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A comparison between Bayesian and heuristic machine-learning models that learn probability information from experience under uncertainty.

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The Influence of Sample Size on Preferences from Experience

Contributing Authors

Janine C. Hoffart, Jana B Jarecki, Gilles Dutilh, & Jörg Rieskamp

Dates

Paper published in 2021.

Abstract

People often learn from experience about the distribution of outcomes of risky options. Typically, people draw small samples, when they can actively sample information from risky gambles to make decisions. We examine how the size of the sample that people experience in decision from experience affects their preferences between risky options. In two studies (N = 40 each), we manipulated the size of samples that people could experience from risky gambles and measured subjective selling prices and the confidence in selling price judgements after sampling. The results show that, on average, sample size influenced neither the selling prices nor confidence. However, cognitive modelling of individual-level learning showed that around half of the participants could be classified as Bayesian learners, whereas the other half adhered to a frequentist learning strategy and that if learning was cognitively simpler more participants adhered to the latter. The observed selling prices of Bayesian learners changed with sample size as predicted by Bayesian principles, whereas sample size affected the judgements of frequentist learners much less. These results illustrate the variability in how people learn from sampled information and provide an explanation for why sample size often does not affect judgements.

Publication

Hoffart, J. C., Jarecki, J. B., Dutilh, G., & Rieskamp, J. (2022). The influence of sample size on preferences from experience. Quarterly Journal of Experimental Psychology, 75(1), 1-17. https://doi.org/10.1177/17470218211044013

Funding

University of Basel.

Notes

None.

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A comparison between Bayesian and heuristic machine-learning models that learn probability information from experience under uncertainty.

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