Evaluating Auditory Performance Limits: II. One-parameter discrimination with random-level variation

Previous studies have combined analytical models of stochastic neural respo nses with signal detection theory (SDT) to predict psychophysical performan ce limits; however, these studies have typically been limited to simple mod els and simple psychophysical tasks. A companion article in this issue ("Ev aluating Auditory Performance Limits: I") describes an extension of the SDT approach to allow the use of computational models that provide more accura te descriptions of neural responses. This article describes an extension to more complex psychophysical tasks. A general method is presented for evalu ating psychophysical performance limits for discrimination tasks in which o ne stimulus parameter is randomly varied. Psychophysical experiments often randomly vary a single parameter in order to restrict the cues that are ava ilable to the subject. The method is demonstrated for the auditory task of random-level frequency discrimination using a computational auditory nerve (AN) model. Performance limits based on AN discharge times (all-information ) are compared to performance limits based only on discharge counts (rate p lace). Both decision models are successful in predicting that random-level variation has no effect on performance in quiet, which is the typical resul t in psychophysical tasks with random-level variation. The distribution of information across the AN population provides insight into how different ty pes of AN information can be used to avoid the influence of random-level va riation. The rate-place model relies on comparisons between fibers above an d below the tone frequency (i.e., the population response), while the all-i nformation model does not require such across-fiber comparisons. Frequency discrimination with random-level variation in the presence of high-frequenc y noise is also simulated. No effect is predicted for all-information, cons istent with the small effect in human performance; however, a large effect is predicted for rate-place in noise with random-level variation.