVal lngdbn As Long) Public Declare Sub seqrecord Lib "s2drv32s.dll" Ali as "_seqrecord@4" (ByVal lngdbn As Long) Public Declare Sub trash Lib "s2drv32s.dll" Alias _trash@0" () Public Declare Sub totop Lib "s2drv32s.dll" Alias _totop@4" (ByVal lngsn As Long) Public Declare Sub tone Lib "s2drv32s.dll" Alias "_ tone@8" (ByVal sngf As Single, ByVal sngsr As Single) Public Declare Sub tangent Lib "s2drv32s.dll" Alias "_tangent@0" () Public Declare Function topsize Lib "s2drv32s.dll" Alias "_topsize@0" () As Long Public Declare Function tsize Lib "s2drv32s.dll" Al ias "_tsize@4" (ByVal lngbufn As Long) As Long Public Declare Sub usercall Lib "s2drv32s.dll" Alia s "_usercall@12" (ByVal lngcid As Long, ByVal sngargf As Single, ByVal lngarg24 As Lo ng) Public Declare Function userfunc Lib "s2drv32s.dll" Alias "_userfunc@12" (ByVal lngcid As Long, ByVal sngargf As Single, ByVal lngarg24 As Long) As Single Public Declare Sub value Lib "s2drv32s.dll" Alias _value@4" (ByVal sngv As Single) Public Declare Function whatis Lib "s2drv32s.dll" A lias "_whatis@4" (ByVal lngind As Long) As Single Public Declare Sub xreal Lib "s2drv32s.dll" Alias _xreal@0" () Public Declare Sub ximag Lib "s2drv32s.dll" Alias _ximag@0" () Public Const AD1_CODE = 17 Public Const AD2_CODE = 20 Public Const AD3_CODE = 21 Public Const qANY = 5 Public Const ALL = 15 Public Const ADC1 = 4 Public Const ADC2 = 8 Public Const ADC3 = 16 Public Const ADC4 = 32 Public Const AUTOSLEW = 0 Public Const ADCEXP = 14 Public Const ADC_BASE = 2064 Public Const ADC_IND = 1 Public Const BIQ16 = 4 Public Const BIQ32 = 5 Public Const CG1_CODE = 3 Public Const COS2 = 1 Public Const COS4 = 2 Public Const COS6 = 3 Public Const COMPUTER = 0

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185 Public Const CONTIN_REPS = 0 Public Const COMMON = 0 Public Const COEFEXP = 9 Public Const COEF_BASE = 18928 Public Const COEF_IND = 512 Public Const CT_LEFT = 1 Public Const CT_RIGHT = 2 Public Const CT_STEREO = 3 Public Const CT_MONSTER = 4 Public Const DB4_CODE = 27 Public Const DA1_CODE = 16 Public Const DD1_CODE = 18 Public Const DA2_CODE = 19 Public Const DA3_CODE = 22 Public Const DUAL_4_1 = 1 Public Const DUALDAC = 3 Public Const DAC1 = 1 Public Const DAC2 = 2 Public Const DUALADC = 12 Public Const DAC3 = 4 Public Const DAC4 = 8 Public Const DAC5 = 16 Public Const DAC6 = 32 Public Const DAC7 = 64 Public Const DAC8 = 128 Public Const DSPIDEXP = 2 Public Const DSPINEXP = 3 Public Const DSPINLEXP = 4 Public Const DSPINREXP = 5 Public Const DSPOUTEXP = 6 Public Const DSPOUTLEXP = 7 Public Const DSPOUTREXP = 8 Public Const DELINEXP = 10 Public Const DELOUTEXP = 11 Public Const DACEXP = 13 Public Const DSPID_BASE = 0 Public Const DSPID_IND = 1 Public Const DSPINL_BASE = 18920 Public Const DSPINL_IND = 512 Public Const DSPINR_BASE = 18888 Public Const DSPINR_IND = 512 Public Const DSPOUTL_BASE = 18880 Public Const DSPOUTL_IND = 512 Public Const DSPOUTR_BASE = 18884 Public Const DSPOUTR_IND = 512 Public Const DELOUT_BASE = 1024 Public Const DELOUT_IND1 = 32 Public Const DELOUT_IND2 = 1 Public Const DELIN_BASE = 1152 Public Const DELIN_IND = 1

