Abstract
Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and lexical decision tasks. Mouse-tracking data are commonly analyzed using a two-step procedure which first summarizes individuals' hand trajectories with independent measures, and then applies standard statistical models on them. However, this approach can be problematic in many cases. In particular, it does not provide a direct way to capitalize the richness of hand movement variability within a consistent and unified representation. In this article we present a novel, unified framework for mouse-tracking data. Unlike standard approaches to mouse-tracking, our proposal uses stochastic state-space modeling to represent the observed trajectories in terms of both individual movement dynamics and experimental variables. The model is estimated via a Metropolis-Hastings algorithm coupled with a non-linear recursive filter. The characteristics and potentials of the proposed approach are illustrated using a lexical decision case study. The results highlighted how dynamic modeling of mouse-tracking data can considerably improve the analysis of mouse-tracking tasks and the conclusions researchers can draw from them.
Highlights
Over the last decades, the study of computer-mouse trajectories has brought to light new perspectives into the investigation of a wide range of cognitive processes [e.g., for a recent review see Freeman (2017)]
The MCMC convergences of the algorithm are reported in Supplementary Materials
The results of this first analysis clearly show that the dynamics of the state-space model were unaffected by the different categories represented in the recoded experimental factor. This pattern finds further support in the post-hoc comparisons between the three experimental conditions (Figure 4B). These findings indicate that for a dynamic model represented according to a state-space modeling framework, the three stimulus categories (HF, low frequency words (LF), and NW) were all processed in a very similar way, as the original trajectories were not sufficiently different among the three stimulus categories
Summary
The study of computer-mouse trajectories has brought to light new perspectives into the investigation of a wide range of cognitive processes [e.g., for a recent review see Freeman (2017)]. Unlike traditional behavioral measures, such as reaction times and accuracies, mouse trajectories may offer a valid and cost-effective way to measure the real-time evolution of ongoing cognitive processes during experimental tasks (Friedman et al, 2013). This has been supported by recent researches investigating mouse-tracking in association to more consolidated experimental devices, such as eye-tracking and fMRI (e.g., Quétard et al, 2016; Stolier and Freeman, 2017). The curvature of computer-mouse trajectories might reveal competing processes activated in discriminating the
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