Subjective value-weights on benefit and risk in human neurocomputation changes between conservative and risky decision-making

The substantial variability in people's risky decision-making constitutes a compelling and interesting topic. It is particularly fascinating to note that even when confronted with identical circumstances, the same individual tends to exhibit variability in their risk decisions, oscillating between a propensity for heightened risk-taking and a more cautious approach. The current study investigated human computational neural mechanisms of risky and conservative decision-making in sequential risk-taking tasks through integrative analysis of dynamic-updating model parameters and EEG signatures of decision processes. The model revealed that, during risky decision-making, subjective benefit values exerted a stronger influence, whereas subjective risk values dominated conservative decisions. Emotional factors, particularly regret-related emotion, primarily modulated conservative decisions rather than risky decisions. By applying time-resolved multivariate pattern analyses to EEG data and identifying the peak decoding accuracy as a marker of stage completion, we dissociated valuation from the selection stage in decision-making. The peak decoding accuracy during the valuation stage demonstrated higher and more stable in risky decision-making compared to conservative decision-making. Further analysis suggested that peak decoding accuracy in risky decision-making may reflect a relatively balanced consideration between risk and benefit values. Notably, emotional factors had less impact on the selection stage of risky decisions, but significantly affected the selection stage of conservative decisions. These findings elucidate the adaptability and dynamic architecture of neurocomputation across risky and conservative decisions.