A computational analysis of the constraints on parallel word identification

Erik D. Reichle, Elizabeth R. Schotter

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    5 Citations (Scopus)

    Abstract

    The debate about how attention is allocated during reading has been framed in as: Either attention is allocated in a strictly serial manner, to support the identification of one word at a time, or it is allocated as a gradient, to support the concurrent processing of multiple words. The first part of this article reviews reading models to examine the feasibility of both positions. Although word-identification and sentence-processing models assume that words are identified serially to incrementally build larger units of representation, discourse-processing model allow several propositions to be co-active in working memory. The remainder of this article then describes an instance-based model of word identification, Über-Reader, and simulations comparing the identification of single words and word pairs. These simulations indicate that, although word pairs can be identified, accurate identification is restricted to short high-frequency words due to the computational demands of both memory retrieval and limited visual acuity.

    Original languageEnglish
    Title of host publicationCogSci 2020: Proceedings of the 42nd Annual Conference of the Cognitive Science Society
    Subtitle of host publicationdeveloping a mind: learning in humans, animals, and machines
    Place of PublicationAustin, Texas
    PublisherCognitive Science Society
    Pages164-170
    Number of pages7
    Publication statusPublished - 2020
    EventAnnual Meeting of the Cognitive Science Society (42nd : 2020) - Virtual
    Duration: 29 Jul 20201 Aug 2020

    Conference

    ConferenceAnnual Meeting of the Cognitive Science Society (42nd : 2020)
    CityVirtual
    Period29/07/201/08/20

    Bibliographical note

    Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • attention
    • computational modeling
    • reading
    • sentence processing
    • word identification
    • Über-Reader

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