Journal of Neuroscience · 2009 · 459 citations · 58 references
Life SciencesNeurosciencePsychologyBehavioral NeuroscienceCognitive NeuroscienceDecision TheoryBehavioral Decision-makingDecision Making Under RiskDecision-making ModelsRisk AversionRisk PreferencesRisk-sensitive Decision MakingBlood Oxygenation Level-dependent ContrastBlood Oxygenation Level-dependent ImagingCingulate Cortex FunctionEconomic Decision AnalysisNeural Correlates Of ChoiceRisky BehaviorRisky Decision MakingStriatal Function
Decision making under risk is central to human behavior, and economic theory posits that value, risk, and risk aversion shape choices, yet the role of neural correlates in driving actual decisions remains unclear. Two experiments had participants choose between risky and safe options. BOLD signals in the ventral striatum, anterior cingulate, and inferior frontal gyrus encode value, risk, and risk aversion respectively, and their combined activity accurately predicts participants’ risky versus safe choices, supporting the use of these neural correlates as an ideal observer of decision behavior.
Decision making under risk is central to human behavior. Economic decision theory suggests that value, risk, and risk aversion influence choice behavior. Although previous studies identified neural correlates of decision parameters, the contribution of these correlates to actual choices is unknown. In two different experiments, participants chose between risky and safe options. We identified discrete blood oxygen level-dependent (BOLD) correlates of value and risk in the ventral striatum and anterior cingulate, respectively. Notably, increasing inferior frontal gyrus activity to low risk and safe options correlated with higher risk aversion. Importantly, the combination of these BOLD responses effectively decoded the behavioral choice. Striatal value and cingulate risk responses increased the probability of a risky choice, whereas inferior frontal gyrus responses showed the inverse relationship. These findings suggest that the BOLD correlates of decision factors are appropriate for an ideal observer to detect behavioral choices. More generally, these biological data contribute to the validity of the theoretical decision parameters for actual decisions under risk.
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