Sheng and colleagues (PNAS 2020) decomposed loss-averse behavior in accept–reject mixed gambles into a valuation bias (overweighting losses relative to gains), selectively indexed by gaze allocation, and a response bias (a predisposition to reject gambles), selectively indexed by pupil dilation, within a drift-diffusion evidence-accumulation framework. This decomposition, however, is correlational, leaving its causal and mechanistic status unresolved. This problem formulates the source's central open question: (1) whether preferential gaze toward losses causally generates or amplifies the valuation-bias component of loss aversion or merely reflects a prior evaluative asymmetry; and (2) whether the pupil-dilation correlate of response bias reflects locus coeruleus–norepinephrine-mediated effort to overcome the default tendency to reject. The question remains open: the closest causal test (Hirmas and Engelmann 2023) manipulated attribute exposure duration rather than gaze, found only partial effects on decision weights, and no significant change in loss aversion, while the noradrenergic mediation of the pupil-linked response bias is entirely untested.

Background

Loss aversion — the tendency to avoid losses at the expense of acquiring equivalent gains — is among the most robust anomalies in behavioral decision science, dating to prospect theory (Kahneman and Tversky 1979), which formalizes it as a valuation bias: losses loom larger than gains in the value function, captured by a loss-aversion coefficient λ\lambda above 1. In the standard laboratory task, participants accept or reject mixed gambles (equal chances of winning and losing money); loss-averse behavior is rejection of gambles unless the potential gain is sufficiently larger than the potential loss. An alternative account attributes the same behavior not to distorted valuation but to a response bias: a predisposition to avoid the action that could incur a loss — that is, to reject the gamble and remain with the status quo. Choices alone cannot distinguish these accounts.

Two tools make the distinction tractable. First, the drift-diffusion model (DDM; Ratcliff, Smith, Brown, and McKoon 2016) treats a binary decision as noisy evidence accumulation toward one of two boundaries; it separates the rate of evidence accumulation (drift rate, which reflects the relative evidence for accepting versus rejecting and can encode asymmetric weighting of gains and losses) from the starting point of the accumulation process (a prior bias toward one boundary, i.e., a response bias). Fitting the DDM jointly to choices and response times — typically with hierarchical Bayesian estimation (HDDM) — yields participant-level estimates of valuation bias and response bias. Zhao, Walasek, and Bhatia (2020) showed with choice and response-time data alone that both components contribute to conventionally measured loss aversion; Clay, Clithero, Harris, and Reed (2017) reached related conclusions.

Second, physiological measures provide process-level markers. Eye tracking reveals which attribute (gain or loss) a person inspects and for how long: gaze bias both reflects and influences preference (Shimojo, Simion, Shimojo, and Scheier 2003), and the attentional DDM (aDDM; Krajbich, Armel, and Rangel 2010) proposes that the currently fixated attribute receives amplified weight in evidence accumulation. Pupillometry — the measurement of pupil diameter — indexes effort and arousal: non-luminance-evoked pupil dilation is linked to activity in the locus coeruleus, the brain's main source of norepinephrine (Joshi, Li, Kalwani, and Gold 2016), and decision-related pupil dilation tracks upcoming choice and individual bias (de Gee, Knapen, and Donner 2014). Cavanagh, Wiecki, Kochar, and Frank (2014) showed eye tracking and pupillometry index dissociable latent decision processes. Separately, pharmacological (propranolol; Sokol-Hessner et al. 2015) and positron-emission-tomography work (Takahashi 2013) implicate the noradrenergic system in loss aversion as a whole, and Pachur, Schulte-Mecklenbeck, Murphy, and Hertwig (2018) argued prospect-theory distortions reflect selective attention to outcome information, using mouse-tracking.

Within this landscape, Sheng, Ramakrishnan, Seok, Zhao, Thelaus, Cen, and Platt (2020) coupled the DDM with eye tracking and pupillometry in 94 participants performing accept–reject mixed gambles. They found gaze allocation selectively indexed valuation bias (loss-averse participants preferentially fixated losses), pupil dilation selectively indexed response bias (larger dilation when accepting than rejecting, especially in participants with a stronger reject bias), and the two physiological markers dissociated the two components — a double dissociation validating the decomposition. Their aDDM analysis showed losses were overweighted relative to gains regardless of gaze location, but the overweighting was amplified when gaze was on the loss — suggesting a possible mechanism in which an a priori evaluative asymmetry drives preferential inspection of losses, which in turn further amplifies loss weighting. The authors were explicit that this evidence is correlational: causal manipulations of attention are needed to determine whether gaze drives or merely reflects valuation bias, and the noradrenergic interpretation of the pupil effect remained an inference from the DDM framework and known pupil–locus coeruleus physiology rather than a directly tested mechanism.

