<p>Model recovery analysis.</p>

Synthetic datasets were generated from each model using posterior parameter estimates from the empirically fitted hierarchical Bayesian models and subsequently refit using the full candidate model space. Recovery analyses used conditional simulation procedures in which choices were regenerated while preserving the original trial structure and task states.

Values indicate the proportion of simulations in which a given generating model (rows) was correctly or incorrectly identified as the best-fitting model (columns) based on PSIS-LOO predictive performance. Recovery accuracy was high across all candidate models (0.96–1.00), with only minor misclassification of some simple single-feature models as the more flexible multi-feature policy. Importantly, the multi-feature policy and -based model showed perfect recovery, indicating strong discriminability between the principal competing accounts examined in the study.