Advanced mouse-tracking analytic techniques for enhancing psychological science

Abstract

The best laid plans of mice and men often go awry.(Burns, 1785)And often, going awry is psychologically meaningful. Recent advances in psychological science have shown that motion trajectories reflect underlying cognitive processes. In the current article, we discuss how analysis of computer mouse-trajectories and their temporal dynamics can provide powerful insight into these processes. Our goal is to describe how researchers might incorporate mouse-tracking into their psychological toolbox such that they can address novel hypotheses within their areas of research. We primarily focus on recently developed advanced AbstractComputer mouse-tracking is a relatively recently developed behavioral methodology that can contribute unique insight into a wide variety of psychological phenomena. By recording mouse movements en route to specific responses on a screen, researchers glean continuous information about tentative commitments to multiple response alternatives over time. This approach yields a richness of data that can be fully explored with a variety of sophisticated analytic techniques, but these approaches are relatively underutilized and can be difficult to adopt. Here we describe several techniques for researchers to examine the onset and timing of evolving decision processes; test the degree of response competition at different time points; assess trajectory complexity with spatial disorder analyses; identify qualitatively distinct psychological processes during response generation; and finally to distill unique and meaningful components from mouse-tracking data for subsequent analysis. With this guide, we hope researchers can address novel hypotheses otherwise inaccessible with more traditional methods.

What is in a reach? Domain-general spatial modulation of motor responses by number representations

“…This makes it hard to classify movement initiation and reach modulations purely as part of pre or post decision processing. Some have suggested using paradigms that force fast random movement initiation in order to definitively capture pre-decision processing in reach trajectories (Hehman, Stolier, & Freeman, 2015). However, this would not help explain our observation that reach modulations spontaneously emerge in self-paced paradigms after difficulty-dependent response times.…”

The interpretation of prosodic variability in the context of accompanying sociophonetic cues

“…It has been pointed out (e.g., Freeman & Ambady, 2010;Hehman et al, 2014) that if the mean trajectory for an experimental condition shows a moderate deviation from a straight line, and therefore also a moderate average MD value, then it might result from averag- ing two trajectory distributions-one that reflects marked attractions to the alternative response before resolving onto the selected response, and one that consists of more-or-less direct movement to the selected response. In at least one study using mouse-tracking there has been an explicit prediction of such a pattern.…”

“…The most common analyses are measures of the displacement of the mouse trajectory from a straight-line response from the starting position to the target for the choice being made (see for instance Freeman, 2014;Freeman & Ambady, 2010;Hehman et al, 2014;Papesh & Goldinger, 2012). Two such measures are shown in Figure 2.…”

“…In addition, (x, y) coordinate data can be analyzed for changes in direction, speed, or acceleration, in either the horizontal or vertical dimension, or both. Changes in direction can either be simple reversals (x-flips, y-flips, or both) or more complex measures such as sample entropy (for details of these and other measures, see Hehman et al, 2014). MD is the measure selected for analysis in the current paper.…”

Real-World Decision Making: Logging Into Secure vs. Insecure Websites

“…This results in trajectories that can be displayed and analyzed in a manner similar to other mouse tracking studies using a two-alternative forced choice design such as Spivey and Dale's work on the continuous dynamics of cognition [22], or Koop and Johnson's work on response dynamics of preferential choice [23]. After the mouse trajectories were normalized, Area Under the Curve (AUC) was calculated by taking the integral of the distance of the trajectory from the straight line vector through the start and end points as used by Spivey, Kehoe, and Dale and described by Hehman, Stolier, Freeman [21], [24]. For analysis, the logtransform of AUC was used, since the data could be described by a log-normal distribution log N (µ = 1.33, sd = 1.24), and working with a normal distribution simplifies the statistical analysis F. Statistical Analysis 1) Likelihood to login: For both manipulations (https/http and no spoof/spoof), a hierarchical general mixed effects model with likelihood to login (login model) as the predicted variable and manipulation as the independent variable was tested.…”