TRACK—a new algorithm and open-source tool for the analysis of pursuit-tracking sensorimotor integration processes

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung



In daily life, sensorimotor integration processes are fundamental for many cognitive operations. The pursuit-tracking paradigm is an ecological and valid paradigm to examine sensorimotor integration processes in a more complex environment than many established tasks that assess simple motor responses. However, the analysis of pursuit-tracking performance is complicated, and parameters quantified to examine performance are sometimes ambiguous regarding their interpretation. We introduce an open-source algorithm (TRACK) to calculate a new tracking error metric, the spatial error, based on the identification of the intended target position for the respective cursor position. The identification is based on assigning cursor and target direction changes to each other as key events, based on the assumptions of similarity and proximity. By applying our algorithm to pursuit-tracking data, beyond replication of known effects such as learning or practice effects, we show a higher precision of the spatial tracking error, i.e., it fits our behavioral data better than the temporal tracking error and thus provides new insights and parameters for the investigation of pursuit-tracking behavior. Our work provides an important step towards fully utilizing the potential of pursuit-tracking tasks for research on sensorimotor integration processes.


Seiten (von - bis)433-446
FachzeitschriftBehavior research methods
Frühes Online-Datum25 Jan. 2023
PublikationsstatusVeröffentlicht - Jan. 2024

Externe IDs

Mendeley 909f6191-d8ca-3612-a982-201189b02dbf
ORCID /0000-0002-2989-9561/work/150329047
PubMed 36698001



  • Algorithm, Sensorimotor integration, Tracking task, Pursuit, Smooth, Learning/physiology, Humans, Psychomotor Performance/physiology, Photic Stimulation