Instance memory models as a general computational framework for exploring language processing: bringing the lexicon to life
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Brendan T. Johns
,
Randall K. Jamieson
Abstract
Instance models have been successfully applied to a range of problems including memory, language, attention, learning, action, decision-making, and categorization. According to instance theory, the individual experience constitutes the fundamental unit of knowledge and knowledge of the general emerges during parallel retrieval from memory. Until recently, applications of instance theory to the problem of language were constrained to small and contrived laboratory experiments. However, recent advances in large-scale computational modeling have allowed the approach to be applied at scale to the large and complicated problem of natural language. With those demonstrations now in hand, we argue that the framework can present an articulate mechanistic underbelly to usage-based theories of language that highlights the role of specific language experience in general language behavior. Overall, this article argues that instance memory models provide an opportunity to gain insight into and deepen our understanding of language as a dynamic and contextually embedded process, serving to bridge the gap between cognitive psychology and the language sciences.
Funding source: Natural Sciences and Engineering Research Council of Canada
Award Identifier / Grant number: RGPIN-2020-04727
Acknowledgments
This research was supported by Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2020-04727 to BTJ.
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