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Cited by 645

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Category-specific attention for animals reflects ancestral priorities, not expertise

Open access
New, Joshua, Cosmides, Leda, Tooby, John·2007·Proceedings of the National Academy of Sciences

Abstract

Visual attention mechanisms are known to select information to process based on current goals, personal relevance, and lower-level features. Here we present evidence that human visual attention also includes a high-level category-specialized system that monitors animals in an ongoing manner. Exposed to alternations between complex natural scenes and duplicates with a single change (a change-detection paradigm), subjects are substantially faster and more accurate at detecting changes in animals relative to changes in all tested categories of inanimate objects, even vehicles, which they have been trained for years to monitor for sudden life-or-death changes in trajectory. This animate monitoring bias could not be accounted for by differences in lower-level visual characteristics, how interesting the target objects were, experience, or expertise, implicating mechanisms that evolved to direct attention differentially to objects by virtue of their membership in ancestrally important categories, regardless of their current utility.

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Crossref · 10.1073/pnas.0703913104

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Metadata

Authors
New, J., Cosmides, L., & Tooby, J.
Venue
Proceedings of the National Academy of Sciences
Published
2007-10-16
Volume
104
Issue
42
Pages
16598-16603
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2022111034
Cited by
645
References
2
FWCI
9.75
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Category-specific attention for animals reflects ancestral priorities, not expertise. https://app.cochat.ai/papers/10.1073/pnas.0703913104
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New, J., Cosmides, L., & Tooby, J. (2007). Category-specific attention for animals reflects ancestral priorities, not expertise. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0703913104

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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