Problem Statement

Establish the causal and mechanistic validity of the decomposed account of loss aversion of Sheng et al. (PNAS 2020), in two jointly linked parts that together validate the two channels of the decomposition:

(1) Causal status of attention in valuation bias. Determine whether gaze allocation causally generates or amplifies the valuation-bias component of loss aversion, or merely reflects a pre-existing evaluative asymmetry.

(2) Neurochemical mechanism of the pupil-linked response bias. Test directly whether the pupil-dilation correlate of response bias reflects effort to overcome the default tendency to reject gambles, mediated by locus coeruleus–norepinephrine activity, rather than inferring this mediation from the DDM framework and general pupil–locus coeruleus physiology alone.

The problem is resolved when both parts are settled, each by direct evidence: a positive answer (gaze causally contributes to valuation bias; the pupil effect is noradrenergically mediated) and a well-supported negative answer (e.g., effective gaze manipulation that demonstrably cannot move the valuation-bias component, or evidence against locus coeruleus–norepinephrine mediation) both count as resolutions. Scope note: the source's further directions — generalization to other decision contexts (multi-option, sequential, non-monetary choices) and translation of the gaze/pupil biomarkers into personalized interventions together with their test–retest stability — are open adjacent research programs and are deliberately not folded into this completion boundary.

