Reducing Noise from Competing Neighbours: Word Retrieval with Lateral Inhibition in Multilink
Aaron van Geffen

TL;DR
This study enhances the Multilink model for word retrieval by incorporating lateral inhibition, improving its fit to empirical reaction time data and elucidating its role in lexical selection and translation tasks.
Contribution
The paper introduces lateral inhibition into the Multilink model and adds a task component for translation, significantly improving its empirical data fit and understanding of lexical processes.
Findings
Lateral inhibition improved model-data correlation to 0.67.
Adding a task component enhanced simulation accuracy for translation.
Model showed high correlation (0.538) with reaction times in empirical data.
Abstract
Multilink is a computational model for word retrieval in monolingual and multilingual individuals under different task circumstances (Dijkstra et al., 2018). In the present study, we added lateral inhibition to Multilink's lexical network. Parameters were fit on the basis of reaction times from the English, British, and Dutch Lexicon Projects. We found a maximum correlation of 0.643 (N=1,205) on these data sets as a whole. Furthermore, the simulations themselves became faster as a result of adding lateral inhibition. We tested the fitted model to stimuli from a neighbourhood study (Mulder et al., 2018). Lateral inhibition was found to improve Multilink's correlations for this study, yielding an overall correlation of 0.67. Next, we explored the role of lateral inhibition as part of the model's task/decision system by running simulations on data from two studies concerning interlingual…
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Taxonomy
TopicsNeurobiology of Language and Bilingualism · Reading and Literacy Development · Topic Modeling
