  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
analysed 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.
