Using the ideal observer to predict performance in perceptual tasks: An example from the auditory temporal masking domain

This article provides a demonstration of an analytical technique that can be used to investigate the causes of perceptual phenomena. The technique is based on the concept of the ideal observer, an optimal signal classifier that makes decisions that maximize the probability of a correct response. To demonstrate the technique, an analysis was conducted to investigate the role of the auditory periphery in the production of temporal masking effects. The ideal observer classified output from four models of the periphery. Since the ideal observer is the best of all possible observers, if it demonstrates masking effects, then all other observers must as well. If it does not demonstrate masking effects, then nothing about the periphery requires masking to occur, and therefore masking would occur somewhere else. The ideal observer exhibited several forward masking effects but did not exhibit backward masking, implying that the periphery has a causal role in forward but not backward masking. A general discussion of the strengths of the technique and supplementary equations are also included.