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Related Experiment Video

Updated: Apr 15, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Exploring the knowledge behind predictions in everyday cognition: an iterated learning study.

Rachel G Stephens¹, John C Dunn², Li-Lin Rao³

¹School of Psychology, University of Adelaide, North Terrace, Adelaide, SA, 5005, Australia.

Memory & Cognition

|April 4, 2015

Summary

This summary is machine-generated.

Accurate predictions rely on explicit experience with relevant quantities, not just general domain knowledge. People struggle with unfamiliar domains and even familiar ones when specific quantity experience is lacking.

Keywords:

Bayesian inference Cross-cultural comparison Everyday reasoning Iterated learning

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Area of Science:

Cognitive Psychology
Decision Science

Background:

Accurate event prediction is challenging but crucial.
People often make surprisingly accurate predictions based on real-world quantity distributions.

Purpose of the Study:

Investigate the role of domain experience in prediction accuracy.
Assess human predictive accuracy in both familiar and unfamiliar domains.

Main Methods:

Utilized an iterated learning procedure across three experiments.
Compared prediction accuracy between Australian and Chinese participants for familiar and unfamiliar quantities.
Follow-up experiments focused on bus route prediction strategies.

Main Results:

Predictions were generally accurate but faltered in selectively unfamiliar domains.
Inaccurate predictions occurred even for familiar domains (e.g., local bus routes) when specific quantity experience was limited.
Extrapolation from related knowledge, rather than direct application, was used for unfamiliar domains.

Conclusions:

Explicit experience with relevant quantities, not just general domain familiarity, is key for accurate predictions.
Prediction errors stem from both estimation in familiar domains and extension in unfamiliar ones.
Understanding knowledge extrapolation is vital for improving predictive accuracy.