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Data · dataset · 2026

<p>(A) Posterior predictive checks for SCZ.</p>

Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.1371/journal.pcbi.1014796.g005

<p><i>(Left)</i> Learning curves for each set size plotted in terms of accuracy for empirical data and model simulations.

Description

The model simulations were generated by sampling the posterior and using the post hoc absolute fit method. <i>(Right)</i> 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>(B)</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 <i>m</i>, RL learning rate and WM decay . Note that denotes the back-transformed (via <i>exp</i> function) parameter space. <b>(C)</b> <i>(Left)</i> 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.

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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. <i>(Right)</i> 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.</p>

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ZivaHuboai:figshare.com:article/3403627712 d agoJSON v1
Deakin Research Onlineoai:figshare.com:article/3403627712 d agoJSON v1
DMU Figshareoai:figshare.com:article/3403627712 d agoJSON v1
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