Category formation lies at the heart of a number of higher-order behaviors, including language. We assessed the ability of human adults to learn, from distributional information alone, categories embedded in a sequence of input stimuli using a serial reaction time task. Artificial grammars generated corpora of input strings containing a predetermined and constrained set of sequential statistics. After training, learners were presented with novel input strings, some of which contained violations of the category membership defined by distributional context. Category induction was assessed by comparing performance on novel and familiar strings. Results indicate that learners develop increasing sensitivity to the category structure present in the input, and become sensitive to fine-grained differences in the pre- and post-element contexts that define category membership. Results suggest that distributional analysis plays a significant role in the development of visuomotor categories, and may play a similar role in the induction of linguistic form-class categories.
Bibliographical noteFunding Information:
We would like to thank Elissa L. Newport for productive conversations at many points in the conceptualization and planning of this work. We would also like to thank Jennifer Hooker, Elizabeth Gramzow, and Koleen McCrink for their assistance in data collection. This research was conducted as part of the first author’s doctoral dissertation at the University of Rochester, and was supported in part by funding from the NSF (SBR-9873477), the NIH (HD-037082), the Packard Foundation (2001-17783), and the ONR (N00014-07-1-0937).
- Artificial grammar
- Category induction
- Distributional analysis
- Serial reaction time
- Statistical learning