Attention as inference: Selection is probabilistic; responses are all-or-none samples.
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Theories of probabilistic cognition postulate that internal representations are made up of multiple simultaneously held hypotheses, each with its own probability of being correct (henceforth, "probability distributions"). However, subjects make discrete responses and report the phenomenal contents of their mind to be all-or-none states rather than graded probabilities. How can these 2 positions be reconciled? Selective attention tasks, such as those used to study crowding, the attentional blink, rapid serial visual presentation, and so forth, were recast as probabilistic inference problems and used to assess how graded, probabilistic representations may produce discrete subjective states. The authors asked subjects to make multiple guesses per trial and used 2nd-order statistics to show that (a) visual selective attention operates in a graded fashion in time and space, selecting multiple targets to varying degrees on any given trial; and (b) responses are generated by a process of sampling from the probabilistic states that result from graded selection. The authors concluded that although people represent probability distributions, their discrete responses and conscious states are products of a process that samples from these probabilistic representations.
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Vul, E. (2010) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5 · see citing works
Self-citationAssociative Recognition: Exploring the Contributions of Recollection and Familiarity
Murray, J. (2014) · Stirling Online Research Repository (University of Stirling) · Cited by 5 · see citing works
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Rieth, C. A., & Vul, E. (2012) · eScholarship (California Digital Library) · Cited by 1 · see citing works
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Yiming, L. I. U., Haocheng, L. U. O., & Fu, S. (2023) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2024.00264
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Zhou, Y. J., Curtis, C. E., Fougnie, D. L., & Sreenivasan, K. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.17.688881
Using a betting game to reveal the rich nature of visual working memoriesPreprint
Jabar, S. B. (2020) · OSF Preprints (OSF Preprints)
Shifting the educational paradigm: Discovering concepts and connections
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Hosseini, M. N. G., Zivony, A., Martin Eimer, A., Wyble, B., & Bowman, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.07.749788
- Authors
- Vul, E., Hanus, D., & Kanwisher, N.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Journal of Experimental Psychology: General
- Published
- 2009
- Volume
- 138
- Issue
- 4
- Pages
- 546-560
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- PMC
- PMC2822457
- OpenAlex
- W2092534127
- Cited by
- 86
- References
- 59
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 2 · 2021: 9 · 2020: 8 · 2019: 5 · 2018: 3 · 2017: 4 · 2016: 5 · 2015: 3 · 2014: 8 · 2013: 6 · 2012: 7
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Experimental Psychology General (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (EY13455)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- probabilistic inference, selective attention, attentional sampling, probability distributions, attentional blink, crowding, rapid serial visual presentation, discrete response, conscious perception, second-order statistics
- MeSH terms
- Adolescent, Adult, Attention, Cognition, Cues, Humans, Probability, Psychological Theory, Task Performance and Analysis, Visual Perception, Uncertainty, Young Adult
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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