ArticlePsychological review2020
The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments.
Article in Psychological review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- The samplr package: A tool for modeling human cognition with sampling algorithms.Behavior research methods · 2026Article
- Estimating sample size in conceptual property norms by standardizing coverage.Behavior research methods · 2026Article
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- A Bayesian perspective on observers' inference of group norms.NPJ science of learning · 2026Article
- Divergent patterns of probabilistic reasoning in humans and GPT-5.Frontiers in psychology · 2026Article
- Probabilities of conditionals: The relevance effect might be confounded by the existence of boundary cases.Psychonomic bulletin & review · 2025Article
- An overview of the quantum cognition research program.Psychonomic bulletin & review · 2025Review
- The Bias-and-Expertise Model: A Bayesian Network Model of Political Source Characteristics.Cognitive science · 2025Article
- Information sampling and Bayesian belief formation in statistical judgment.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A Magic Act in Causal Reasoning: Making Markov Violations Disappear.Entropy (Basel, Switzerland) · 2025Article
- Noise in Cognition: Bug or Feature?Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- Subjective probability is modulated by emotions.Scientific reports · 2025Article
- Models of Variability in Probabilistic Causal Judgments.Computational brain & behavior · 2025Article
- A quantum microtubule substrate of consciousness is experimentally supported and solves the binding and epiphenomenalism problems.Neuroscience of consciousness · 2025Article
- Conscious active inference I: A quantum model naturally implements the path integral needed for real-time planning and control.Computational and structural biotechnology journal · 2025Review
- From exchangeability to rational belief: a cognitive interpretation of de Finetti's theorem.Frontiers in psychology · 2025Article
- The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.Psychological review · 2024Article
- Actively Learning to Learn Causal Relationships.Computational brain & behavior · 2024Article
- The Future of Decisions From Experience: Connecting Real-World Decision Problems to Cognitive Processes.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
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Abstract
Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of "noise" in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017; Costello & Watts, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities and distributions of probability estimates. We show in 2 new experiments that this model better captures these mean judgments both qualitatively and quantitatively; which model best fits individual distributions of responses depends on the assumed size of the cognitive sample. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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