Context-dependent feature modulation shapes human decision policies in approach-avoidance conflicts

Sequential decisions often involve trade-offs between immediate rewards and future risks, particularly in approach-avoidance contexts. While such behavior is commonly framed in terms of optimization principles, it remains unclear how decision processes adapt across contexts. We developed a sequential foraging task in which participants made binary choices under probabilistic reward and predation risk (threat). The design dissociated reward probability from threat and enabled formal comparison of decision-relevant features (e.g., reward and threat probabilities), a multi-feature policy, and a mathematically optimal policy derived from a fully observable Markov Decision Process. Crucially, the task included two implicitly signaled conditions - approach and avoidance - that differed in how reward and threat information jointly shaped the optimal policy. In both conditions, higher reward probabilities were associated with higher predation risk. However, the balance between these competing factors differed across environments, creating contexts in which the optimal policy either favored approaching or avoiding the higher-threat option. Behavior was analyzed using hierarchical Bayesian models capturing both individual decision features and optimal action values, as well as their modulation by task context. Avoidance contexts selectively altered the weighting of decision features, with reduced reliance on reward probability. In addition, choices showed evidence for increased alignment with optimal state-action values, even after accounting for heuristic features within a shared model. This indicates that behavior more strongly reflected the value structure of the environment under avoidance, consistent with enhanced integration of decision-relevant features. These findings suggest that adaptive behavior in sequential decision-making arises from context-dependent feature reweighting alongside increased alignment with integrated value signals. This provides a parsimonious account of how humans adjust decision-making under threat, without requiring assumptions about discrete strategy shifts.