The expression of decision and learning variables in movement patterns related to decision actions.

Decisions are not necessarily easy to separate into a planning and an execution phase and the decision-making process can often be reflected in the movement associated with the decision. Here, we used formalized definitions of concepts relevant in decision-making and learning to explore if and how these concepts correlate with decision-related movement paths, both during and after a choice is made. To this end, we let 120 participants (46 males, mean age = 24.5 years) undergo a repeated probabilistic two-choice task with changing probabilities where we used mouse-tracking, a simple non-invasive technique, to study the movements related to decisions. The decisions of the participants were modelled using Bayesian inference which enabled the computation of variables related to decision-making and learning. Analyses of the movement during the decision showed effects of relevant decision variables, such as confidence, on aspects related to, for instance, timing and pausing, range of movement and deviation from the shortest distance. For the movements after a decision there were some effects of relevant learning variables, mainly related to timing and speed. We believe our findings can be of interest for researchers within several fields, spanning from social learning to experimental methods and human-machine/robot interaction.