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

Humans adapt decision-making in approach-avoidance conflicts by reweighting features and aligning with optimal values, especially under threat, with a 2-fold increase in value integration.

• Why it matters: Understanding how humans modify decision strategies in risky, real-world scenarios is crucial for insights into adaptive behavior and risk management, yet the mechanisms remain unclear.
• What they did: A sequential foraging task was designed with probabilistic reward and threat, allowing comparison of decision features, policies, and optimal strategies across approach and avoidance contexts using hierarchical Bayesian models.
• The result: Findings show that in avoidance contexts, humans reduce reliance on reward probability and better align choices with integrated environmental values, enabling more adaptive decision policies under threat.