Using computational auditory models to predict simultaneous masking data: Model comparison

In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation bet ween the psychophysics of hearing and the underlying physiology, One approa ch to studying the auditory system has been to design computational auditor y models that predict neurophysiological data such as neural firing rates [ 15], [1], To link these physiologically-based models to psychophysics, theo retical bounds on detection performance have been derived using signal dete ction theory to analyze the simulated data for various psychophysical tasks [20]. Previous efforts, including our own recent work using the Auditory Image Mo del, have demonstrated the validity of this type of analysis; however, theo retical predictions often continue tb exceed experimentally-measured perfor mance [9], [21]. In this paper, we compare predictions of detection perform ance across several computational auditory models. We also reconcile some o f the previously observed discrepancies by incorporating appropriate signal uncertainty into the optimal detector.