Computational modeling shows that outcome-irrelevant learning is quite reliable across sessions, yet not everyone does this equally. Working memory capacity strongly predicts outcome-irrelevant learning. Suggesting working memory is central for maintaining a causal structure guiding learning.

To examine the possibility that participants are not convinced by the instructions, in Experiment 2, we gave 600 trials across three days, allowing them to infer that locations should be neglected. But, we find they keep assigning credit to outcome-irrelevant locations.

So we created a “magical forest” narrative, telling participants that the offered leaves are randomly driven to their locations by the wind. We find participants still show outcome-irrelevant learning, leading them to choose suboptimally and win a smaller money bonus.

Excited to be in #NeurIPS2024

Visit my poster at the Behavioral ML workshop or just come say Hi

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