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

Mouse tracking performance: A new approach to analyzing continuous mouse tracking data

Mouse tracking is an important source of data in cognitive science. Most contemporary mouse tracking studies use binary-choice tasks and analyze the curvature or velocity of an individual mouse movement during an experimental trial as participants select from one of the two options. However, there are many types of mouse tracking data available beyond what is produced in a binary-choice task, including naturalistic data from web users. In order to utilize these data, cognitive scientists need tools that are robust to the lack of trial-by-trial structure in most normal computer tasks. We use singular value decomposition (SVD) and detrended fluctuation analysis (DFA) to analyze whole time series of unstructured mouse movement data. We also introduce a new technique for describing two-dimensional mouse traces as complex-valued time series, which allows SVD and DFA to be applied in a straightforward way without losing important spatial information. We find that there is useful information at the level of whole time series, and we use this information to predict performance in an online task. We also discuss how the implications of these results can advance the use of mouse tracking research in cognitive science.

Moving beyond response times with accessible measures of manual dynamics

Button-press measures of response time (RT) and accuracy have long served a central role in psychological research. However, RT and accuracy provide limited insight into how cognitive processes unfold over time. To address this limitation, researchers have used hand-tracking techniques to investigate how cognitive processes unfold over the course of a response, are modulated by recent experience, and function across the lifespan. Despite the efficacy of these techniques for investigating a wide range of psychological phenomena, widespread adoption of hand-tracking techniques within the field is hindered by a range of factors, including equipment costs and the use of specialized software. Here, we demonstrate that the behavioral dynamics previously observed with specialized motion-tracking equipment in an Eriksen flanker task can be captured with an affordable, portable, and easy-to-assemble response box. Six-to-eight-year-olds and adults (N = 90) completed a computerized version of the flanker task by pressing and holding a central button until a stimulus array appeared. Participants then responded by releasing the central button and reaching to press one of two response buttons. This method allowed RT to be separated into initiation time (when the central button was released) and movement time (time elapsed between initiation and completion of the response). Consistent with previous research using motion-tracking techniques, initiation times and movement times revealed distinct patterns of effects across trials and between age groups, indicating that the method used in the current study presents a simple solution for researchers from across the psychological and brain sciences looking to move beyond RTs.