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Updated: Jun 24, 2026
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Appetitive Associative Olfactory Learning in Drosophila Larvae
Published on: February 18, 2013
Associative learning explains "intuitive statistics" in animals
Stefano Ghirlanda1, Janelle Mendoza2
1DataWorks.
Psychological Review
|June 22, 2026
Summary
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.
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.
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