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Decomposing loss aversion from gaze allocation and pupil dilation.


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.

SUBMITTER: Sheng F 

PROVIDER: S-EPMC7260957 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Decomposing loss aversion from gaze allocation and pupil dilation.

Sheng Feng F   Ramakrishnan Arjun A   Seok Darsol D   Zhao Wenjia Joyce WJ   Thelaus Samuel S   Cen Puti P   Platt Michael Louis ML  

Proceedings of the National Academy of Sciences of the United States of America 20200508 21


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 leadin  ...[more]

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