Learning words in space and time: Contrasting models of the suspicious coincidence effect

Jenkins, Gavin W., Samuelson, Larissa ORCID: https://orcid.org/0000-0002-9141-3286, Penny, Will D. ORCID: https://orcid.org/0000-0001-9064-1191 and Spencer, John ORCID: https://orcid.org/0000-0002-7320-144X (2021) Learning words in space and time: Contrasting models of the suspicious coincidence effect. Cognition, 210. ISSN 0010-0277

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Abstract

In their 2007b Psychological Review paper, Xu and Tenenbaum found that early word learning follows the classic logic of the “suspicious coincidence effect:” when presented with a novel name (‘fep’) and three identical exemplars (three Labradors), word learners generalized novel names more narrowly than when presented with a single exemplar (one Labrador). Xu and Tenenbaum predicted the suspicious coincidence effect based on a Bayesian model of word learning and demonstrated that no other theory captured this effect. Recent empirical studies have revealed, however, that the effect is influenced by factors seemingly outside the purview of the Bayesian account. A process-based perspective correctly predicted that when exemplars are shown sequentially, the effect is eliminated or reversed (Spencer, Perone, Smith, & Samuelson, 2011). Here, we present a new, formal account of the suspicious coincidence effect using a generalization of a Dynamic Neural Field (DNF) model of word learning. The DNF model captures both the original finding and its reversal with sequential presentation. We compare the DNF model's performance with that of a more flexible version of the Bayesian model that allows both strong and weak sampling assumptions. Model comparison results show that the dynamic field account provides a better fit to the empirical data. We discuss the implications of the DNF model with respect to broader contrasts between Bayesian and process-level models.

Item Type: Article
Additional Information: The authors would like to acknowledge Bob McMurray for his contributions to an earlier version of this manuscript. We also thank Gregor Schöner for discussions of the theoretical ideas and Samuel Forbes for help with the model repository. This research was supported in part by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program award to GWJ and by the Eunice Kennedy Shriver National Institute of Child Health & Human Development award number R01HD045713 to LKS. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver Institute of Child Health & Human Development or the National Institutes of Health.
Uncontrolled Keywords: bayesian model,category hierarchy,comparison,dynamic field model,word learning,experimental and cognitive psychology,language and linguistics,developmental and educational psychology,linguistics and language,cognitive neuroscience ,/dk/atira/pure/subjectarea/asjc/3200/3205
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: LivePure Connector
Date Deposited: 06 Jan 2021 00:59
Last Modified: 22 Oct 2022 07:36
URI: https://ueaeprints.uea.ac.uk/id/eprint/78054
DOI: 10.1016/j.cognition.2020.104576

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