Human inference reflects a normative balance of complexity and accuracy
Open accessAbstract
We must often infer latent properties of the world from noisy and changing observations. Complex, probabilistic approaches to this challenge such as Bayesian inference are accurate but cognitively demanding, relying on extensive working memory and adaptive processing. Simple heuristics are easy to implement but may be less accurate. What is the appropriate balance between complexity and accuracy? Here we model a hierarchy of strategies of variable complexity and find a power law of diminishing returns: increasing complexity gives progressively smaller gains in accuracy. The rate of diminishing returns depends systematically on the statistical uncertainty in the world, such that complex strategies do not provide substantial benefits over simple ones when uncertainty is either too high or too low. In between, there is a complexity dividend. In two psychophysical experiments, we confirm specific model predictions about how working memory and adaptivity should be modulated by uncertainty.
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- Authors
- Tavoni, G., Doi, T., Pizzica, C., Balasubramanian, V., & Gold, J. I.
- Affiliations
- Washington University in St. Louis (US)
- University of Pennsylvania (US)
- Corresponding author
- Tavoni, Gaia
- Venue
- Nature Human Behaviour
- Published
- 2022-05-30
- Volume
- 6
- Issue
- 8
- Pages
- 1153-1168
- ISSN
- 2397-3374 (ISSN-L)
- PMC
- PMC9446026
- OpenAlex
- W4281869941
- Cited by
- 35
- References
- 60
- Citations by year
- 2026: 13 · 2025: 10 · 2024: 7 · 2023: 5
- FWCI
- 3.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH115557)
- National Institute of Biomedical Imaging and Bioengineering (R01EB026945)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- human inference, accuracy-complexity trade-off, Bayesian inference, heuristics, working memory, statistical uncertainty, adaptive inference, probabilistic reasoning
- MeSH terms
- Heuristics, Bayes Theorem, Data Collection, Humans, Uncertainty
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