# A state space approach to dynamic modeling of mouse-tracking data

## TL;DR

This paper introduces a novel state-space modeling framework for mouse-tracking data that captures movement dynamics and experimental variables, offering a more integrated analysis method than traditional two-step procedures.

## Contribution

It presents a unified stochastic state-space approach for analyzing mouse-tracking trajectories, improving the interpretation of real-time cognitive processes.

## Key findings

- Enhanced analysis of mouse-tracking data using the proposed model
- Improved understanding of movement dynamics in decision tasks
- Case study demonstrating the model's effectiveness

## 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 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.