ERP evidence for the ecological validity of artificial language learning

Abstract

The experimental study of artificial language learning has become a widely used means of investigating the predictions of theories of language learning. Although much is now known about the generalizations that learners make from various kinds of data, relatively little is known about how those generalizations are cognitively encoded. This paper presents an ERP study of brain responses to violations of lab-learned phonotactics. Novel words that violated a learned phonotactic constraint elicited a larger Late Positive Component (LPC) than novel words that satisfied it. Similar LPCs have been found for violations of natively acquired linguistic structure, as well as for violations of other types of abstract generalizations, such as musical structure. We argue that lab-learned phonotactic generalizations are represented abstractly, and similarly to natively acquired syntactic and phonological rules.

Documents:

Manuscript As of October, 2018

Poster Presented at LabPhon 15, the LSA annual meeting, 2016, and AMP 2018

    Late positivity to nonwords which violate the lab-learned pattern relative to nonwords which fit the lab-learned pattern.