References

  1. Feng Sheng, Arjun Ramakrishnan, Darsol Seok, Wenjia Joyce Zhao, Samuel Thelaus, Puti Cen, Michael Louis Platt. Decomposing loss aversion from gaze allocation and pupil dilation. Proceedings of the National Academy of Sciences 117(21):11356–11363, 2020. DOI: 10.1073/pnas.1919670117. https://doi.org/10.1073/pnas.1919670117
  2. Feng Sheng, Arjun Ramakrishnan, Darsol Seok, Wenjia Joyce Zhao, Samuel Thelaus, Puti Cen, Michael Louis Platt. Decomposing loss aversion from gaze allocation and pupil dilation. bioRxiv preprint, 2020. DOI: 10.1101/2020.02.27.967711. https://doi.org/10.1101/2020.02.27.967711
  3. Daniel Kahneman, Amos Tversky. Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–292, 1979. DOI: 10.2307/1914185. https://doi.org/10.2307/1914185
  4. S. M. Tom, C. R. Fox, C. Trepel, R. A. Poldrack. The neural basis of loss aversion in decision-making under risk. Science 315(5811):515–518, 2007. DOI: 10.1126/science.1134239. https://doi.org/10.1126/science.1134239
  5. R. Ratcliff, P. L. Smith, S. D. Brown, G. McKoon. Diffusion decision model: Current issues and history. Trends in Cognitive Sciences 20(4):260–281, 2016. DOI: 10.1016/j.tics.2016.01.007. https://doi.org/10.1016/j.tics.2016.01.007
  6. I. Krajbich, C. Armel, A. Rangel. Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience 13(10):1292–1298, 2010. DOI: 10.1038/nn.2635. https://doi.org/10.1038/nn.2635
  7. J. F. Cavanagh, T. V. Wiecki, A. Kochar, M. J. Frank. Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General 143(4):1476–1488, 2014. DOI: 10.1037/a0035813. https://doi.org/10.1037/a0035813
  8. J. W. de Gee, T. Knapen, T. H. Donner. Decision-related pupil dilation reflects upcoming choice and individual bias. Proceedings of the National Academy of Sciences 111(5):E618–E625, 2014. DOI: 10.1073/pnas.1317557111. https://doi.org/10.1073/pnas.1317557111
  9. S. Joshi, Y. Li, R. M. Kalwani, J. I. Gold. Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron 89(1):221–234, 2016. DOI: 10.1016/j.neuron.2015.11.028. https://doi.org/10.1016/j.neuron.2015.11.028
  10. S. Shimojo, C. Simion, E. Shimojo, C. Scheier. Gaze bias both reflects and influences preference. Nature Neuroscience 6(12):1317–1322, 2003. DOI: 10.1038/nn1150. https://doi.org/10.1038/nn1150
  11. T. Pachur, M. Schulte-Mecklenbeck, R. O. Murphy, R. Hertwig. Prospect theory reflects selective allocation of attention. Journal of Experimental Psychology: General 147(2):147–169, 2018. DOI: 10.1037/xge0000406. https://doi.org/10.1037/xge0000406
  12. Wenjia Joyce Zhao, Lukasz Walasek, Sudeep Bhatia. Psychological mechanisms of loss aversion: A drift-diffusion decomposition. Cognitive Psychology 123:101331, 2020. DOI: 10.1016/j.cogpsych.2020.101331. https://doi.org/10.1016/j.cogpsych.2020.101331
  13. S. N. Clay, J. A. Clithero, A. M. Harris, C. L. Reed. Loss aversion reflects information accumulation, not bias: A drift-diffusion model study. Frontiers in Psychology 8:1708, 2017. DOI: 10.3389/fpsyg.2017.01708. https://doi.org/10.3389/fpsyg.2017.01708
  14. Stephanie M. Smith, Ian Krajbich. Gaze amplifies value in decision making. Psychological Science 30(1):116–128, 2019. DOI: 10.1177/0956797618810521. https://doi.org/10.1177/0956797618810521
  15. Xiao-Yang Sui, Hong-Zhi Liu, Li-Lin Rao. The timing of gaze-contingent decision prompts influences risky choice. Cognition 195:104077, 2020. DOI: 10.1016/j.cognition.2019.104077. https://doi.org/10.1016/j.cognition.2019.104077
  16. Alejandro Hirmas, Jan B. Engelmann. Impulsiveness moderates the effects of exogenous attention on the sensitivity to gains and losses in risky lotteries. Journal of Economic Psychology 95:102600, 2023. DOI: 10.1016/j.joep.2023.102600. https://doi.org/10.1016/j.joep.2023.102600
  17. Alejandro Hirmas, Jan B. Engelmann, Joël van der Weele. Individual and contextual effects of attention in risky choice. Experimental Economics 27(5):1211–1238, 2024. DOI: 10.1007/s10683-024-09849-7. https://doi.org/10.1007/s10683-024-09849-7
  18. Feng Sheng, Ruining Wang, Zexian Liang, Xiaoyi Wang, Michael L. Platt. The art of the deal: Deciphering the endowment effect from traders' eyes. Science Advances 9(34):eadf2115, 2023. DOI: 10.1126/sciadv.adf2115. https://doi.org/10.1126/sciadv.adf2115
  19. Shaozhi Nie, Muzhi Wang, Jian Li, Huan Luo, Hang Zhang. The neural dynamics of loss aversion. Imaging Neuroscience 1, 2023. DOI: 10.1162/imag_a_00047. https://doi.org/10.1162/imag_a_00047
  20. Jinlian Wang, Chang Liu, Xiang Li, Yuanyuan Gao, Weipeng Jin, Pinchun Wang, Xuyi Chen, Qiang Wang. Neural and computational mechanisms of loss aversion in smartphone addiction. Cerebral Cortex 35(6), 2025. DOI: 10.1093/cercor/bhaf150. https://doi.org/10.1093/cercor/bhaf150
  21. Blair RK Shevlin, Rachael Gwinn, Aidan Makwana, Ian Krajbich. Overt visual attention modulates decision-related signals in the frontal cortex. eLife 14, 2026. DOI: 10.7554/eLife.103846. https://doi.org/10.7554/eLife.103846
  22. Xu He, Boyu Qiu, Yuting Deng, Zhengxinyue Wang, Xinyu Cao, Xiaoyu Zheng, Jianjun Zhu, Wei Zhang. Material hardship predicts response bias in loss-averse decisions: The roles of anxiety and cognitive control. The Journal of Psychology 158(4):309–324, 2024. DOI: 10.1080/00223980.2023.2296946. https://doi.org/10.1080/00223980.2023.2296946
  23. Peter Sokol-Hessner, Sandra F. Lackovic, Russell H. Tobe, Colin F. Camerer, Bennett L. Leventhal, Elizabeth A. Phelps. Determinants of propranolol's selective effect on loss aversion. Psychological Science 26(7):1123–1130, 2015. DOI: 10.1177/0956797615582026. https://doi.org/10.1177/0956797615582026
  24. H. Takahashi, S. Fujie, C. Camerer, R. Arakawa, H. Takano, F. Kodaka, H. Matsui, T. Ideno, S. Okubo, K. Takemura, M. Yamada, Y. Eguchi, T. Murai, Y. Okubo, M. Kato, H. Ito, T. Suhara. Norepinephrine in the brain is associated with aversion to financial loss. Molecular Psychiatry 18(1):3–4, 2013. DOI: 10.1038/mp.2012.7. https://doi.org/10.1038/mp.2012.7
  25. Jia Jin, Qin Xiao, Yuxuan Liu, Ting Xu, Qiang Shen. Test-retest reliability of decisions under risk with outcome evaluation: evidence from behavioral and event-related potentials (ERPs) measures in 2 monetary gambling tasks. Cerebral Cortex 35(3), 2025. DOI: 10.1093/cercor/bhaf058. https://doi.org/10.1093/cercor/bhaf058