Types of approximation for probabilistic cognition: Sampling and variational.
Open accessAbstract
A basic challenge for probabilistic models of cognition is explaining how probabilistically correct solutions are approximated by the limited brain, and how to explain mismatches with human behavior. An emerging approach to solving this problem is to use the same approximation algorithms that were been developed in computer science and statistics for working with complex probabilistic models. Two types of approximation algorithms have been used for this purpose: sampling algorithms, such as importance sampling and Markov chain Monte Carlo, and variational algorithms, such as mean-field approximations and assumed density filtering. Here I briefly review this work, outlining how the algorithms work, how they can explain behavioral biases, and how they might be implemented in the brain. There are characteristic differences between how these two types of approximation are applied in brain and behavior, which points to how they could be combined in future research.
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It is possible to cast many approximate inference schemes as resource-rational algorithms.
“It is possible to cast many approximate inference schemes as resource-rational algorithms (A. N. Sanborn, 2017).”
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“tion, etc); they have also been put forth as candidate cognitive algorithms [96, 72].”
Background
- Authors
- Sanborn, A. N.
- Affiliations
- University of Warwick (GB)
- Corresponding author
- Sanborn, Adam N.
- ORCID
- Sanborn, Adam N.
- Venue
- Brain and Cognition
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 112
- Pages
- 98-101
- ISSN
- 0278-2626 (ISSN-L)
- OpenAlex
- W1813828162
- Cited by
- 45
- References
- 4
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 9 · 2022: 4 · 2021: 4 · 2020: 4 · 2019: 5 · 2018: 2 · 2017: 5 · 2016: 2
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Brain and Cognition (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Funders
- Economic and Social Research Council (ES/K004948/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- approximation algorithms, sampling algorithms, variational algorithms, importance sampling, Markov chain Monte Carlo, mean-field approximation, behavioral biases, cognitive modeling
- MeSH terms
- Algorithms, Brain, Cognition, Computer Simulation, Humans, Models, Statistical
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