Erdman, A., Gouwy, A., et al. (2025).
Nature Communications, 16(1).
Abstract
The tendency to embrace or avoid risk varies across and within individuals, with significant consequences for economic behavior and mental health. Such variations can partially be explained by differences in the relative weights given to potential gains and losses. Applying this insight to real-life decisions, however, is complicated because such decisions are often based on prior learning experiences. Here, we ask which cognitive process—decision-making or learning—determines the weighting of gains or losses? Over 28 days, 100 participants engaged in a longitudinal decision task wherein choices were based on prior learning. Computational modeling of participants’ choices revealed that changes in risk-taking are primarily explained by changes in how learning, not decisions, weight gains and losses. Moreover, inferred changes in learning manifested in participants’ neural and physiological learning signals in response to outcomes. We conclude that in experience-based decisions, learning plays a primary role in governing risk-taking behavior.
Here are some thoughts:
This research is important for psychologists because it demonstrates that risk-taking behavior in experience-based decisions is primarily shaped by prior learning rather than the decision-making process itself. By tracking participants over 28 days, the study found that changes in risk preferences were largely due to how individuals learned from gains and losses, rather than how they evaluated options at the moment of decision. This insight helps psychologists understand the cognitive mechanisms behind risk behavior, which is relevant to economic choices, mental health, and conditions like addiction or anxiety, where distorted learning from outcomes can lead to maladaptive decisions.
The study also links learning biases to neural and physiological signals, such as heart rate and EEG responses to negative outcomes, offering psychologists measurable markers of how people update their evaluations over time. These findings support the development of more accurate models of behavior that incorporate learning dynamics, rather than assuming static risk preferences. This has implications for clinical interventions, as it suggests that therapies targeting how individuals learn from experiences—rather than just how they make decisions—could be more effective in treating disorders involving risky behavior.
Moreover, the research addresses the "description-experience gap" by showing that real-world, experience-based decisions differ fundamentally from those made based on described probabilities. This highlights the need for psychological research and interventions to focus on experiential learning processes to better reflect real-life decision-making contexts. Overall, the study advances psychological science by clarifying the central role of learning in shaping risk behavior and offering new directions for understanding and modifying human behavior in both healthy and clinical populations.
