Deep Learning and Competition in Psycholinguistic Research

Abstract

MacWhinney, Bates, and colleagues developed the Competition Model in the 1980s as an alternate to Chomskyan models that encapsulate syntax as a special-purpose module. The Competition Model adopted the functional perspective that language serves communicative goals and functions. In contrast to the premise that knowledge of language is innate, the Competition model asserts that language is learned and processed through general cognitive mechanisms that identify and weight phonological, morphological, syntactic, and semantic cues in the language experiences of the learner. These weighted cues guide the language user in the comprehension and production of language forms. The present article provides background on the Competition Model, describes machine simulations of linguistic competition, and extends the principles of the Competition Model to new machine models and applications through deep learning networks.

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