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 language | English |
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Title of host publication | CogSci 2020: Proceedings of the 42nd Annual Conference of the Cognitive Science Society |
Subtitle of host publication | developing a mind: learning in humans, animals, and machines |
Place of Publication | Austin, Texas |
Publisher | Cognitive Science Society |
Pages | 164-170 |
Number of pages | 7 |
Publication status | Published - 2020 |
Event | Annual Meeting of the Cognitive Science Society (42nd : 2020) - Virtual Duration: 29 Jul 2020 → 1 Aug 2020 |
Conference
Conference | Annual Meeting of the Cognitive Science Society (42nd : 2020) |
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City | Virtual |
Period | 29/07/20 → 1/08/20 |
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