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The Role of Attention in Category Representation.
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- Author(s): Gao M;Gao M; Turner BM; Turner BM; Sloutsky VM; Sloutsky VM
- Source:
Cognitive science [Cogn Sci] 2024 Apr; Vol. 48 (4), pp. e13438.- Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't- Language:
English - Source:
- Additional Information
- Source: Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 7708195 Publication Model: Print Cited Medium: Internet ISSN: 1551-6709 (Electronic) Linking ISSN: 03640213 NLM ISO Abbreviation: Cogn Sci Subsets: MEDLINE
- Publication Information: Publication: 2009-: Hoboken, N.J. : Wiley-Blackwell
Original Publication: Norwood, N. J., Ablex Pub. Corp. - Subject Terms:
- Abstract: Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule-plus-similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule-based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants' memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule-based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion.
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- Contributed Indexing: Keywords: Attention; Categorization; Computational model; Learning; Memory; Priming effects; Representation
- Publication Date: Date Created: 20240412 Date Completed: 20240415 Latest Revision: 20240514
- Publication Date: 20240515
- Accession Number: PMC11018344
- Accession Number: 10.1111/cogs.13438
- Accession Number: 38605457
- Source:
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