2026-10-14T05:20:59Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34036549 2026-09-30T17:52:31Z category_15 category_24 category_45 category_734 category_64 portal_5 item_type_1 month_year_09_2026

<p>Model recovery analysis.</p> Sergej A. E. Golowin (25145509) Niall W. Duncan (9177024) Faizan Shaikh (25145512) Christoph W. Korn (11657787) Neuroscience Evolutionary Biology Sociology Biological Sciences not elsewhere classified Cancer without requiring assumptions enabled formal comparison choices showed evidence sequential foraging task integrated value signals approach &# 8211 g ., reward humans adjust decision avoidance &# 8211 threat probabilities ), higher reward probabilities optimal action values individual decision features higher predation risk predation risk action values value structure sequential decision optimal policy relevant features decision features reward probability probabilistic reward xlink "> strongly reflected shared model remains unclear reduced reliance policies based parsimonious account optimization principles increased alignment immediate rewards future risks findings suggest feature policy enhanced integration creating contexts commonly framed avoidance contexts <p>Confusion matrix showing model recovery performance across all candidate policy models. Synthetic datasets were generated from each model using posterior parameter estimates from the empirically fitted hierarchical Bayesian models and subsequently refit using the full candidate model space. Recovery analyses used conditional simulation procedures in which choices were regenerated while preserving the original trial structure and task states. Values indicate the proportion of simulations in which a given generating model (rows) was correctly or incorrectly identified as the best-fitting model (columns) based on PSIS-LOO predictive performance. Recovery accuracy was high across all candidate models (0.96–1.00), with only minor misclassification of some simple single-feature models as the more flexible multi-feature policy. Importantly, the multi-feature policy and -based model showed perfect recovery, indicating strong discriminability between the principal competing accounts examined in the study. Although some recovery fits involving highly flexible models applied to data generated by simpler single-feature policies produced elevated values or reduced effective sample sizes, overall model discriminability remained robust.</p> 2026-09-30T18:00:31Z Image Figure 10.1371/journal.pcbi.1014793.g005 https://figshare.com/articles/figure/_p_Model_recovery_analysis_p_/34036549 CC BY 4.0