Psychological mechanisms of loss aversion: A drift-diffusion decomposition

Zhao, Wenjia; Walasek, Lukasz; Bhatia, Sudeep

doi:10.1016/j.cogpsych.2020.101331

Cited by 38 publications

(40 citation statements)

References 95 publications

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“…Despite much philosophizing about the reasons for risk-averse and loss-averse behavior, relatively less research has focused on how people arrive at those choices [for notable recent exceptions, see refs. (50)(51)(52)]. The standard approach reduces a continuous and dynamic decision process into a static and binary choice outcome (important though that outcome may be) and, in doing so, ignores potentially useful information.…”

Section: Decisions Involving Riskmentioning

confidence: 99%

Using dynamic monitoring of choices to predict and understand risk preferences

Stillman

Krajbich

Ferguson

2020

Proc. Natl. Acad. Sci. U.S.A.

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Navigating conflict is integral to decision-making, serving a central role both in the subjective experience of choice as well as contemporary theories of how we choose. However, the lack of a sensitive, accessible, and interpretable metric of conflict has led researchers to focus on choice itself rather than how individuals arrive at that choice. Using mouse-tracking—continuously sampling computer mouse location as participants decide—we demonstrate the theoretical and practical uses of dynamic assessments of choice from decision onset through conclusion. Specifically, we use mouse tracking to index conflict, quantified by the relative directness to the chosen option, in a domain for which conflict is integral: decisions involving risk. In deciding whether to accept risk, decision makers must integrate gains, losses, status quos, and outcome probabilities, a process that inevitably involves conflict. Across three preregistered studies, we tracked participants’ motor movements while they decided whether to accept or reject gambles. Our results show that 1) mouse-tracking metrics of conflict sensitively detect differences in the subjective value of risky versus certain options; 2) these metrics of conflict strongly predict participants’ risk preferences (loss aversion and decreasing marginal utility), even on a single-trial level; 3) these mouse-tracking metrics outperform participants’ reaction times in predicting risk preferences; and 4) manipulating risk preferences via a broad versus narrow bracketing manipulation influences conflict as indexed by mouse tracking. Together, these results highlight the importance of measuring conflict during risky choice and demonstrate the usefulness of mouse tracking as a tool to do so.

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Section: Decisions Involving Riskmentioning

confidence: 99%

Using dynamic monitoring of choices to predict and understand risk preferences

Stillman

Krajbich

Ferguson

2020

Proc. Natl. Acad. Sci. U.S.A.

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“…This constitutes one divergence between the two frameworks. The aDDM could, however, be modified to produce loss aversion-for instance, by transforming sampled outcomes by a value function that is steeper in the loss than in the gain domain, or by assuming a biased starting point for accumulation (similar to Zhao, Walasek, & Bhatia, 2020).…”

Section: Loss Aversionmentioning

confidence: 99%

Nonlinear probability weighting can reflect attentional biases in sequential sampling.

Zilker

Pachur

2022

Psychological Review

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Nonlinear probability weighting allows cumulative prospect theory (CPT) to account for key phenomena in decision making under risk (e.g., certainty effect, fourfold pattern of risk attitudes). It describes the impact of risky outcomes on preferences in terms of a rank-dependent nonlinear transformation of their objective probabilities. The attentional Drift Diffusion Model (aDDM) formalizes the finding that attentional biases toward an option can shape preferences within a sequential sampling process. Here we link these two influential frameworks. We used the aDDM to simulate choices between two options while systematically varying the strength of attentional biases to either option. The resulting choices were modeled with CPT. Changes in preference due to attentional biases in the aDDM were reflected in highly systematic signatures in the parameters of CPT's weighting function (curvature, elevation). Based on these insights, we predicted and demonstrated-in a re-analysis of a large set of previously published empirical data-that attentional biases are also systematically linked to patterns in probability weighting empirically. Our analyses also reveal a previously overlooked link between patterns in probability weighting and response times. These findings highlight that distortions in probability weighting can arise from simple option-specific attentional biases in information search, and suggest an alternative to common interpretations of weighting-function parameters in terms of probability sensitivity and optimism. They also point to novel, attention-based explanations for empirical phenomena associated with characteristic shapes of CPT's probability-weighting function (e.g., certainty effect, description-experience gap). The results advance the integration of two prominent computational frameworks for decision making.

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“…Formally, response times in the model are given by the time t it takes a random walk s(t) to cross the choice boundary θ for one option or the other. Because options were randomly assigned to the left and right sides and systematic biases toward one side or the other were negligible, we assume that this process starts at s(0) = 0 (although post-stimulus biases can occasionally be gainfully assigned to start points Zhao et al, 2020). As a decision maker considers the potential payoffs of options, they use payoff information to update their preferences with probability w and delay information to update their preferences with probability 1 − w. On average, this will result in a "drift" toward the favored option, such that the average change of the state µ is described by the expected value of v(x,t) from Equation 6.…”

Section: Response Timesmentioning

confidence: 99%

Cognitive mechanisms underlying subjective value of past and future events: Modeling systematic reversals of temporal value asymmetry

Pd¹,

Baldwin²,

Ec³

2021

Preprint

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People discount both future outcomes that could happen and past outcomes that could have happened according to how far away they are in time. A common finding is that future outcomes are often preferred to past ones when the payoffs and temporal distance (how long ago / until they occur) are matched, referred to as temporal value asymmetry. In this paper, we examine the consistency of this effect by examining the effect of manipulating the magnitude and delays of past and future payoffs on participants' choices, and challenge the claim that differences in value are primarily due to differences in discounting rates for past and future events. We find reversals of the temporal value asymmetry when payoffs are low and when temporal distance is large, suggesting that people have different sensitivity to the magnitude of past and future payoffs. We show that these effects can be accommodated in an direct difference model of intertemporal choice but not in the most common discounting models (hyperboloid), suggesting that both temporal distance and payoff magnitude carry independent influences on the subjective value of past and future outcomes. Finally, we explore how these tendencies to represent past and future outcome values are related to one another and to individual differences in personality and psychological traits, showing how these measures cluster according to whether they measure processes related to past/future events, payoffs/delays, and whether they are behavioral/self-report measures.

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Psychological mechanisms of loss aversion: A drift-diffusion decomposition

Cited by 38 publications

References 95 publications

Using dynamic monitoring of choices to predict and understand risk preferences

Using dynamic monitoring of choices to predict and understand risk preferences

Nonlinear probability weighting can reflect attentional biases in sequential sampling.

Cognitive mechanisms underlying subjective value of past and future events: Modeling systematic reversals of temporal value asymmetry

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