Participants aged 7–25 attribute positive and negative outcomes to hidden agents in line with the true causal structure of each environment¶

Claim · All claims
Evidence · 1 study · causal r2 · q3
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? Trial-wise attributions showed a significant reward outcome by territory interaction, with negative outcomes attributed most to the Robber and positive outcomes most to the Millionaire. → Alexandra O. Cohen 2020

Evidence¶

Alexandra O. Cohen 2020¶

Alexandra O. Cohen, Kate Nussenbaum, Hayley M. Dorfman, Samuel J. Gershman and Catherine A. Hartley. (2020). The rational use of causal inference to guide reinforcement learning strengthens with age. npj Science of Learning 5:16. https://doi.org/10.1038/s41539-020-00075-3

q3 · i? · causal · r2

Logistic mixed-effects analysis of trial-wise hidden-agent attributions in the gold-mining task (N = 90, ages 7–25). The interaction between reward outcome and territory was significant, "p < 0.0001", confirming attributions tracked each environment's structure.

"there was a signi ficant reward outcome by territory interaction ( χ2(2, N = 90) = 87.69, p < 0.0001) indicating that participants attributed negative outcomes most often to the Robber, to a lesser extent to the Sheriff, and rarely to the Millionaire"

Discussion¶

Related Claims¶

• Older participants learn faster across all territories while younger participants learn better in the environment where a hidden agent generates positive outcomes — related
• Adolescents flexibly estimate hidden-agent intervention probability from experience, less tied to their explicitly reported beliefs than adults — related
• Children's reinforcement learning is best fit by a one-learning-rate model, whereas adolescents and adults are best fit by Bayesian models incorporating hidden-agent beliefs — related
• Individuals can explicitly understand the causal structure of an environment yet fail to use that knowledge to guide action selection — related