Doing psychological science by hand

Abstract

Over the past decade, mouse-tracking in choice tasks has become a popular method across psychological science. This method exploits hand movements as a measure of multiple response activations that can be tracked continuously over hundreds of milliseconds. Whereas early mouse-tracking research focused on specific debates, researchers have realized the methodology has far broader theoretical value. This more recent work demonstrates that mouse-tracking is a widely applicable measure across the field, capable of exposing the micro-structure of real-time decisions including their component processes and millisecond-resolution time-course in ways that inform theory. In the article, recent advances in the mouse-tracking approach are described, and comparisons with the gold standard measure of reaction time and other temporally-sensitive methodologies are provided. Future directions, including mapping to neural representations with brain-imaging and ways to improve our theoretical understanding of mouse-tracking methodology, are discussed.

Keywords: decision-making; hand movement; mouse-tracking; reaction time; temporal dynamics.

Figure 1

(a) A depiction of a standard two-choice mouse-tracking paradigm, where on each trial participants click a start button at the bottom-center, which reveals a stimulus. Participants then move the cursor and click on one of the two responses in either top corners. There are many variants, including multi-choice paradigms (e.g., four choices), sequences of stimuli (e.g., priming), or responses serving as stimuli themselves (as in b). (b) Mouse-tracking reveals decision micro-structure. In conditions of conflict, dynamic models tend to predict simultaneously active processes (e.g., impulse toward unhealthy food vs. long-term goal toward healthy food) that continuously self-organize into an explicit response. This leads to parallel attraction effects with a unimodal distribution. Dual-systems models tend to predict a System 1 process occurs automatically (e.g., automatic impulse) on certain trials, which is then intervened on by a System 2 process (e.g., controlled goal). This leads to two subpopulations of trials (extreme mid-flight correction trials and no-attraction trials) creating a bimodal distribution. (c) Example of mouse-tracking used as a time-course methodology from Sullivan et al. (2015). The strength of the relationship (regression coefficients) between trajectories’ angle-of-movement and the relative tastiness and healthfulness of one food option over another is plotted for each time window, separately for participants with low and high self-control ability. Vertical lines indicate onset of significant effects. Healthfulness was processed as early as tastiness for high self-control participants; for low self-control participants, healthfulness was processed considerably later.

Figure 2

Schematic illustration of results from Stolier and Freeman (2017). Sets of cubes are meant to illustrate neural-representational multi-voxel patterns. During synchronized mouse-tracking and fMRI, participants categorized the gender or race of typical and atypical exemplar faces. On a given atypical trial (e.g., feminine male face), the extent to which participants were attracted to the opposite category response (e.g., ‘female’) predicted an increased similarity in the face’s neural-representational pattern to that opposite category in the right fusiform gyrus (rFG), a face-processing region. The opposite category’s neural-representational pattern was measured as the average pattern of all typical trials for that category (e.g., average of typical female trials). This paradigm can therefore identity which levels of neural representation are impacted by specific dynamics of a decision trajectory.