Individuals can explicitly understand the causal structure of an environment yet fail to use that knowledge to guide action selection¶

Claim · All claims
Evidence · 1 study · causal r2 · q2
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Children demonstrated explicit awareness of the hidden agents' intervention structure but did not implicitly use it to guide reinforcement learning, suggesting dissociable learning mechanisms for acquiring causal understanding versus using causal knowledge for action selection. → 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

q2 · i? · causal · r2

The authors' interpretation of the combined attribution and modeling findings: children's explicit attributions matched task structure, yet their choices followed a one-learning-rate model. The article argues different learning mechanisms may underlie acquiring causal understanding versus using it for policy selection.

"Our results demonstrate that individuals can learn about and gain an explicit understanding of the causal structure of their environments but fail to use that knowledge to guide action selection"

Discussion¶

Related Claims¶

• Participants aged 7–25 attribute positive and negative outcomes to hidden agents in line with the true causal structure of each environment — related
• Adolescents flexibly estimate hidden-agent intervention probability from experience, less tied to their explicitly reported beliefs than adults — related