Decomposing loss aversion from gaze allocation and pupil dilation

Feng Sheng, Arjun Ramakrishnan, Darsol Seok, Wenjia Joyce Zhao, Samuel Thelaus, Puti Cen, Michael Louis Platt, Feng Sheng, Arjun Ramakrishnan, Darsol Seok, Wenjia Joyce Zhao, Samuel Thelaus, Puti Cen, Michael Louis Platt

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.

Keywords: drift-diffusion model; gaze allocation; loss aversion; pupil dilation.

Copyright © 2020 the Author(s). Published by PNAS.

Figures

Fig. 1.
Fig. 1.
Behavior. (A, Top) Gambling task. Gambles were not resolved after decisions. (Middle) Probability of acceptance (B) and response times (C) across gambles. (Bottom) Illustration of the DDM for the gambling task (D) and estimates of valuation bias and response bias for each individual participant (E). Each dot represents a participant: those to the right of the green dashed line displayed valuation bias of overweighing loss relative to gains, and those above the purple dashed line displayed a response bias to reject gambles. The green dot (P69) and the purple dot (P46) in quadrant 1 indicate the two example participants illustrated in SI Appendix, Fig. S6 and SI Appendix, Fig. S7.
Fig. 2.
Fig. 2.
Gaze. (A) Probability of gaze allocation to gains and losses. (B) Gaze-loss ratio across gambles. (C) Gaze-loss ratio predicted by magnitude of gain and loss across trials. Black and gray bars denote negative and positive beta values, respectively. (D) Gaze-loss ratio predicted by valuation bias and response bias across participants. Error bars indicate SEs. ***: P < 0.001; ns: nonsignificant, P > 0.1.
Fig. 3.
Fig. 3.
Pupil. (A) Change in pupil size around the time of the decision (t = 0) to accept (red) vs. reject (blue) gambles. (B) Correlation between probability of gamble acceptance and accept–reject pupil-size differential. (C) Influence of choice, gain, and loss on decision-related pupil size across trials. Black and gray bars denote negative and positive beta values, respectively. (D) Accept–-reject pupil-size differential predicted by valuation bias and response bias. Error bars indicate SEs. ***: P < 0.001; **: P < 0.01; *: P < 0.05; †: P < 0.1; ns: nonsignificant, P > 0.1.

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Source: PubMed

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