<p>Each ablation removes or replaces a single mechanism of the joint model while holding all others fixed, isolating that mechanism’s contribution.</p>

<p><b>(A-C)</b> Ablation experiments show that PADDL provides better fits and identifies key diagnostic RT patterns. <b>(A)</b> Accuracy (<i>left</i>) and RT trends (<i>right</i>) for the model which uses the estimated learning rate from the choice-only softmax model and the model (model with static decision bounds, i.e., without proactive control). The PADDL trends are plotted in dashed lines for comparison and those from the ablation/alternate models are plotted in solid lines.

Static bounds fail to capture the large differences in RTs by set size, whereas softmax produces progressively faster RTs over learning particularly for higher set sizes. <b>(B)</b> Summary statistic showing RT discrepancy by set size in late trials for PADDL compared to the softmax-recovered, overestimated learning rate. <b>(C)</b> Summary statistic showing RT discrepancy by set size in early trials for PADDL compared to the static bounds model. <b>(D-E)</b> Posterior predictive checks show that PADDL captures empirical RT patterns diagnostic of RL learning rate and proactive decision threshold adjustment, as identified in panels B and <b>C. (D)</b> RT differences in empirical and simulated data between low- and high- participant subgroups (in HC) during late/asymptotic learning phase. <b>(E)</b> RT differences in empirical and simulated data between high- and low-<i>m</i> participant subgroups (in HC) during early learning phase.