ArticleProceedings of the National Academy of Sciences of the United States of America2021
Validating model-based Bayesian integration using prior-cost metamers.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Human learning of probability distributions is biased toward moderate structural complexity.Nature communications · 2026Article
- Selective encoding of priors for flexible categorization but not Bayesian inference in the frontal eye field.bioRxiv : the preprint server for biology · 2026Article
- A corticostriatal learning mechanism linking excess striatal dopamine and auditory hallucinations.bioRxiv : the preprint server for biology · 2025Article
- Bayesian approaches to smooth pursuit of random dot kinematograms: effects of varying RDK noise and the predictability of RDK direction.Journal of neurophysiology · 2024Article
- Are we really Bayesian? Probabilistic inference shows sub-optimal knowledge transfer.PLoS computational biology · 2024Article
- Article
- Environmental dynamics shape perceptual decision bias.PLoS computational biology · 2023Article
- A unitary mechanism underlies adaptation to both local and global environmental statistics in time perception.PLoS computational biology · 2023Article
- A General Framework for Inferring Bayesian Ideal Observer Models from Psychophysical Data.eNeuro · 2023Review
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Authors and funding
2 authors.
Funding
Abstract
There are two competing views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a paradigm that probes the sensitivity of humans to transitions between prior-cost pairs that demand the same optimal policy (metamers) but distinct internal models. We demonstrate the utility of our approach in two experiments that were classically explained by Bayesian theory. Our approach validates the Bayesian learning strategy in an interval timing task but not in a visuomotor rotation task. More generally, our work provides a domain-general approach for testing the circumstances under which humans explicitly implement model-based Bayesian computations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.