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186 Public Const DAC_BASE = 2048 Public Const DAC_IND = 1 Public Const DAMA_16 = 4 Public Const DAMA_F = 5 Public Const ET1_CODE = 5 Public Const EXT = 5 Public Const EXCLUSIVE = 1 Public Const EXTERNAL = 2 Public Const FALL = 2 Public Const FREE_RUN = 5 Public Const FALLING = 3 Public Const FIR16 = 1 Public Const FIR32 = 2 Public Const F_HP = 0 Public Const F_LP = 1 Public Const F_NT = 2 Public Const FP_Kp = 8 Public Const FP_Ki = 4 Public Const FP_Kd = 2 Public Const FP_Ilim = 1 Public Const FASTDAC = 16 Public Const FASTDAC3 = 0 Public Const FILE_16 = 1 Public Const FILE_F = 2 Public Const FILE_A = 3 Public Const qGAUSS = 1 Public Const GSYNC = 32764 Public Const HTI_CODE = 26 Public Const HEADSIZE = 1024 Public Const IIR32 = 3 Public Const INP1 = 1 Public Const INP2 = 2 Public Const INP3 = 3 Public Const INP4 = 4 Public Const INP5 = 5 Public Const INP6 = 6 Public Const INP7 = 7 Public Const INP8 = 8 Public Const INTERNAL = 1 Public Const IBEXP = 15 Public Const IREGEXP = 17 Public Const IB_BASE = 0 Public Const IB_IND = 1 Public Const IREG_BASE = 480 Public Const IREG_IND = 1 Public Const INIT_PRIMARY = 1 Public Const INIT_SECONDARY = 2 Public Const INIT_EITHER = 3 Public Const INIT_FORCEPRIM = 4 Public Const LAST = 3

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187 Public Const LSYNC = 32766 Public Const MC1_CODE = 28 Public Const MANUAL = 0 Public Const MUD_GAIN = 2 Public Const MUD_HP = 3 Public Const MUD_LP = 4 Public Const MUD_NT = 5 Public Const MUD_IT = 6 Public Const MUD_ALL = 15 Public Const MONO = 1 Public Const MONSTER = 3 Public Const NEG_EDGE = 2 Public Const NEG_ENABLE = 4 Public Const NONE = 5 Public Const NEG = 1 Public Const ONOFF = 0 Public Const OFF = 0 Public Const qON = 1 Public Const OUTA = 0 Public Const OUTB = 1 Public Const OUTC = 2 Public Const OUTD = 3 Public Const OBEXP = 16 Public Const OB_BASE = 0 Public Const OB_IND = 1 Public Const PA4_CODE = 1 Public Const PI1_CODE = 6 Public Const PF1_CODE = 9 Public Const PI2_CODE = 11 Public Const PM1_CODE = 15 Public Const PD1_CODE = 23 Public Const PEAK = 3 Public Const POS_EDGE = 1 Public Const POS_ENABLE = 3 Public Const PM1_STEREO = 0 Public Const PM1_MONO = 1 Public Const P_AZ = 1 Public Const P_EL = 2 Public Const P_ROLL = 3 Public Const P_X = 4 Public Const P_Y = 5 Public Const P_Z = 6 Public Const POS = 0 Public Const PC_VEL = 1# Public Const PC_ACC = 2# Public Const PC_GEAR = 3# Public Const PC_MINP = 4# Public Const PC_MAXP = 5# Public Const PC_HOME = 6# Public Const PC_REFMODE = 7#

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188 Public Const PC_SRCHVEL = 8# Public Const PC_REFPOS = 9# Public Const PC_Kp = 10# Public Const PC_Ki = 11# Public Const PC_Kd = 12# Public Const PC_Ilim = 13# Public Const QUAD_2_1 = 0 Public Const RAMP = 4 Public Const RAMP2 = 5 Public Const RAMP4 = 6 Public Const RAMP6 = 7 Public Const RISE = 1 Public Const RISING = 2 Public Const RM_MANUAL = 1# Public Const RM_REFSWITCH = 2# Public Const SW2_CODE = 2 Public Const SD1_CODE = 4 Public Const SS1_CODE = 14 Public Const qSINE = 3 Public Const SING_8_1 = 2 Public Const SN1 = 1 Public Const SN2 = 2 Public Const SN3 = 3 Public Const SN4 = 4 Public Const SN5 = 5 Public Const SN6 = 6 Public Const SN7 = 7 Public Const SN8 = 8 Public Const SN9 = 9 Public Const SN10 = 10 Public Const SN11 = 11 Public Const SN12 = 12 Public Const SN13 = 13 Public Const SN14 = 14 Public Const SN15 = 15 Public Const SN16 = 16 Public Const STEREO = 2 Public Const SYNC_ALL = 17912 Public Const STACK = 6 Public Const TG6_CODE = 10 Public Const TRIGGED_REPS = 1 Public Const TAPEXP = 12 Public Const TAP_BASE = 1280 Public Const TAP_IND1 = 32 Public Const TAP_IND2 = 1 Public Const UI1_CODE = 7 Public Const UNIFORM = 2 Public Const VALLEY = 4 Public Const VEXP = 1 Public Const WG1_CODE = 8

