The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Rational approximations to rational models: alternative algorithms for category learning
Open accessPublished version of a preprint
Abstract
Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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 [21]
Citations
Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,916
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,711
Sequential Monte Carlo Methods in Practice
Arnaud Doucet, B. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,516
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,536
Flagged first, then by citations
Cited by
Sorted by citations
Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892
Self-citationComputational rationality: A converging paradigm for intelligence in brains, minds, and machines
Gershman, S. J., Horvitz, E., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aac6076 · Cited by 722
Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel.
Kleinschmidt, D., & Jaeger, T. F. (2015) · Psychological Review · doi:10.1037/a0038695 · Cited by 684
A tutorial on Bayesian nonparametric models
Gershman, S. J., & Blei, D. M. (2011) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2011.08.004 · Cited by 626
Jones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533
Metadata
- Authors
- Sanborn, A. N., Griffiths, T. L., & Navarro, D. J.
- Affiliations
- Gatsby Computational Neuroscience Unit (GB)
- University of California System (US)
- Venue
- Psychological Review
- Published
- 2010
- Volume
- 117
- Issue
- 4
- Pages
- 1144-1167
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2038407704
- Semantic Scholar
- bf244633a58d0ca27f2b383ed225f3c42481eed9
- Cited by
- 369
- References
- 94
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 9 · 2023: 28 · 2022: 12 · 2021: 15 · 2020: 26 · 2019: 48 · 2018: 24 · 2017: 28 · 2016: 22 · 2015: 34 · 2014: 17 · 2013: 26 · 2012: 29
- FWCI
- 34.33
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Funders
- National Science Foundation (IIS-0845410)
- Royal Society
- Air Force Office of Scientific Research (FA9550-07-1-0351)
- Australian Research Council (DP0773794)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- category learning, categorization, approximation algorithms, Monte Carlo methods, Gibbs sampling, particle filter, approximate inference, sequential inference, human inference
- MeSH terms
- Algorithms, Cognition, Discrimination, Psychological, Humans, Learning, Models, Psychological, Monte Carlo Method, Probability, Models, Statistical
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
