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Data · dataset · 2026

<p>Structure of the decision space and behavioral relevance of optimal policy.</p>

Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.1371/journal.pcbi.1014793.g003

<p><b>(A)</b> Distribution of the optimal policy value differences () derived from the Markov Decision Process (MDP), quantifying choice difficulty.

Description

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.

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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>

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ZivaHuboai:figshare.com:article/3403654313 d agoJSON v1
Deakin Research Onlineoai:figshare.com:article/3403654313 d agoJSON v1
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