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Abstract Converging evidence has demonstrated that humans exhibit two distinct strategies when learning in complex environments. One is model-free learning, i.e., simple reinforcement of rewarded actions, and the other is model-based learning, which considers the structure of the environment. Recent work has argued that people exhibit little model-based behavior unless it leads to higher rewards. Here we use mouse tracking to study model-based learning in stochastic and deterministic (pattern-based) environments of varying difficulty. In both tasks participants’ mouse movements reveal that they learned the structures of their environments, despite the fact that standard behavior-based estimates suggested no such learning in the stochastic task. Thus, we argue that mouse tracking can reveal whether subjects have structure knowledge, which is necessary but not sufficient for model-based choice. 

Abstract Social decision making involves balancing conflicts between selfishness and pro-sociality. The cognitive processes underlying such decisions are not well understood, with some arguing for a single comparison process, while others argue for dual processes (one intuitive and one deliberative). Here, we propose a way to reconcile these two opposing frameworks. We argue that behavior attributed to intuition can instead be seen as a starting point bias of a sequential sampling model (SSM) process, analogous to a prior in a Bayesian framework. Using mini-dictator games in which subjects make binary decisions about how to allocate money between themselves and another participant, we find that pro-social subjects become more pro-social under time pressure and less pro-social under time delay, while selfish subjects do the opposite. Our findings help reconcile the conflicting results concerning the cognitive processes of social decision making and highlight the importance of modeling the dynamics of the choice process. 

In our everyday lives, we often have to choose between many different options. When deciding what to order off a menu, for example, or what type of soda to buy in the supermarket, we have a range of possibilities to consider. So how do we decide what to go for? Researchers believe we make such choices by assigning a subjective value to each of the available options. But we can do this in several different ways. We could look at every option in turn, and then choose the best one once we have considered them all. This is a so-called ‘rational’ decision-making approach. But we could also consider each of the options one at a time and stop as soon as we find one that is good enough. This strategy is known as ‘satisficing’. In both approaches, we use our eyes to gather information about the items available. Most scientists have assumed that merely looking at an item – such as a particular brand of soda – does not affect how we feel about that item. But studies in which animals or people choose between much smaller sets of objects – usually up to four – suggest otherwise. The results from these studies indicate that looking at an item makes that item more attractive to the observer, thereby increasing its subjective value. Thomas et al. now show that gaze also plays an active role in the decision-making process when people are spoilt for choice. Healthy volunteers looked at pictures of up to 36 snack foods on a screen and were asked to select the one they would most like to eat. The researchers then recorded the volunteers’ choices and response times, and used eye-tracking technology to follow the direction of their gaze. They then tested which of the various decision-making strategies could best account for all the behaviour. The results showed that the volunteers’ behaviour was best explained by computer models that assumed that looking at an item increases its subjective value. Moreover, the results confirmed that we do not examine all items and then choose the best one. But neither do we use a purely satisficing approach: the volunteers chose the last item they had looked at less than half the time. Instead, we make decisions by comparing individual items against one another, going back and forth between them. The longer we look at an item, the more attractive it becomes, and the more likely we are to choose it. 

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.