Figure 3.

Individual parameter estimates and DSST performance: Maximum posterior parameter values of the dual-system reinforcement learning model for each participant as a function of performance on the Digit Symbol Substitution Test (DSST) are displayed. The lines represent predictions from linear regressions of each model parameter on DSST scores, with 95% confidence intervals (CI). (A–D) Regression lines and CI in unbounded fitting-space were transformed to model-space for plotting by passing them through the inverse-logit function. (A) Best-fitting individual parameter values for the weighting parameter ω, which determines the balance between model-free (weight = 0) and model-based (weight = 1) control. (B) Regression of best-fitting weighting parameter values on the interaction between DSST scores × working memory span (median-split factor). (C) Best-fitting parameter values for the second-stage learning rate α₂. (D) The lambda (λ) parameter determines update of model-free step 1 action values by step 2 prediction errors. (E) Repetition factor, p, indicates how strongly individuals tend to repeat previous actions.