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189 Public Const WG2_CODE = 12 Public Const WAVE = 4 Public Const XB1_CODE = 0 Public Const XXX_CODE = 13 Public Const XTRG1 = 1 Public Const XTRG2 = 2 Public Const XCLK1 = 3 Public Const XCLK2 = 4 Public Const XMUX = 1 Public Const UB_0DB = 1 Public Const UB_6DB = 2 Public Const UB_12DB = 3 Public Const UB_18DB = 4 Public Const UB_24DB = 5 Public Const UB_100ns = 0 Public Const UB_1us = 1 Public Const UB_10us = 2 Public Const UB_100us = 3 Public Const UB_1ms = 4 Public Const UB_EXT = 7 Public Const UB_CH1 = 0 Public Const UB_CH2 = 1 Public Const UB_CH3 = 2 Public Const UB_CH4 = 3 Public Const UB_CHALL = 10 Public Const UB_START = 32767 Public Const UB_STOP = 32765 Public Const x1 = 0 Public Const x2 = 1 Public Const x4 = 2 Public Const x8 = 3 Public Const x16 = 4 Public Const x32 = 5 Public Const x64 = 6 Public Const x128 = 7 Module 3 ForwardBackward Declarations (Similar prog ramming was used for Masker thresholds and Gap Detection) 'Variables Public Deltat As Double Public ISI As Double Public StartAttn As Integer Public VariableAttn As Integer Public Unatten As Double Public Masker1Dur As Double Public Masker2Dur As Double Public Masker3Dur As Double Public Masker4Dur As Double Public SigDur As Double

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190 Public Masker1Delay As Double Public Masker2Delay As Double Public Masker3Delay As Double Public Masker4Delay As Double Public TotalDur As Double Public SigDelay As Double Public Masker1Amplitude As Double Public Masker2Amplitude As Double Public Masker3Amplitude As Double Public Masker4Amplitude As Double Public SignalAmplitude As Double Public Masker1Freq As String Public Masker2Freq As String Public Masker3Freq As String Public Masker4Freq As String Public HighCutoff1 As Integer Public LowCutoff1 As Integer Public HighCutoff2 As Integer Public LowCutoff2 As Integer Public HighCutoff3 As Integer Public LowCutoff3 As Integer Public HighCutoff4 As Integer Public LowCutoff4 As Integer Public HighCutoffSig As Integer Public LowCutoffSig As Integer Public Interval2Add As Double Public GeoMean As Double Public DLPercent As Double Public SD As Double Public FrequencyDifference As Double Public ReversalResults(10) As Double Public FreqDiffSquare As Double Public SDSum As Double Public RP2Filename As String Public StandardFrequency1 As Double Public SignalFrequency1 As Double Public Freq(100) As Double Public FreqDiff(100) As Double Public Task As String Public SimCond As String Public Dur As Integer Public StartingFrequency As Double Public NoteFrequency As Double Public Choice As Integer Public RightWrong As Integer Public Responses(100) As Integer

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191 Public Reversals(100) As Integer Public Trial As Double Public Bumptop As Integer Public Bumpbot As Integer Public Signal As Integer Public SubName As String Public Thresh As Integer Public Condition As String Public Decision As Integer Public I As Long Public J As Integer Public NumReversals As Integer Public Lastlevel1 As Integer Public Lastlevel2 As Integer Public rcheck As Integer Public NumReversal As Integer Public ExitFlagB As Integer Public ExitFlagR As Integer Public ExitFlagE As Integer Public mclick As Integer Public RandNum As Integer Public Srate As Single Public Slope As Integer Public Junk As Long Public Amp1 As Double Public Amp2 As Double Public ConditionOrder As Integer Public ConditionCounter As Integer Public CenterFreqM1 As Integer Public CenterFreqM2 As Integer Public GapNeuroCode As Integer Public NeuroCode As Integer Public StimulusFile As String Public Attn(200) As Double Public VarAttn As Double Public Session As String Public M1Dur As Double Public M2Dur As Double Public M3Dur As Double

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192 Public M4Dur As Double Public M2DurStart As Double Public ISInterval As Integer Public Number As Double Public M1GapDur As Double Public M1M2GapDur As Double Public AttnMult As Double Public K As Integer Public alreadyused As Integer Public GapNum As Integer Public GapDurArray(11) As Integer Public RandNumArray(11) As Integer Public RandCtr As Integer Public RandNumString As String 'Public Srate As Single 'Public Slope As Integer 'Public Junk As Long Sub delay(secs!) Dim Start! Start! = Timer While (Timer < (Start! + secs!)) DoEvents Wend End Sub Function GetRandom(range%) Randomize GetRandom = Int((range%) Rnd) + 1 End Function Visual Basic Programming Masker Threshold Form Details Dim secs As Single Dim Start As Single Dim interval As Integer Sub CmdRun_Click()

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193 Dim e1 As Long 'generic variable for error checks Dim data(0 To 4999) As Single buffer for saving d ata Srate = 20 Call GetRunInfo Call InitAdaptive Call PA5x1.ConnectPA5("USB", 1) 'If PA5x1.ConnectPA5("USB", 1) Then 'MsgBox ("Connection established") 'Else 'MsgBox "Unable to connect" 'End If Dim ErrMess As String ErrMess = PA5x1.GetError If Len(ErrMess) > 0 Then MsgBox ErrMess End If Call PA5x1.SetAtten(StartAttn) 'error1 = PA5x1.GetError 'If error1 = "" Then 'MsgBox "Attenuation set correctly" 'Else 'MsgBox error1 'End If Call RPcoX1.ConnectRP2("USB", 1) Call RPcoX1.LoadCOF(RP2Filename) Call RPcoX1.Run If RPcoX1.GetStatus <> 7 Then MsgBox ("RP not running correctly") End End If e1 = RPcoX1.SetTagVal("HighCut1", HighCutOff) If e1 = 0 Then MsgBox ("error reading parameter1") End If e1 = RPcoX1.SetTagVal("LowCut1", LowCutOff) If e1 = 0 Then MsgBox ("error reading parameter2") End If e1 = RPcoX1.SetTagVal("SignalDuration", MaskSigDur)

