A Binaural Model of Monotic Level Discrimination

1. A Binaural Model of Monotic Level Discrimination Daniel E. Shub and H. Steven Colburn Boston University, Hearing Research Center Harvard-MIT, Health Science and Technology

2. Introduction • Monaural level discrimination can be degraded by the addition of a second ear [Rowland and Tobias JSHR 1967, Bernstein JASA 2005, and Shub and Colburn ARO 2004] • Traditional models cannot predict this degradation • Understanding this degradation might be important for bilateral hearing aids and cochlear implants

3. Outline • Psychophysical experiment • One-interval left-ear level discrimination task with a roving contralateral distractor • Extremely limited amount of published data on degradation from the other ear • Previous studies used multi-interval adaptive paradigms which increases model complexity • Predict results with a detection theoretic model based on binaural information

4. Psychophysical Experiment Distractor tone Roving level: 50-80 dB Roving phase: ± 90° Target tone Level: 50 or 58 dB Fixed phase: 0° • Task is to detect a level increment of a monaural target in the presence of a simultaneous but contra-aural distractor • Both target and distractor are 600 Hz tones with 300 ms duration • 1-interval, 2-alternative-forced-choice with feedback paradigm • Without distractor: traditional monotic level discrimination task

5. Stimulus Perception • Dominant perception: Single image with a salient loudness and position • Target level affects loudness and position • Distractor level affects loudness and position • Distractor phase affects position • Additional “fragile” images • Time image, image shape/width

6. Psychophysical Results Distractor Lags Leads Pf Pd • Overall Performance: • With distractor: 73% correct • No distractor: 97% correct (not shown) • Responded “Incremented” more with intense and lagging distractors

7. Detection Theoretic Model • Observe: • Analysis is currently limited to zero-mean Gaussian noise which is independent across the dimensions • Variances of the internal noise and value of trading ratio k are fit to previous level discrimination and lateralization experiments

8. Ideal Observer • Ideal (maximum likelihood) observer: • Achieves 99% correct discrimination performance • Decision rule is defined by a complex surface • Divides 3-D space into regions of “Incremented” and “Un-Incremented” • Small  and large  fall into the “Un-Incremented” region • For some Q and T, there are no values of  which fall into the “Incremented” region • ,  and  carry too much information •  and  do not carry sufficient information • Consider non-ideal observer of ,  and 

9. Non-Ideal Observer • Non-ideal observer modifies the ideal rule: • Responds “Incremented” for large , independent of  and  • Assumes subjects always respond “Incremented” whenever a “loud” stimulus is heard • Decision rule (criterion) is jittered (zero-mean Gaussian noise) to further decrease discrimination performance • Assumes subjects have difficulties implementing the multidimensional decision space; imposes cost for complex decisions • Non-ideal observer has four free parameters • Variances of the criterion jitter (sL, sQ, sT) • The  for which the response is always “Incremented” (threshold)

10. Minimum RMS Error Predications RMS error of 11% 60% of the variance was accounted for

11. Maximum Variance Accounted For RMS error was 17% 73% of the variance was accounted for

12. Comparison Psychophysical Data RMS error: 11% 60% of variance accounted for Minimum RMS error predictions Captures mean, but not shape RMS error: 17% 73% of variance accounted for Maximum variance accounted for predictions Captures shape, but not mean

13. Monotic Level Discrimination • Under monotic conditions our model is a monaural energy detector • Level discrimination models are often more complex than simple energy detectors • Our model could be modified such that under monotic conditions it reduces to these “better” models of level discrimination • Modified models have not been evaluated • Current model is run on a 54-processor supercomputer

14. Conclusions • Monaural level discrimination can be degraded by the “other” ear • Ideal observer of two dimensions is degraded • 2D model does not predict the data accurately • Ideal observer of “loudness”, “position” and “time-image” is NOT degraded by the “other” ear • Non-ideal observer of the three dimensions predicts a large proportion of the variance of the data

15. Acknowledgments NIH NIDCD DC00100 and DC004663 Binaural Gang at Boston University Nat Durlach

16. Why Three Dimensions? • Observe: • The ideal observer of two dimensions has an RMS error of 37%

17. 2-D Model Predictions RMS error was 37% and variance was added Visually a completely wrong fit