Design factors in mouse-tracking: What makes a difference?
Investigating cognitive processes by analyzing mouse movements has become a popular method in many psychological disciplines. When creating mouse-tracking experiments, researchers face many design choices—for example, whether participants indicate responses by clicking a button or just by entering the button area. Hitherto, numerous different settings have been employed, but little is known about how these methodological differences affect mouse-tracking data. We systematically investigated the influences of three central design factors, using a classic mouse-tracking paradigm in which participants classified typical and atypical exemplars. In separate experiments, we manipulated the response indication, mouse sensitivity, and starting procedure. The core finding that mouse movements deviate more toward the nonchosen option for atypical exemplars was replicated in all conditions. However, the size of this effect varied. Specifically, it was larger when participants indicated responses via click and when they were instructed to initialize the movement early. Trajectory shapes also differed between setups. For example, a dynamic start led to mostly curved trajectories, responses via click led to a mix of straight and “change-of-mind” trajectories, and responses via touch led to mostly straight trajectories. Moreover, the distribution of curvature indices was classified as bimodal in some setups and as unimodal in others. Because trajectory curvature and shape are frequently used to make inferences about psychological theories, such as differentiating between dynamic and dual-system models, this study shows that the specific design must be carefully considered when drawing theoretical inferences. All methodological designs and analyses were implemented using open-source software and are available from https://osf.io/xdp7a/.
KeywordsMouse-tracking Cognitive processes Experimental design Decision-making Response dynamics
- Aczel, B., Szollosi, A., Palfi, B., Szaszi, B., & Kieslich, P. J. (2018). Is action execution part of the decision-making process? An investigation of the embodied choice hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 44, 918–926. https://doi.org/10.1037/xlm0000484 Google Scholar
- Grage, T., Schoemann, M., & Scherbaum, S. (2019). Lost to translation: 1809 How design factors of the mouse-tracking procedure impact the 1810 inference from action to cognition. Manuscript submitted for 1811 publication.Google Scholar
- Hehman, E., Carpinella, C. M., Johnson, K. L., Leitner, J. B., & Freeman, J. B. (2014). Early processing of gendered facial cues predicts the electoral success of female politicians. Social Psychological and Personality Science, 5, 815–824. https://doi.org/10.1177/1948550614534701 CrossRefGoogle Scholar
- Kieslich, P. J., Henninger, F., Wulff, D. U., Haslbeck, J. M. B., & Schulte-Mecklenbeck, M. (in press). Mouse-tracking: A practical guide to implementation and analysis. In M. Schulte-Mecklenbeck, A. Kühberger, & J. G. Johnson (Eds.), A handbook of process tracing methods. New York, NY: Routledge.Google Scholar
- Kieslich, P. J., & Hilbig, B. E. (2014). Cognitive conflict in social dilemmas: An analysis of response dynamics. Judgment and Decision Making, 9, 510–522.Google Scholar
- Koop, G. J. (2013). An assessment of the temporal dynamics of moral decisions. Judgment and Decision Making, 8, 527–539.Google Scholar
- Koop, G. J., & Johnson, J. G. (2011). Response dynamics: A new window on the decision process. Judgment and Decision Making, 6, 750–758.Google Scholar
- R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
- Schoemann, M., Lüken, M., Grage, T., Kieslich, P. J., & Scherbaum, S. (2019). Validating mouse-tracking: How design factors influence action dynamics in intertemporal decision making. Behavior Research Methods. Advance online publication. https://doi.org/10.3758/s13428-018-1179-4
- Schulte-Mecklenbeck, M., Johnson, J. G., Böckenholt, U., Goldstein, D. G., Russo, J. E., Sullivan, N. J., & Willemsen, M. C. (2017). Process-tracing methods in decision making: On growing up in the 70s. Current Directions in Psychological Science, 26, 442–450. https://doi.org/10.1177/0963721417708229 CrossRefGoogle Scholar
- Szaszi, B., Palfi, B., Szollosi, A., Kieslich, P. J., & Aczel, B. (2018). Thinking dynamics and individual differences: Mouse-tracking analysis of the denominator neglect task. Judgment and Decision Making, 13, 23–32.Google Scholar
- Wulff, D. U., Haslbeck, J. M. B., Kieslich, P. J., Henninger, F., & Schulte-Mecklenbeck, M. (in press). Mouse-tracking: Detecting types in movement trajectories. In M. Schulte-Mecklenbeck, A. Kühberger, & J. G. Johnson (Eds.), A handbook of process tracing methods. New York, NY: Routledge.Google Scholar
- Wulff, D. U., Haslbeck, J. M. B., & Schulte-Mecklenbeck, M. (2018). Measuring the (dis-)continuous mind: What movement trajectories reveal about cognition. Manuscript in preparationGoogle Scholar