The power of a unimodal distribution in cue reweighting: Unimodality vs prediction error as signs of cue irrelevance

Abstract

Maye & Gerken (2000) proposed that sound categories can be learned from probability distributions: a unimodal distribution suggests a single category, while a bimodal one suggests two contrasting ones. Research on distributional learning has focused on developing a contrast through exposure to a bimodal distribution. Here, we instead investigate how exposure to a unimodal distribution affects perception of a pre-existing multidimensional contrast (voicing, for which the primary cue is VOT). A total of 60 adult native English speakers were exposed to either bimodal or unimodal VOT distributions spanning the unaspirated/aspirated boundary (bear/pear). However, we paired acoustic stimuli with pictures of bears and pears independently of VOT in training. For each stimulus, participants were asked to guess the referent and received (random) feedback, generating an error signal that suggested VOT is no longer informative and should be downweighed. In this design, the bimodal distribution suggests the existence ...