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Probability Matching as a Computational Strategy Used in Perception
Open accessAbstract
The question of which strategy is employed in human decision making has been studied extensively in the context of cognitive tasks; however, this question has not been investigated systematically in the context of perceptual tasks. The goal of this study was to gain insight into the decision-making strategy used by human observers in a low-level perceptual task. Data from more than 100 individuals who participated in an auditory-visual spatial localization task was evaluated to examine which of three plausible strategies could account for each observer's behavior the best. This task is very suitable for exploring this question because it involves an implicit inference about whether the auditory and visual stimuli were caused by the same object or independent objects, and provides different strategies of how using the inference about causes can lead to distinctly different spatial estimates and response patterns. For example, employing the commonly used cost function of minimizing the mean squared error of spatial estimates would result in a weighted averaging of estimates corresponding to different causal structures. A strategy that would minimize the error in the inferred causal structure would result in the selection of the most likely causal structure and sticking with it in the subsequent inference of location-"model selection." A third strategy is one that selects a causal structure in proportion to its probability, thus attempting to match the probability of the inferred causal structure. This type of probability matching strategy has been reported to be used by participants predominantly in cognitive tasks. Comparing these three strategies, the behavior of the vast majority of observers in this perceptual task was most consistent with probability matching. While this appears to be a suboptimal strategy and hence a surprising choice for the perceptual system to adopt, we discuss potential advantages of such a strategy for perception.
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“Bayesian samplers can match behavioral stochasticity in tasks such as perception, categorization, causal reasoning, and decision making [20,21,23,24].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [23]
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Metadata
- Authors
- Wozny, D. R., Beierholm, U. R., & Shams, L.
- Affiliations
- University of California, Los Angeles (US)
- Gatsby Computational Neuroscience Unit (GB)
- Corresponding author
- Shams, Ladan
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-08-05
- Volume
- 6
- Issue
- 8
- Pages
- e1000871
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2916852
- OpenAlex
- W2067064274
- Cited by
- 265
- References
- 50
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 48 · 2023: 15 · 2022: 15 · 2021: 21 · 2020: 15 · 2019: 20 · 2018: 24 · 2017: 18 · 2016: 16 · 2015: 15 · 2014: 5 · 2013: 4 · 2012: 7
- FWCI
- 8.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Max Planck Digital Library (submittedVersion, other-oa)
- Figshare (submittedVersion, cc-by)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Funders
- National Institutes of Health
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- probability matching, perceptual decision making, causal inference, model selection, weighted averaging, suboptimal strategies, cross-modal integration, perceptual inference
- MeSH terms
- Decision Making, Female, Humans, Male, Probability Learning, Sound Localization, Visual Perception, Young Adult
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