Sensitivity to changing characteristics of Gaussian-shaped stimulus distributions in auditory categorization

Abstract

This experiment examined the ability of human listeners to categorize sounds as a function of changing training distribution characteristics. Participants were presented non-speech sounds randomly sampled from two overlapping, Gaussian-like distributions. The sounds consisted of narrow-band noise bursts varying in center frequency. Participants mapped the distributions of sounds onto creatures in a video game receiving visual and auditory feedback about accuracy. The distributions were constant for half of the participants. For the other half, an unsignaled switch to distributions with a new optimal boundary occurred in the middle of the session. By examining obtained category boundaries one can distinguish between 3 possible responses to the switch: (1) adaptive switching resulting in the new optimal boundary being learned; (2) persistence of first learned boundary; and (3)averaging input across the entire session, resulting in an obtained boundary midway between the first and second optimal boundaries. The results demonstrate that most listeners could adaptively switch between distribution conditions with no external signal informing them of such a switch. The results indicate that most listeners are updating possible underlying distributions of input in a remarkably adaptive manner. [Work supported by NIH.]