ArticlePsychological science2016
Searching for Category-Consistent Features: A Computational Approach to Understanding Visual Category Representation.
Article in Psychological science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Distractor similarity and category variability effects in search.Attention, perception & psychophysics · 2024Article
- The attentive reconstruction of objects facilitates robust object recognition.PLoS computational biology · 2024Article
- Explicit and implicit category learning in categorical visual search.Attention, perception & psychophysics · 2023Article
- Generalisation of value-based attentional priority is category-specific.Quarterly journal of experimental psychology (2006) · 2023Article
- Children's knowledge of superordinate words predicts subsequent inductive reasoning.Journal of experimental child psychology · 2022Article
- Negative cues minimize visual search specificity effects.Vision research · 2022Article
- Real-world object categories and scene contexts conjointly structure statistical learning for the guidance of visual search.Attention, perception & psychophysics · 2022Article
- Categorical cuing: Object categories structure the acquisition of statistical regularities to guide visual search.Journal of experimental psychology. General · 2021Article
- Target specificity improves search, but how universal is the benefit?Attention, perception & psychophysics · 2020Article
- Template-to-distractor distinctiveness regulates visual search efficiency.Current opinion in psychology · 2019Review
- Specifying the precision of guiding features for visual search.Journal of experimental psychology. Human perception and performance · 2019Article
- Adding details to the attentional template offsets search difficulty: Evidence from contralateral delay activity.Journal of experimental psychology. Human perception and performance · 2017Article
- Visual search for object categories is predicted by the representational architecture of high-level visual cortex.Journal of neurophysiology · 2017Article
- Object Categorization in Finer Levels Relies More on Higher Spatial Frequencies and Takes Longer.Frontiers in psychology · 2017Article
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Authors and funding
3 authors.
Funding
Abstract
This article introduces a generative model of category representation that uses computer vision methods to extract category-consistent features (CCFs) directly from images of category exemplars. The model was trained on 4,800 images of common objects, and CCFs were obtained for 68 categories spanning subordinate, basic, and superordinate levels in a category hierarchy. When participants searched for these same categories, targets cued at the subordinate level were preferentially fixated, but fixated targets were verified faster when they followed a basic-level cue. The subordinate-level advantage in guidance is explained by the number of target-category CCFs, a measure of category specificity that decreases with movement up the category hierarchy. The basic-level advantage in verification is explained by multiplying the number of CCFs by sibling distance, a measure of category distinctiveness. With this model, the visual representations of real-world object categories, each learned from the vast numbers of image exemplars accumulated throughout everyday experience, can finally be studied.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.