A neural network model of lexical-semantic competition during spoken word recognition

<p>Visual world studies show that upon hearing a word in a target-absent visual context containing related and unrelated items, toddlers and adults briefly direct their gaze toward phonologically related items, before shifting toward semantically and visually related ones. We present a neural...

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Bibliografski detalji
Glavni autori: Duta, M, Plunkett, K
Format: Journal article
Jezik:English
Izdano: Frontiers Media 2021
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author Duta, M
Plunkett, K
author_facet Duta, M
Plunkett, K
author_sort Duta, M
collection OXFORD
description <p>Visual world studies show that upon hearing a word in a target-absent visual context containing related and unrelated items, toddlers and adults briefly direct their gaze toward phonologically related items, before shifting toward semantically and visually related ones. We present a neural network model that processes dynamic unfolding phonological representations of words and maps them to static internal lexical, semantic, and visual representations. The model, trained on representations derived from real corpora, simulates this early phonological over semantic/visual preference. Our results support the hypothesis that incremental unfolding of a spoken word is in itself sufficient to account for the transient preference for phonological competitors over both unrelated and semantically and visually related ones. Phonological representations mapped dynamically in a bottom-up fashion to semantic-visual representations capture the early phonological preference effects reported in visual world tasks. The semantic visual preference typically observed later in such a task does not require top-down feedback from a semantic or visual system.</p>
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spelling oxford-uuid:bbebe9cb-f8ec-47f0-acd6-115197fa198d2024-08-19T17:33:14ZA neural network model of lexical-semantic competition during spoken word recognitionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:bbebe9cb-f8ec-47f0-acd6-115197fa198dEnglishSymplectic ElementsFrontiers Media2021Duta, MPlunkett, K<p>Visual world studies show that upon hearing a word in a target-absent visual context containing related and unrelated items, toddlers and adults briefly direct their gaze toward phonologically related items, before shifting toward semantically and visually related ones. We present a neural network model that processes dynamic unfolding phonological representations of words and maps them to static internal lexical, semantic, and visual representations. The model, trained on representations derived from real corpora, simulates this early phonological over semantic/visual preference. Our results support the hypothesis that incremental unfolding of a spoken word is in itself sufficient to account for the transient preference for phonological competitors over both unrelated and semantically and visually related ones. Phonological representations mapped dynamically in a bottom-up fashion to semantic-visual representations capture the early phonological preference effects reported in visual world tasks. The semantic visual preference typically observed later in such a task does not require top-down feedback from a semantic or visual system.</p>
spellingShingle Duta, M
Plunkett, K
A neural network model of lexical-semantic competition during spoken word recognition
title A neural network model of lexical-semantic competition during spoken word recognition
title_full A neural network model of lexical-semantic competition during spoken word recognition
title_fullStr A neural network model of lexical-semantic competition during spoken word recognition
title_full_unstemmed A neural network model of lexical-semantic competition during spoken word recognition
title_short A neural network model of lexical-semantic competition during spoken word recognition
title_sort neural network model of lexical semantic competition during spoken word recognition
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