Reinforced statistical learning of auditory categories
Year: 2020
Type: preprint
Abstract: A critical step in language acquisition is learning phoneme categories. While L1 learning has been thought to use unsupervised learning (using the distributional statistics of cues), recent research raises the possibility of supervised learning (using teaching signals). Similarly, L2 learning is studied with supervised learning, but unsupervised may also contribute. We developed the reinforced statistical learning paradigm to examine their interaction. Participants first underwent unsupervised learning, hearing a series of non-linguistic sounds whose statistical structure reflected two categories. In subsequent supervised learning, categories either matched or mismatched. Supervised learning was faster when phases matched, though benefits were limited to specific category configurations. Unsupervised learning did not affect the steepness of categorization along the relevant dimension, but it helped subjects learn to ignore irrelevant dimensions. Unsupervised learning may set the stage for supervised learning, but its role may be to determine which dimensions are important, and not to directly acquire categories.
Authors: Bob McMurray, Samantha Lam Chiu
Institution: University of Iowa
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W3163781171