Speaker
Description
When asked to discriminate between two stimuli that differ along some dimension, such as intensity, frequency, or orientation, humans exhibit a smallest stimulus difference, or threshold, below which discrimination becomes impossible. These limits on perceptual sensitivity have traditionally been explained by internal noise. Contemporary theories instead link perceptual sensitivity to efficient coding of the natural environment, but have not been comprehensively tested. Here, we measured the psychoacoustic sensitivity of a computational model optimized for everyday sound localization and recognition tasks, estimating thresholds using linear classifiers operating on the model’s internal representations. Thresholds derived from learned model representations exhibited qualitative and quantitative matches to human sensitivity across a large set of perceptual dimensions. The results show that many classical perceptual phenomena (such as spectrotemporal modulation sensitivity and binaural unmasking) are side effects of representations optimized for natural listening behavior, and raise the possibility that psychophysical thresholds are determined by linear separability in these optimized representations, rather than intrinsic neural noise.