2026-10-12T14:39:59Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34036534 2026-09-30T17:52:25Z category_15 category_24 category_45 category_734 category_64 portal_5 item_type_6 month_year_09_2026

Exploratory hierarchical policy-arbitration analysis examining continuous alignment with the multi-feature (MF) policy versus integrated -value representations across individuals and conditions. 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 hierarchical Bayesian continuous mixture model was used to estimate trial-wise arbitration weights () reflecting the relative contribution of the MF versus -based policy representations to observed choices. At the trial level, action probabilities were modeled as a probabilistic mixture of the two policy predictions: , where and denote the trial-wise action probabilities predicted by the MF and -based models, respectively, and represents the arbitration weight favoring the MF policy. Importantly, the present analysis was designed to examine policy-level arbitration rather than feature-level modulation. Accordingly, both policy representations were first estimated independently using the hierarchical models from the primary analyses, and the resulting posterior action probabilities were then treated as fixed policy predictions within the arbitration model. This approach allowed arbitration tendencies to be examined while treating the independently estimated policy predictions as fixed inputs to the arbitration model. The -based policy representation was operationalized using posterior predictions derived from the fitted Markov Decision Process (MDP) model based on integrated state–action value differences (). The MF policy representation was operationalized using posterior predictions derived from the fitted hierarchical multi-feature model integrating reward probability (), threat probability (), and the boundary-condition features binary energy state (BES) and wait when safe (WWS). Arbitration weights were estimated hierarchically across participants and task conditions (approach vs. avoidance) while additionally controlling for block number and trial-wise policy similarity. Trial-wise policy similarity was quantified using the absolute difference between the MF and -based policy predictions (), thereby accounting for trial-wise variation in overlap between the competing policy accounts. Importantly, policy-similarity distributions were highly comparable across conditions, indicating that the observed condition-dependent arbitration effects were unlikely to be explained solely by differences in overlap between the candidate policy representations across approach and avoidance environments. (A) Distribution of baseline arbitration weights across participants. Most participants showed greater overall alignment with the MF policy representation, consistent with the primary model-comparison analyses reported in the main text. However, arbitration weights formed a continuous distribution without evidence for sharply separable strategy classes, suggesting graded inter-individual variability in relative policy alignment. (B) Posterior probabilities of condition-dependent shifts toward increased -based policy alignment in avoidance contexts. Most participants showed posterior probabilities substantially above 0.5, indicating a consistent directional tendency toward greater alignment with integrated-value representations under avoidance conditions despite substantial overlap between the MF and -based policy predictions. (PDF) 2026-09-30T18:00:31Z Text Journal contribution 10.1371/journal.pcbi.1014793.s007 https://figshare.com/articles/journal_contribution/_p_Exploratory_hierarchical_policy-arbitration_analysis_examining_continuous_alignment_with_the_multi-feature_MF_policy_versus_integrated_-value_representations_across_individuals_and_conditions_p_/34036534 CC BY 4.0