2026-10-14T05:49:57Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34036277 2026-09-30T17:47:37Z category_15 category_734 category_931 category_106 portal_5 item_type_1 month_year_09_2026

PADDL reveals RL, WM and proactive control differences between HC and SCZ. 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 (A) Posterior predictive checks for SCZ. (Left) Learning curves for each set size plotted in terms of accuracy for empirical data and model simulations. The model simulations were generated by sampling the posterior and using the post hoc absolute fit method. (Right) Learning curves for each set size plotted in terms of RT trends for empirical data and model simulations. Shaded regions show 94% HDI ranges of the group mean. (B) Group-level differences between HC and SCZ. Posterior distributions of the group-level means are shown for the three main contrasts – proactive adjustment of decision threshold m, RL learning rate and WM decay . Note that denotes the back-transformed (via exp function) parameter space. (C) (Left) RT differences (or slope) in empirical and simulated data between HC and SCZ groups over the course of learning. The RT slopes across learning are characterized by learning rates – HC group with higher learning rate shows steeper slope than SCZ. The differences in slope increase with set size. The trend in gray shows the same effect on simulated data when SCZ learning rate is adjusted to match to the HC learning rate. Negating the learning rate differences between HC and SCZ ablates these learning rate induced RT slope effects across learning. (Right) RT differences (or slope) in empirical and simulated data between high- and low- participant subgroups (in HC). The error bars on simulated trends denote 94% HDI ranges. 2026-09-30T17:56:47Z Image Figure 10.1371/journal.pcbi.1014796.g005 https://figshare.com/articles/figure/_p_A_Posterior_predictive_checks_for_SCZ_p_/34036277 CC BY 4.0