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从注视分配和瞳孔扩张分解损失厌恶。

Decomposing loss aversion from gaze allocation and pupil dilation.

机构信息

Wharton Neuroscience Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104;

Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104.

出版信息

Proc Natl Acad Sci U S A. 2020 May 26;117(21):11356-11363. doi: 10.1073/pnas.1919670117. Epub 2020 May 8.

Abstract

Loss-averse decisions, in which one avoids losses at the expense of gains, are highly prevalent. However, the underlying mechanisms remain controversial. The prevailing account highlights a valuation bias that overweighs losses relative to gains, but an alternative view stresses a response bias to avoid choices involving potential losses. Here we couple a computational process model with eye-tracking and pupillometry to develop a physiologically grounded framework for the decision process leading to accepting or rejecting gambles with equal odds of winning and losing money. Overall, loss-averse decisions were accompanied by preferential gaze toward losses and increased pupil dilation for accepting gambles. Using our model, we found gaze allocation selectively indexed valuation bias, and pupil dilation selectively indexed response bias. Finally, we demonstrate that our computational model and physiological biomarkers can identify distinct types of loss-averse decision makers who would otherwise be indistinguishable using conventional approaches. Our study provides an integrative framework for the cognitive processes that drive loss-averse decisions and highlights the biological heterogeneity of loss aversion across individuals.

摘要

规避损失的决策,即人们不惜以牺牲收益为代价来避免损失,非常普遍。然而,其潜在机制仍存在争议。主流观点强调了一种估值偏差,即相对于收益,损失被高估,但另一种观点则强调了一种避免涉及潜在损失的选择的反应偏差。在这里,我们结合计算过程模型、眼动追踪和瞳孔测量,为导致接受或拒绝具有相同赢钱和输钱概率的赌博的决策过程开发了一个基于生理的框架。总体而言,规避损失的决策伴随着对损失的优先注视,以及对接受赌博的瞳孔扩大。使用我们的模型,我们发现注视分配选择性地反映了估值偏差,而瞳孔扩张则选择性地反映了反应偏差。最后,我们证明我们的计算模型和生理生物标志物可以识别出不同类型的规避损失的决策者,而使用传统方法则无法区分这些决策者。我们的研究为驱动规避损失决策的认知过程提供了一个综合框架,并强调了个体之间规避损失的生物异质性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af9/7260957/d52049de942d/pnas.1919670117fig01.jpg

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