Beyond process tracing: Response dynamics in preferential choice

The ubiquity of process models of decision making requires an increased degree of sophistication in the methods and metrics that we use to evaluate models. In this paper, we capitalize on recent work in cognitive science on analyzing response dynamics (or action dynamics). We propose that, as information processing unfolds over the course of a decision, the bearing this has on intended action is also revealed in the motor system. This decidedly “embodied” view suggests that researchers are missing out on potential dependent variables with which to evaluate their models—those associated with the motor response that produces a choice. The current work develops a method for collecting and analyzing such data in the domain of decision making generally, and preferential choice specifically. We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1). After demonstrating that curvature in response trajectories provides a metric of the competition between choice options, we further extend the method to risky decision making (Experiment 2). In this second study, both choice (discrete) and response (continuous) data correspond to the well-known idea of risk-seeking in losses, and risk-aversion in gains, but the continuous data also demonstrate that choices contrary to this maxim may be the product of at least one online preference reversal. In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods.