ArticleCerebral cortex (New York, N.Y. : 1991)2023
The neural correlates of domain-general visual ability.
Article in Cerebral cortex (New York, N.Y. : 1991), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Rethinking category-selectivity in human visual cortex.Cognitive neuroscience · 2026Review
- Object recognition ability predicts episodic location memory, enhanced by meaningfulness.Psychological research · 2026Article
- Advancement in modeling of Alzheimer's disease: a comprehensive review of preclinical screening platforms.Frontiers in aging neuroscience · 2025Review
- Measuring object recognition ability: Reliability, validity, and the aggregate z-score approach.Behavior research methods · 2024Article
- Developmental emergence of holistic processing in word recognition.Developmental science · 2023Article
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3 authors.
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Abstract
People vary in their general ability to compare, identify, and remember objects. Research using latent variable modeling identifies a domain-general visual recognition ability (called o) that reflects correlations among different visual tasks and categories. We measure associations between a psychometrically-sensitive measure of o and a neurometrically-sensitive measure of visual sensitivity to shape. We report evidence for distributed neural correlates of o using functional and anatomical regions-of-interest (ROIs) as well as whole brain analyses. Neural selectivity to shape is associated with o in several regions of the ventral pathway, as well as additional foci in parietal and premotor cortex. Multivariate analyses suggest the distributed effects in ventral cortex reflect a common mechanism. The network of brain areas where neural selectivity predicts o is similar to that evoked by the most informative features for object recognition in prior work, showing convergence of 2 different approaches on identifying areas that support the best object recognition performance. Because o predicts performance across many visual tasks for both novel and familiar objects, we propose that o could predict the magnitude of neural changes in task-relevant areas following experience with specific task and object category.
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