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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. Introduction.

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a binaural model of monotic level discrimination

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

introduction
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
outline
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
psychophysical experiment
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
stimulus perception
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
slide6

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
detection theoretic model
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
ideal observer
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 
non ideal observer
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)
minimum rms error predications
Minimum RMS Error Predications

RMS error of 11%

60% of the variance was accounted for

maximum variance accounted for
Maximum Variance Accounted For

RMS error was 17%

73% of the variance was accounted for

slide12

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

monotic level discrimination
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
conclusions
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
acknowledgments
Acknowledgments

NIH NIDCD

DC00100 and DC004663

Binaural Gang at Boston University

Nat Durlach

why three dimensions
Why Three Dimensions?
  • Observe:
  • The ideal observer of two dimensions has an RMS error of 37%
2 d model predictions
2-D Model Predictions

RMS error was 37% and variance was added

Visually a completely wrong fit

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