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  1. Home
  3. Associative Learning Explains "intuitive Statistics" In Animals.
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  3. Associative Learning Explains "intuitive Statistics" In Animals.

Related Experiment Video

Appetitive Associative Olfactory Learning in Drosophila Larvae
09:22

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Associative learning explains "intuitive statistics" in animals.

Stefano Ghirlanda1, Janelle Mendoza2

  • 1DataWorks.

Psychological Review
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Animals can make smart decisions with uncertain outcomes, similar to a stimulus-response learning model. This associative model accurately predicts animal choices in probabilistic tasks.

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Area of Science:

  • Cognitive Science
  • Animal Behavior
  • Comparative Psychology

Background:

  • Investigating "intuitive statistics" in animals explores their decision-making under probabilistic outcomes.
  • Understanding animal cognition requires models that can quantitatively predict behavior in complex scenarios.

Purpose of the Study:

  • To review experiments on "intuitive statistics" in mammals and birds.
  • To assess the efficacy of a stimulus-response associative model in explaining animal choices.
  • To determine the quantitative accuracy of associative learning models in animal cognition research.

Main Methods:

  • Systematic review of 58 experiments concerning "intuitive statistics" in birds and mammals.
  • Comparison of animal choice data against predictions from a stimulus-response associative model.
  • Quantitative fitting of the model to data from 37 experiments, analyzing variance explained.

    • Main Results:

    • A stimulus-response associative model closely mirrored animal choices across reviewed experiments.
    • The model qualitatively reproduced all experimental results.
    • The model accounted for an average of 88% of the variance in quantitative fits to data from 37 experiments.

    Conclusions:

    • Associative learning models provide a powerful and quantitatively accurate framework for understanding animal cognition.
    • The unexplained variance in animal choices is likely due to measurement error or random mistakes, not more complex cognitive processes.
    • Stimulus-response learning models demonstrate significant predictive power in animal decision-making research.