4.7 Article

Affective decision making under uncertainty during a plausible aviation task: An fMRI study

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NEUROIMAGE
卷 71, 期 -, 页码 19-29

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ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2012.12.060

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Reward and punishment; Dorsolateral prefrontal cortex; Risk-taking; Emotion; Aviation safety

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In aeronautics, plan continuation error (PCE) represents failure to revise a flight plan despite emerging evidence suggesting that it is no longer safe. Assuming that PCE may be associated with a shift from cold to hot reasoning, we hypothesized that this transition may result from a large range of strong negative emotional influences linked with the decision to abort a landing and circle for a repeat attempt, referred to as a go-around. We investigated this hypothesis by combining functional neuroimaging with an ecologically valid aviation task performed under contextual variation in incentive and situational uncertainty. Our goal was to identify regional brain activity related to the sorts of conservative or liberal decision-making strategies engaged when participants were both exposed to a financial payoff matrix constructed to bias responses in favor of landing acceptance, while they were simultaneously experiencing maximum levels of uncertainty related to high levels of stimulus ambiguity. Combined with the observed behavioral outcomes, our neuroimaging results revealed a shift from cold to hot decision making in response to high uncertainty when participants were exposed to the financial incentive. Most notably, while we observed activity increases in response to uncertainty in many frontal regions such as dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), less overall activity was observed when the reward was combined with uncertainty. Moreover, participants with poor decision making, quantified as a lower discriminability index d', exhibited riskier behavior coupled with lower activity in the right DLPFC. These outcomes suggest a disruptive effect of biased financial incentive and high uncertainty on the rational decision-making neural network, and consequently, on decision relevance. (C) 2013 Elsevier Inc. All rights reserved.

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