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Are we really Bayesian? Probabilistic inference shows sub-optimal knowledge transfer.

Chin-Hsuan Sophie Lin1, Trang Thuy Do1, Lee Unsworth1

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Humans learn and combine prior knowledge with sensory evidence like Bayesians, but may not use full Bayesian computation for all behaviors. This study explores how people integrate new information, revealing Bayes-suboptimal but adaptive strategies.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Making

Background:

  • The Bayesian framework accurately models how humans integrate prior knowledge and sensory evidence.
  • Debate exists on whether human behavior reflects precise, computationally intensive Bayesian calculations.

Purpose of the Study:

  • To investigate if human behavior aligns with full Bayesian computation when integrating novel information.
  • To assess how participants combine learned priors with new likelihood information.

Main Methods:

  • Participants estimated target locations using prior knowledge and noisy sensory evidence (likelihood).
  • A transfer learning paradigm tested integration of trained priors with novel likelihoods.
  • Behavioral data were analyzed to quantify deviations from Bayes-optimal predictions.

Main Results:

  • Participants learned priors and combined information in a Bayes-like manner.
  • Integration of novel likelihoods was better within the learned range (interpolation) than outside (extrapolation).
  • Observed integration was quantitatively Bayes-suboptimal in both interpolation and extrapolation conditions.

Conclusions:

  • Human behavior can be Bayes-like without employing full Bayesian computation.
  • Cognitive strategies for integrating new information are adaptive but not always optimal.
  • The study provides a framework for investigating decision-making mechanisms in various tasks.