A dynamic neural field model of memory, attention and cross-situational word learning

Bhat, Ajaz A. ORCID: https://orcid.org/0000-0002-6992-8224, Spencer, John P. ORCID: https://orcid.org/0000-0002-7320-144X and Samuelson, Larissa K. ORCID: https://orcid.org/0000-0002-9141-3286 (2018) A dynamic neural field model of memory, attention and cross-situational word learning. In: Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018 . The Cognitive Science Society, USA, pp. 142-147. ISBN 9780991196784

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Abstract

Recent empirical studies have affirmed the fundamental role of attention and memory processes in statistical word learning tasks. These processes interact in complex ways to guide spontaneous looking behaviors of learners as well as determine their overall learning performance. On the modelling side, studies have made it clear that computational models must provide process-based rather than only computational accounts of word learning, because these can connect to the empirically observed behaviors at a moment-to-moment timescale. Thus, here we present a neurally-grounded process model of word learning called WOLVES (Word-Object Learning Via Visual Exploration in Space) that integrates visual dynamics and word-object binding across multiple timescales. WOLVES integrates multiple established dynamic neural field models to allow fine-grained indexing of component processes driving the looking-learning loop. We report simulation results for three empirical cross-situational word learning experiments to validate the model.

Item Type: Book Section
Additional Information: Funding Information: This work was funded by grant no. R01HD045713 from the NICHD awarded to LKS. Publisher Copyright: © 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.
Uncontrolled Keywords: attention and memory,cross-situational word learning,dft,dynamic neural field theory,artificial intelligence,computer science applications,human-computer interaction,cognitive neuroscience ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School: Faculty of Social Sciences > School of Psychology
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Depositing User: LivePure Connector
Date Deposited: 25 Jan 2023 09:30
Last Modified: 25 Jan 2023 09:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/90782
DOI:

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