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194 If e1 = 0 Then MsgBox ("error reading parameter3") End If e1 = RPcoX1.SetTagVal("SignalAmp", MaskSignalAmplit ude) If e1 = 0 Then MsgBox ("error reading parameter4") End If e1 = RPcoX1.SetTagVal("SigDelay", MaskSignalDelay) If e1 = 0 Then MsgBox ("error reading parameter5") End If FRMINTERVAL.Show Do mclick = 0 I = GetRandom(2) Signal = I FRMINTERVAL!Text1.Text = Str$(I) + Str$(VariableA ttn) 'set attenuation = VariableAttn If I = 2 Then FRMINTERVAL!INTERVAL1.Visible = True e1 = RPcoX1.SetTagVal("SigDelay", SigDe lay2) If e1 = 0 Then MsgBox ("error reading parameter") End If SigDelay2 = SigDelay + Interval2Add Call RPcoX1.SoftTrg(1) If Task = "MaskerThreshold" Then Call delay(0 .75) FRMINTERVAL!INTERVAL2.Visible = True 'Call RPcoX1.SoftTrg(1) End If If I = 1 Then FRMINTERVAL!INTERVAL1.Visible = True e1 = RPcoX1.SetTagVal("SigDelay", SigDelay) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) If Task = "MaskerThreshold" Then Call delay(0 .75) FRMINTERVAL!INTERVAL2.Visible = True

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195 'Call RPcoX1.SoftTrg(1) End If Do Until mclick = 1 'Pause program until get a res ponse DoEvents Loop FRMINTERVAL!INTERVAL1.Visible = False FRMINTERVAL!INTERVAL2.Visible = False RightWrong = Signal Choice Call Levitt Call delay(0.4) Trial = Trial + 1 Loop While ExitFlagR = 0 Call RPcoX1.Halt If ExitFlagR = 1 Then Call Finishup End If End Sub Sub CmdQuit_Click() End End Sub Sub Form_Load() 'Putting items in combo boxes CboStartAttn.AddItem "0" CboStartAttn.AddItem "20" CboStartAttn.AddItem "30" CboStartAttn.AddItem "40" CboStartAttn.AddItem "50" CboStartAttn.AddItem "60" CboStartAttn.AddItem "70" CboStartAttn.AddItem "80" CboStartAttn.AddItem "90" CboMaskerFreq.AddItem "Broad Band" CboTask.AddItem "MaskerThreshold" End Sub Sub InitAdaptive()

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196 'Initialize the adaptive variables Bumptop = 0 Bumpbot = 0 RightWrong = 0 Trial = 1 NumReversal = 0 Slope = 1 Decision = 1 GapSum = 0 GapThresh = 0 Signal = 0 Choice = 0 ExitFlagB = 0 ExitFlagR = 0 ExitFlagE = 0 'LeftStandard = 1 'RightStandard = 2 'LeftSignal = 3 'RightSignal = 4 SDSum = 0 FreqSum = 0 For I = 0 To 100 Responses(I) = 0 Reversals(I) = 0 Next I For I = 0 To 1 Attn(I) = StartAttn Next I End Sub Public Sub Levitt() If RightWrong <> 0 Then incorrect answer Responses(Trial) = RightWrong If NumReversal <= 4 Then Attn(Trial + 1) = Attn(Trial) 4 VariableAttn = VariableAttn 4 End If If NumReversal > 4 Then Attn(Trial + 1) = Attn(Trial) 2 VariableAttn = VariableAttn 2 End If Call Bumpcheck Call Reversecheck Else 'correct answer

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197 Responses(Trial) = RightWrong Lastlevel = Attn(Trial) Attn(Trial 1) If Lastlevel <> 0 Then Attn(Trial + 1) = Attn(T rial) If Lastlevel = 0 Then If NumReversal <= 4 Then Attn(Trial + 1) = Attn(Trial) + 4 VariableAttn = VariableAttn + 4 End If If NumReversal > 4 Then Attn(Trial + 1) = Attn(Trial) + 2 VariableAttn = VariableAttn + 2 End If End If Call Bumpcheck Call Reversecheck End If Call PA5x1.SetAtten(VariableAttn) End Sub Public Sub Bumpcheck() If Attn(Trial + 1) < 0 Then Attn(Trial + 1) = 0 Bumptop = Bumptop + 1 If Bumptop > 4 Then MsgBox "Hitting Top", 48, "Fulton T emporal Masking Program" ExitFlagB = 1 End If End If If Attn(Trial + 1) > 119 Then Attn(Trial + 1) = 119 Bumpbot = Bumpbot + 1 If Bumpbot > 4 Then MsgBox "Hitting Bottom", 48, "Fulto n Temporal Masking Program" ExitFlagB = 1 End If End If End Sub Public Sub Reversecheck() rcheck = (Attn(Trial + 1) Attn(Trial)) Slope If rcheck < 0 Then NumReversal = NumReversal + 1 Reversals(NumReversal) = Trial Slope = -1 Slope End If

