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

The useful part

Korn 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).

How it works

• Crucially, the task included two implicitly signaled conditions – approach and avoidance – that differed in how reward and threat information jointly shaped the optimal policy.
• 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.
• choices showed evidence for increased alignment with optimal state–action values, even after accounting for heuristic features within a shared model.
• In both conditions, higher reward probabilities were associated with higher predation risk.
• Behavior was analyzed using hierarchical Bayesian models capturing both individual decision features and optimal action values, as well as their modulation by task context.

What to take from it

Avoidance contexts selectively altered the weighting of decision features, with reduced reliance on reward probability. This indicates that behavior more strongly reflected the value structure of the environment under avoidance, consistent with enhanced integration of decision-relevant features.