Listeners' convergence towards an artificial agent in a joint phoneme categorization task

Abstract

This study focuses on inter-individual convergence effects in the perception and categorization of speech sounds. We ask to what extent two listeners can come to establish a shared set of categorization criteria in a phoneme identification task that they accomplish together. Several hypotheses are laid out in the framework of a Bayesian model of speech perception that we have developed to account for how two listeners may each infer the parameters that govern their partner’s responses. In our experimental paradigm, participants were asked to perform a joint phoneme identification task with a partner that, unbeknownst to them, was an artificial agent, whose responses we manipulated along two dimensions, the location of the categorical boundary and the slope of the identification function. Convergence was found to arise for bias but not for slope. Numerical simulations suggested that lack of convergence in slope may stem from the listeners’ prior level of confidence in the variance in VOT for the two phonemic categories. This study sheds new light on perceptual convergence between listeners in the categorization of speech sounds, a phenomenon that has received little attention so far in spite of its central importance for speech communication.