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198 If NumReversal < 10 Then ExitFlagR = 0 End If If NumReversal >= 10 Then ExitFlagR = 1 End If End Sub Public Sub Finishup() Unload FRMINTERVAL For I = 1 To 10 WhichAttn = Reversals(I) ReversalResults(I) = Attn(WhichAttn) Next I For I = 5 To 10 WhichAttn = Reversals(I) AttnSum = AttnSum + Attn(WhichAttn) Next I FinalAttn = AttnSum / 6 For I = 5 To 10 WhichAttn = Reversals(I) StdDevSum = StdDevSum + (Attn(WhichAttn) FinalAttn) (Attn(WhichAttn) FinalAttn) StdDev = Sqr(StdDevSum) / 6 Next I AttnMult = 1 For I = 3 To 10 WhichAttn = Reversals(I) AttnMult = AttnMult Attn(WhichAttn) If AttnMult = 0 Then AttnMult = 1 Next I GMean = (AttnMult ^ 0.125) Threshold = Unatten FinalAttn GeoMean = Unatten GMean 'SD = ((SDSum / 6) ^ 0.5) FrmResults.Show FrmResults!TxtResultsName.Text = SubName FrmResults!TxtResultsTask.Text = MaskerThreshol d

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199 FrmResults!TxtResultsStartAttn.Text = StartAttn FrmResults!TxtResultsEndAttn.Text = VariableAtt n FrmResults!TxtResultsMaskerFreq.Text = Task FrmResults!TxtResultsThresh.Text = Threshold FrmResults!TxtResultsGeoMean.Text = GeoMean FrmResults!TxtResultsSD.Text = StdDev FrmResults!DateLabel.Caption = Date & " & Tim e 'Format(Now, "ddddd") FrmResults!TxtReversal1.Text = ReversalResults( 1) FrmResults!TxtReversal2.Text = ReversalResults( 2) FrmResults!TxtReversal3.Text = ReversalResults( 3) FrmResults!TxtReversal4.Text = ReversalResults( 4) FrmResults!TxtReversal5.Text = ReversalResults( 5) FrmResults!TxtReversal6.Text = ReversalResults( 6) FrmResults!TxtReversal7.Text = ReversalResults( 7) FrmResults!TxtReversal8.Text = ReversalResults( 8) FrmResults!TxtReversal9.Text = ReversalResults( 9) FrmResults!TxtReversal10.Text = ReversalResults (10) End Sub Public Sub GetRunInfo() SubName = TxtName.Text StartAttn = Val(CboStartAttn.Text) Task = CboTask.Text MaskerFreq = CboMaskerFreq.Text MaskSigDur = 250 MaskSignalAmplitude = 0.1 MaskSignalDelay = 1 Interval2Add = 750 SigDelay2 = MaskSignalDelay + Interval2Add If MaskerFreq = "Broad Band" Then HighCutOff = 5500 LowCutOff = 20 End If If Task = "MaskerThreshold" Then MaskSigDur = 250 MaskSigDelay = 1 End If I = 1 J = 1 ExitFlag = 0 If Task = "MaskerThreshold" Then RP2Filename = "I:\psycholab\Fulton\MaskerThreshold\MaskerThreshol d"

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200 Unatten = 78.8 VariableAttn = StartAttn End Sub Visual Basic Progamming for Gap Detection Task Form Details Dim secs As Single Dim Start As Single Dim interval As Integer Sub CmdRun_Click() Dim e1 As Long Dim data(0 To 4999) As Single Srate = 20 Call GetDuration Call GetRunInfo Call InitAdaptive Call PA5x1.ConnectPA5("USB", 1) Dim ErrMess As String ErrMess = PA5x1.GetError If Len(ErrMess) > 0 Then MsgBox ErrMess End If Call RPcoX1.ConnectRP2("USB", 1) Call RPcoX1.LoadCOF(RP2Filename) Call RPcoX1.Run If RPcoX1.GetStatus <> 7 Then MsgBox ("RP not running correctly") End End If e1 = RPcoX1.SetTagVal("M1Duration", Marker1Dur) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("M2Duration", Marker2Dur) If e1 = 0 Then MsgBox ("error reading parameter") End If

