Using dynamic monitoring of choices to predict and understand risk preferences

Significance Choices that consist of risky versus certain options are pervasive and consequential, leading many researchers to investigate when and which individuals select risk over certainty. The present research takes an alternative approach and measures computer mouse movements to assess how people arrive at these decisions. We show that measuring mouse movements while participants are deciding between a risky gamble and a certain payout powerfully detects their conflict about the options, and that this conflict strongly predicts their risk preferences. Further, mouse movements are predictive of risk preferences even when choice outcomes are not. The present research thus demonstrates the unique utility of dynamic measures of choice, as well as the predictive and theoretical importance of conflict in risky decision-making. 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.