2026-10-12T05:42:27Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34036546 2026-09-30T17:52:30Z category_15 category_24 category_45 category_734 category_64 portal_5 item_type_1 month_year_09_2026

Model comparison and context-dependent feature reweighting. 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 (A) PSIS-LOO model comparison across candidate models. Bars show differences in expected log predictive density () relative to the best-performing model (multi-feature policy). The multi-feature policy outperformed all single-feature and alternative heuristic models, indicating that behavior is best captured by a combination of task-relevant features. Asterisks (*) denote features that were included in the multi-feature policy. (B) Posterior estimates (mean 95% credible intervals) from the unified hierarchical model showing main effects and condition-dependent modulation of decision features on foraging behavior (logit scale). Positive values indicate increased probability of foraging. Avoidance contexts selectively modulated feature weights, including reduced sensitivity to and increased influence of -values. A full overview of all model effects, including block modulation and feature interactions, is provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1014793#pcbi.1014793.s002" target="_blank">S1 Fig</a>. (C) Marginal effects of condition on (left) and -values (right). Lines show predicted probability of foraging (mean 95% credible intervals) for approach and avoidance conditions. Under avoidance, the influence of is reduced, whereas sensitivity to -values is increased, indicating stronger alignment with integrated value signals. Rug plots indicate the distribution of observed data. 2026-09-30T18:00:31Z Image Figure 10.1371/journal.pcbi.1014793.g004 https://figshare.com/articles/figure/_p_Model_comparison_and_context-dependent_feature_reweighting_p_/34036546 CC BY 4.0