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201 'e1 = RPcoX1.SetTagVal("M1GapDuration", Marker2 Delay) 'If e1 = 0 Then 'MsgBox ("error reading parameter") 'End If e1 = RPcoX1.SetTagVal("Marker1Amp", Marker1Ampl itude) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("Marker2Amp", Marker2Ampl itude) If e1 = 0 Then MsgBox ("error reading parameter") End If 'e1 = RPcoX1.SetTagVal("HighCut1", HighCutoff1) 'If e1 = 0 Then 'MsgBox ("error reading parameter") 'End If 'e1 = RPcoX1.SetTagVal("HighCut2", HighCutoff2) 'If e1 = 0 Then 'MsgBox ("error reading parameter") 'End If 'e1 = RPcoX1.SetTagVal("LowCut1", LowCutoff1) 'If e1 = 0 Then 'MsgBox ("error reading parameter") 'End If 'e1 = RPcoX1.SetTagVal("LowCut2", LowCutoff2) 'If e1 = 0 Then 'MsgBox ("error reading parameter") 'End If 'e1 = RPcoX1.SetTagVal("TotalDuration", TotalDu r) 'If e1 = 0 Then 'MsgBox ("error reading parameter") 'End If FRMINTERVAL.Show If Task = "Practice" Then Call Practice Do mclick = 0

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202 I = GetRandom(2) Signal = I FRMINTERVAL!Text1.Text = Str$(I) + Str$(gapsize) Call delay(1) If I = 2 Then FRMINTERVAL!INTERVAL1.Visible = True e1 = RPcoX1.SetTagVal("M1GapDuration", 251) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("TotalDuration", Standa rdTotalDur) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True e1 = RPcoX1.SetTagVal("TotalDuration", Target TotalDur) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("M1GapDuration", Target Marker2Delay) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) End If If I = 1 Then FRMINTERVAL!INTERVAL1.Visible = True e1 = RPcoX1.SetTagVal("TotalDuration", Target TotalDur) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("M1GapDuration", Target Marker2Delay) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True e1 = RPcoX1.SetTagVal("M1GapDuration", 251) If e1 = 0 Then MsgBox ("error reading parameter") End If

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203 e1 = RPcoX1.SetTagVal("TotalDuration", Standa rdTotalDur) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) End If Do Until mclick = 1 'Pause program until get a res ponse DoEvents Loop FRMINTERVAL!INTERVAL1.Visible = False FRMINTERVAL!INTERVAL2.Visible = False RightWrong = Signal Choice Call GetDuration Call Levitt Call delay(0.4) Trial = Trial + 1 Loop While ExitFlagR = 0 Call RPcoX1.Halt If ExitFlagR = 1 Then Call Finishup End If End Sub Sub CmdQuit_Click() End End Sub Sub Form_Load() 'Putting items in combo boxes CboStartGap.AddItem "5" CboStartGap.AddItem "10" CboStartGap.AddItem "15" CboStartGap.AddItem "20" CboM1Freq.AddItem "Broadband" CboM2Freq.AddItem "Broadband" ChoTask.AddItem "Gap Detection" ChoTask.AddItem "Practice"

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204 End Sub Sub InitAdaptive() 'Initialize the adaptive variables Bumptop = 0 Bumpbot = 0 RightWrong = 0 Trial = 1 NumReversal = 0 Slope = 1 Decision = 1 GapSum = 0 GapMult = 1 GapThresh = 0 GapGeoMean = 0 Signal = 0 Choice = 0 ExitFlagB = 0 ExitFlagR = 0 ExitFlagE = 0 'LeftStandard = 1 'RightStandard = 2 'LeftSignal = 3 'RightSignal = 4 SDSum = 0 FreqSum = 0 For I = 0 To 100 Responses(I) = 0 Reversals(I) = 0 Next I 'For I = 1 To 16 'RandArray(I) = 0 'Next I gapsize = StartGap For I = 0 To 1 Gaps(I) = StartGap Next I FileNumber = GetRandom(9) End Sub

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205 Public Sub Levitt() If RightWrong <> 0 Then incorrect answer Responses(Trial) = RightWrong Gaps(Trial + 1) = Gaps(Trial) 1.2 gapsize = gapsize 1.2 Call Bumpcheck Call Reversecheck Else Responses(Trial) = RightWrong LastLevel = Gaps(Trial) Gaps(Trial 1) If LastLevel <> 0 Then Gaps(Trial + 1) = Gaps(T rial) If LastLevel = 0 Then Gaps(Trial + 1) = Gaps(Trial) / 1.2 gapsize = gapsize / 1.2 End If Call Bumpcheck Call Reversecheck End If 'If gapsize > 15 Then gapsize = 15 GapNumber = gapsize '* 1000 GapNumbers(Trial) = GapNumber TargetMarker2Delay = Marker1Dur + gapsize TargetTotalDur = Marker1Dur + gapsize + Marker2Dur 'Interval2Add = Marker1Dur + gapsize + Marker2Dur + 250 End Sub Public Sub GetDuration() 'read numbers into array DurationArray(1) = 250 DurationArray(2) = 275 DurationArray(3) = 300 DurationArray(4) = 325 DurationArray(5) = 350 RandNum = 5 Marker1Dur = 250 DurationNumber = GetRandom(RandNum) Marker2Dur = DurationArray(DurationNumber) StandardTotalDur = Marker1Dur + 1 + Marker2Dur End Sub Public Sub Bumpcheck() If Gaps(Trial + 1) > 150 Then

