Using dynamic monitoring of choices to predict and understand risk preferences

Abstract

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.

Chimpanzees (Pan troglodytes) show subtle signs of uncertainty when choices are more difficult

“…rhesus macaques vs. capuchins, see Beran et al, 2016 ; or bonobos vs. chimpanzees, see Heilbronner, Rosati, Stevens, Hare, & Hauser, 2008 ). There is some evidence consistent with this in humans, for example, a recent computer mouse-tracking study with human participants demonstrated a close relationship between tracking metrics that were comparable to the operationalization of wavering used in our study and subjective risk perception as well as individual risk aversion ( Stillman, Krajbich, & Ferguson, 2020 ). Alternatively, risk-averse individuals or species may differ more strongly with regard to how sensitive they are in noticing their self-generated cues, or in learning to respond to them with second-order behavior.…”

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

“…Indeed, it has recently been shown that decision-makers change how they interact with their options as they become more and more familiar with them, as evidenced by changes in their gaze patterns (S. E. Cavanagh, Malalasekera, Miranda, Hunt, & Kennerley, 2019). Finer-grained estimates of choice dynamics such as gaze or mouse tracking therefore hold promises for further elucidating interactions between strategic/heuristic processes and more comprehensive evaluations (Callaway, Rangel, et al, 2021;Hunt et al, 2016;Jang et al, 2021;Stillman, Krajbich, & Ferguson, 2020). Computational models that explicitly address these interactions may then also scale better to larger choice sets, where strategies that reduce information processing costs play a much more prominent role (Thomas et al, 2021), and could provide insights into the mechanisms by which decision-making is adapted to changing contextual demands (Teoh et al, 2020).…”

Breaking reality’s constraints on motivated cognition

“…and recorded mouse cursor trajectories as participants clicked a button labeled 'yes' or 'no'. We expected all participants to click 'yes,' but for trajectories to veer closer to 'no' among participants with a goal to defend, indicating greater difficulty in providing the correct response (Stillman, Krajbich, & Ferguson, 2020;Melnikoff, Mann, Stillman, Shen, & Ferguson, 2021).…”