2026-10-14T06:49:56Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34036543 2026-09-30T17:52:29Z category_15 category_24 category_45 category_734 category_64 portal_5 item_type_1 month_year_09_2026

<p>Structure of the decision space and behavioral relevance of optimal policy.</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><b>(A)</b> Distribution of the optimal policy value differences () derived from the Markov Decision Process (MDP), quantifying choice difficulty. Values near zero indicate weak preference between actions, whereas larger absolute values reflect clearer optimal choices. The distribution is concentrated around zero, indicating that most trials involve finely balanced trade-offs. Insets illustrate example distributions for different trial types. <b>(B)</b> State-level decision difficulty () across conditions for the entire theoretical MDP grids (all possible state–action values). Violin plots show similar distributions for avoidance and approach conditions, indicating comparable overall task difficulty. Points denote mean values. <b>(C)</b> Empirical cumulative distribution functions (ECDFs) of for approach and avoidance conditions. The strong overlap between curves indicates highly similar difficulty distributions across contexts. Although the Kolmogorov–Smirnov test was statistically significant (D = 0.04, p < 0.01), the small effect size suggests that differences between conditions are negligible. <b>(D)</b> Relationship between alignment with optimal policy value differences () and behavioral success. Each point represents a participant; the x-axis shows the individual slope () relating to choice, and the y-axis shows the number of successfully completed forests. Greater alignment with optimal values is associated with higher performance (r = 0.49, p = 0.01), supporting the behavioral relevance of the MDP-derived benchmark.</p> 2026-09-30T18:00:31Z Image Figure 10.1371/journal.pcbi.1014793.g003 https://figshare.com/articles/figure/_p_Structure_of_the_decision_space_and_behavioral_relevance_of_optimal_policy_p_/34036543 CC BY 4.0