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206 Bumptop = Bumptop + 1 If Bumptop > 3 Then MsgBox "Gap Size Too Large", 48, "T emporal Resolution" ExitFlagB = 1 End If End If If Gaps(Trial + 1) < 1 Then Gaps(Trial + 1) = 1 Bumpbot = Bumpbot + 1 If Bumpbot > 3 Then MsgBox "Gap Size Too Small", 48, "T emporal Resolution" ExitFlagB = 1 End If End If End Sub Public Sub Reversecheck() rcheck = (Gaps(Trial + 1) Gaps(Trial)) Slop e If rcheck < 0 Then NumReversal = NumReversal + 1 Reversals(NumReversal) = Trial Slope = -1 Slope End If If NumReversal < 8 Then ExitFlagR = 0 End If If NumReversal >= 8 Then ExitFlagR = 1 End If End Sub Public Sub Finishup() Unload FRMINTERVAL For I = 1 To 8 WhichGaps = Reversals(I) ReversalResults(I) = Gaps(WhichGaps) Next I For I = 3 To 8 J = Reversals(I) Gapms = GapNumbers(J) GapSum = GapSum + Gapms If Gapms = 0 Then Gapms = 1

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207 GapMult = GapMult Gapms Next I GapThresh = (GapSum / 6) GapGeoMean = (GapMult ^ 0.167) FrmResults.Show FrmResults!TxtResultsName.Text = SubName FrmResults!TxtResultsStartGap.Text = StartGap FrmResults!TxtResultsGapThresh.Text = GapThresh FrmResults!TxtResultsGapGeoMean.Text = GapGeoMe an FrmResults!DateLabel.Caption = Date & " & Tim e FrmResults!TxtReversal1.Text = ReversalResults( 1) FrmResults!TxtReversal2.Text = ReversalResults( 2) FrmResults!TxtReversal3.Text = ReversalResults( 3) FrmResults!TxtReversal4.Text = ReversalResults( 4) FrmResults!TxtReversal5.Text = ReversalResults( 5) FrmResults!TxtReversal6.Text = ReversalResults( 6) FrmResults!TxtReversal7.Text = ReversalResults( 7) FrmResults!TxtReversal8.Text = ReversalResults( 8) End Sub Public Sub GetRunInfo() SubName = TxtName.Text StartGap = Val(CboStartGap.Text) Task = ChoTask.Text 'Marker1Dur = 250 'Marker2Dur = 250 Marker1Amplitude = 0.1 Marker2Amplitude = 0.1 Marker1Freq = CboM1Freq.Text Marker2Freq = CboM2Freq.Text 'If Masker1Freq = "Broad Band" Then 'HighCutoff1 = 5500 'LowCutoff1 = 20 'End If 'If Masker2Freq = "Broad Band" Then 'HighCutoff2 = 5500 'LowCutoff2 = 20 'End If gapsize = StartGap 'If Task = "gap detection" Then

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208 StandardMarker2Delay = 251 TargetMarker2Delay = Marker1Dur + gapsize TargetTotalDur = Marker1Dur + gapsize + Marker2Dur 'Interval2Add = Marker1Dur + gapsize + Marker2Dur + 250 If Task = "Practice" Then Marker2Delay = Marker1Dur + 50 TotalDur = Marker1Dur + 50 + Marker2Dur Interval2Add = Marker1Dur + 50 + Marker2Dur + 250 End If I = 1 J = 1 ExitFlag = 0 If Task = "Gap Detection" Then RP2Filename = "I:\ps ycholab\Fulton\VB Gap Detection\FultonGapDetection.rco" If Task = "Practice" Then RP2Filename = "I:\psychol ab\Fulton\VB Gap Detection\FultonGapDetection.rco" End Sub Public Sub Practice() Do mclick = 0 I = GetRandom(2) If Task = "Practice" Then RP2Filename = "I:\psychol ab\Fulton\VB Gap Detection\FultonGapDetection.rco" Signal = I FRMINTERVAL!Text1.Text = Str$(I) + Str$(gapsize) Call delay(1) If I = 2 Then FRMINTERVAL!INTERVAL1.Visible = True M1DurTemp = Marker1Dur + 1 e1 = RPcoX1.SetTagVal("M1GapDuration", M1DurT emp) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("M2Duration", Marker2Du r) If e1 = 0 Then MsgBox ("error reading parameter") End If totaldurtemp = Marker1Dur + 1 + Marker2Dur

