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Abstract

Recognition memory models posit that false alarm rates increase as the global similarity between the probe cueand the contents of memory is increased. Global similarity predictions have been commonly tested using category length designs where it has been found that false alarm rates increase as the number of studied items froma common category is increased. In this work, we explored global similarity predictions within unstructured listsof words using representations from the BEAGLE model (Jones & Mewhort, 2007). BEAGLE differs from traditional semantic space models in that it contains two types of representations: item vectors, which encode unordered co-occurrence, and order vectors, in which words are similar to the extent to which they are shareneighboring words in the same relative positions. Global similarity among item and order vectors was regressedonto drift rates in the diffusion decision model (DDM: Ratcliff, 1978), which unifies both response times andaccuracy. We implemented this model in a hierarchical Bayesian framework across seven datasets with listscomposed of unrelated words. Results indicated clear deficits due to global similarity among item vectors, butonly a minimal impact of global similarity among the order vectors. We also found evidence for a linear relationship between global similarity and drift rate and did not find any evidence that global similarity differentially affected performance in speed vs. accuracy emphasis conditions. In addition, we found that globalsemantic similarity could only partially account for the word frequency effect, suggesting that other factorsbesides semantic similarity may be responsible.

Item Type:	Article
Authors/Creators:	Osth, AF and Shabahang, KD and Mewhort, DJK and Heathcote, A
Keywords:	recognition memory, semantic similarity, semantic space models, diffusion decision model
Journal or Publication Title:	Journal of Memory and Language
Publisher:	Academic Press Inc Elsevier Science
ISSN:	0749-596X
DOI / ID Number:	10.1016/j.jml.2019.104071
Copyright Information:	© 2019 Elsevier Inc. All rights reserved.
Related URLs:	Google Scholar: Heathcote, A UTAS Profile: Heathcote, A
Item Statistics:	View statistics for this item

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