Human inference reflects a normative balance of complexity and accuracy

Gaia Tavoni (), Takahiro Doi, Chris Pizzica, Vijay Balasubramanian and Joshua I. Gold
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Gaia Tavoni: Washington University in St. Louis
Takahiro Doi: University of Pennsylvania
Chris Pizzica: University of Pennsylvania
Vijay Balasubramanian: University of Pennsylvania
Joshua I. Gold: University of Pennsylvania

Nature Human Behaviour, 2022, vol. 6, issue 8, 1153-1168

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41562-022-01357-z

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