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209 e1 = RPcoX1.SetTagVal("TotalDuration", totald urtemp) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True M1DurTemp = Marker1Dur + 50 e1 = RPcoX1.SetTagVal("M1GapDuration", M1DurT emp) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("M2Duration", Marker2Du r) If e1 = 0 Then MsgBox ("error reading parameter") End If totaldurtemp = Marker1Dur + 50 + Marker2Dur e1 = RPcoX1.SetTagVal("TotalDuration", totald urtemp) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) End If If I = 1 Then FRMINTERVAL!INTERVAL1.Visible = True M1DurTemp = Marker1Dur + 50 e1 = RPcoX1.SetTagVal("M1GapDuration", M1DurT emp) If e1 = 0 Then MsgBox ("error reading parameter") End If e1 = RPcoX1.SetTagVal("M2Duration", Marker2Du r) If e1 = 0 Then MsgBox ("error reading parameter") End If totaldurtemp = Marker1Dur + 50 + Marker2Dur e1 = RPcoX1.SetTagVal("TotalDuration", totald urtemp) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) Call delay(1) FRMINTERVAL!INTERVAL2.Visible = True M1DurTemp = Marker1Dur + 1 e1 = RPcoX1.SetTagVal("M1GapDuration", M1DurT emp) If e1 = 0 Then MsgBox ("error reading parameter")

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210 End If e1 = RPcoX1.SetTagVal("M2Duration", Marker2Du r) If e1 = 0 Then MsgBox ("error reading parameter") End If totaldurtemp = Marker1Dur + 1 + Marker2Dur e1 = RPcoX1.SetTagVal("TotalDuration", totald urtemp) If e1 = 0 Then MsgBox ("error reading parameter") End If Call RPcoX1.SoftTrg(1) End If Do Until mclick = 1 'Pause program until get a res ponse DoEvents Loop FRMINTERVAL!INTERVAL1.Visible = False FRMINTERVAL!INTERVAL2.Visible = False RightWrong = Signal Choice Call GetDuration Call delay(0.4) Trial = Trial + 1 Loop While ExitFlagR = 0 Call RPcoX1.Halt If ExitFlagR = 1 Then Call Finishup End If End Sub

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211 Appendix E Example results form forTemporal masking tasks

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212 Appendix F Speech in interrupted noise task example score shee t 24-dB S/B12-dB S/B 0-dB S/B1pain 16hate 31gaze 2youth 17shack 32life 3wheat 18tool 33get 4dodge 19voice 34read 5cool 20rush 35bath 20-dB S/B 8-dB S/B 6ditch 21turn # Correct7ring 22young 8kick 23bite 9chair 24pick 10luck 25half 16-dB S/B 4-dB S/B 11base 26far 12wire 27learn 13red 28mood 14time 29talk 15 judge 30 note Threshold (50%) dB S/BTrack 25, List 1, Random 1

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About the Author Susan Fulton was born in Jacksonville, FL. She gr aduated from the University of South Florida with a Master of Scienc e degree in Audiology in 1987. Susan has more than 23 years of clinical audiologic al experience with adults and children. She currenly works as a visitng instruct or at the University of South Florida. Susan has also worked for 8 years as a cl inical audiologist for Pediatric Otolarynogology Associates.


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The effects of aging on temporal masking
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[Tampa, Fla] :
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Dissertation (PHD)--University of South Florida, 2010.
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ABSTRACT: The ability to resolve rapid intensity and frequency fluctuations in sound is important for understanding speech, especially in real-world environments that include background noise and reverberation. Older listeners often complain of difficulties understanding speech in such real-world environments. One factor thought to influence speech understanding in noisy and reverberant environments is temporal resolution, the ability to follow rapid acoustic changes over time. Temporal resolution is thought to help listeners resolve rapid acoustic changes in speech as well as use small glimpses of speech available in the dips or gaps in the background sounds. Temporal resolution is an ability that is known to deteriorate with age and hearing loss, negatively affecting the ability to understand speech in noisy real-world environments. Measures of temporal resolution, including temporal masking, gap detection, and speech in interrupted noise, use a silent gap as the cue of interest. Temporal masking and speech in interrupted noise tasks measure how well a listener resolves a stimulus before, after, or between sounds (i.e., within a silent gap), while gap detection tasks measure how well the listener resolves the timing of a silent gap itself. A listener needs to resolve information within the gap and the timing of the gap when listening to speech in background sounds. This study examined the role of aging and hearing loss on three measures of temporal resolution: temporal masking, gap detection, and speech understanding in interrupted noise. For all three measures, participants were young listeners with normal hearing (n = 8, mean age = 25.4 years) and older listeners with hearing loss (n = 9, mean age = 72.1 years). Results showed significant differences between listener groups for all three temporal measures. Specifically, older listeners with hearing loss had higher temporal masked thresholds, larger gap detection thresholds, and required a higher signal-to-noise ratio for speech understanding in interrupted noise. Relations between temporal tasks were observed. Temporal masked thresholds and gap detection thresholds accounted for a significant amount of the variance in speech-in-noise scores. Findings suggest that deficits in temporal resolution abilities may contribute to the speech-in-noise difficulties reported by older listeners.
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Advisor: Jennifer Lister, Ph.D.
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Temporal resolution
Gap detection
Speech in noise
Speech perception
Sensorineural hearing loss
Presbycusis
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