2026-10-12T00:07:52Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34036271 2026-09-30T17:47:34Z category_15 category_734 category_931 category_106 portal_5 item_type_1 month_year_09_2026

<p>Each ablation removes or replaces a single mechanism of the joint model while holding all others fixed, isolating that mechanism’s contribution.</p> Krishn Bera (25145477) Alexander Fengler (24611274) Megan A. Boudewyn (25145480) Cameron S. Carter (5421878) Molly A. Erickson (5954450) James M. Gold (25145483) Steven J. Luck (25145486) J. Daniel Ragland (19372260) Andrew P. Yonelinas (25145489) Angus W. MacDonald III (25145492) Deanna M. Barch (6759263) Michael J. Frank (8703909) Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy support robust long novel neurocognitive process limited working memory increase response caution flexibly adapt behavior despite added complexity proactive control mechanisms oriented computational phenotyping computational advances support manipulating wm demands increased wm load slowed incremental rl cognitive control processes rl learning rates disentangle separable learning cognitive control computational modeling wm vs yielded accurate test phase term retention schizophrenia revealed sample test sample prediction rlwm paradigm previous choice people rely often equally given choice decision making <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. The difference in RT is calculated between the median-split, high and low parameter participant groups as determined from the PADDL model fits. The error bars on simulated trends denote 94% HDI ranges.</p> 2026-09-30T17:47:28Z Image Figure 10.1371/journal.pcbi.1014796.g003 https://figshare.com/articles/figure/_p_Each_ablation_removes_or_replaces_a_single_mechanism_of_the_joint_model_while_holding_all_others_fixed_isolating_that_mechanism_s_contribution_p_/34036271 CC BY 4.0