Analyzing spatial data from mouse tracker methodology: An entropic approach

Abstract

Mouse tracker methodology has recently been advocated to explore the motor components of the cognitive dynamics involved in experimental tasks like categorization, decision-making, and language comprehension. This methodology relies on the analysis of computer-mouse trajectories, by evaluating whether they significantly differ in terms of direction, amplitude, and location when a given experimental factor is manipulated. In this kind of study, a descriptive geometric approach is usually adopted in the analysis of raw trajectories, where they are summarized with several measures, such as maximum-deviation and area under the curve. However, using raw trajectories to extract spatial descriptors of the movements is problematic due to the noisy and irregular nature of empirical movement paths. Moreover, other significant components of the movement, such as motor pauses, are disregarded. To overcome these drawbacks, we present a novel approach (EMOT) to analyze computer-mouse trajectories that quantifies movement features in terms of entropy while modeling trajectories as composed by fast movements and motor pauses. A dedicated entropy decomposition analysis is additionally

A State Space Approach to Dynamic Modeling of Mouse-Tracking Data

“…Interestingly, the addition of the covariate bigram frequency in the model allowed the main effect of stimulus category to show up. Indeed, while at the medium and high levels of bigram frequency the results are in line with those observed at a sample level in the original study (see Figures 1,2,5 in [6]) and in a recent re-analysis (see Table 2 in [10]), in the case of low bigram the probability to activate the distractor increases with respect to high frequency words (HF). This might be somewhat related to a moderate difficulty in the orthographic processing of low frequency bigram words [57] even in the case of stimuli with richer lexical representation.…”

“…Since we are interested in studying the co-activation of competing processes as reflected in some spatial properties of the response -such as location, direction, and amplitude of the action dynamics [66,21] -we need to simplify the original data structure so that these properties can easily emerge. Inspired by some of the work on this problem [28,40,10], we reduce the dimensionality of the data by projecting s ij in a proper lower-dimensional subspace of movement via the restricted four-quadrant inverse tangent mapping (atan2, see [9]) from the real coordinates to the interval [0, π] N as follows:…”

Mouse-tracking: A practical guide to implementation and analysis

“…Similarly, the current chapter has limited itself to the frequently investigated two-option design, but mouse-tracking can easily be extended to situations with more than two alternatives (e.g., Koop & Johnson, 2011). Lastly, more sophisticated analysis methods are being developed to more fully harvest the rich potential of mouse-tracking data, such as time continuous multiple regression (Scherbaum & Dshemuchadse, 2018), entropy approaches (Calcagnì, Lombardi, & Sulpizio, 2017), generalized processing tree models (Heck, Erdfelder, & Kieslich, in press), and decision landscapes (Zgonnikov, Aleni, Piiroinen, O'Hora, & di Bernardo, 2017). Thus, we are confident that mouse-tracking will continue to offer researchers novel insights into how decision processes unfold over time.…”

Bringing the Nonlinearity of the Movement System to Gestural Theories of Language Use: Multifractal Structure of Spoken English Supports the Compensation for Coarticulation in Human Speech Perception

“…Stronger use of CfC led to a significant decrease in the number of x-position flips, i.e., the number of times that the mouse-cursor trajectory changes horizontal direction. X-position flips were greatest for the middle steps of the [ga]-to-[da] continuum but decreased across trials in the speech-context condition for slower mouse-tracking movements, which we separate from fast mouse-tracking movements using Calcagnì et al's ( 2017 ) informational-entropy measures to parse fast movements from the entire trajectory. In summary, results essentially suggested that speech contexts appear to trigger significant change—and improvements—in compensation for coarticulation on multiple time scales.…”