A comparison using signal detection theory of the ability of two computational auditory models to predict experimental data

Abstract

In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates. To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks. Previous efforts, including our own recent work using the auditory image model, have demonstrated the validity of this type of analysis; however, theoretical predictions often exceed experimentally-measured performance. In this paper, we compare predictions of detection performance across several computational auditory models. We reconcile some of the previously observed discrepancies by incorporating phase uncertainty into the optimal detector.