Mousetrap
Mousetrap: An integrated, open-source mouse-tracking package. Mouse-tracking – the analysis of mouse movements in computerized experiments – is becoming increasingly popular in the cognitive sciences. Mouse movements are taken as an indicator of commitment to or conflict between choice options during the decision process. Using mouse-tracking, researchers have gained insight into the temporal development of cognitive processes across a growing number of psychological domains. In the current article, we present software that offers easy and convenient means of recording and analyzing mouse movements in computerized laboratory experiments. In particular, we introduce and demonstrate the mousetrap plugin that adds mouse-tracking to OpenSesame, a popular general-purpose graphical experiment builder. By integrating with this existing experimental software, mousetrap allows for the creation of mouse-tracking studies through a graphical interface, without requiring programming skills. Thus, researchers can benefit from the core features of a validated software package and the many extensions available for it (e.g., the integration with auxiliary hardware such as eye-tracking, or the support of interactive experiments). In addition, the recorded data can be imported directly into the statistical programming language R using the mousetrap package, which greatly facilitates analysis. Mousetrap is cross-platform, open-source and available free of charge from https://github.com/pascalkieslich/mousetrap-os.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
Sorted by year (- Antonio Calcagnì, Massimiliano Pastore, Gianmarco Altoè: ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in R (2019) arXiv
- Heck, Daniel W.; Erdfelder, Edgar; Kieslich, Pascal J.: Generalized processing tree models: jointly modeling discrete and continuous variables (2018)
- Schulz, Eric; Speekenbrink, Maarten; Krause, Andreas: A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions (2018)