Advertisement

Design factors in mouse-tracking: What makes a difference?

  • Pascal J. KieslichEmail author
  • Martin Schoemann
  • Tobias Grage
  • Johanna Hepp
  • Stefan Scherbaum
Article

Abstract

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/.

Keywords

Mouse-tracking Cognitive processes Experimental design Decision-making Response dynamics 

Notes

References

  1. 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
  2. Dale, R., & Duran, N. D. (2011). The cognitive dynamics of negated sentence verification. Cognitive Science, 35, 983–996.  https://doi.org/10.1111/j.1551-6709.2010.01164.x CrossRefGoogle Scholar
  3. Dale, R., Kehoe, C., & Spivey, M. J. (2007). Graded motor responses in the time course of categorizing atypical exemplars. Memory & Cognition, 35, 15–28.  https://doi.org/10.3758/BF03195938 CrossRefGoogle Scholar
  4. Dshemuchadse, M., Scherbaum, S., & Goschke, T. (2013). How decisions emerge: Action dynamics in intertemporal decision making. Journal of Experimental Psychology: General, 142, 93–100.  https://doi.org/10.1037/a0028499 CrossRefGoogle Scholar
  5. Duran, N. D., Nicholson, S. P., & Dale, R. (2017). The hidden appeal and aversion to political conspiracies as revealed in the response dynamics of partisans. Journal of Experimental Social Psychology, 73, 268–278.  https://doi.org/10.1016/j.jesp.2017.07.008 CrossRefGoogle Scholar
  6. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160.  https://doi.org/10.3758/BRM.41.4.1149 CrossRefGoogle Scholar
  7. Faulkenberry, T. J., Witte, M., & Hartmann, M. (2018). Tracking the continuous dynamics of numerical processing: A brief review and editorial. Journal of Numerical Cognition, 4, 271–285.  https://doi.org/10.5964/jnc.v4i2.179 CrossRefGoogle Scholar
  8. Fischer, M. H., & Hartmann, M. (2014). Pushing forward in embodied cognition: May we mouse the mathematical mind? Frontiers in Psychology, 5, 1315.  https://doi.org/10.3389/fpsyg.2014.01315 Google Scholar
  9. Freeman, J. B. (2014). Abrupt category shifts during real-time person perception. Psychonomic Bulletin & Review, 21, 85–92.  https://doi.org/10.3758/s13423-013-0470-8 CrossRefGoogle Scholar
  10. Freeman, J. B. (2018). Doing psychological science by hand. Current Directions in Psychological Science, 27, 315–323.  https://doi.org/10.1177/0963721417746793 CrossRefGoogle Scholar
  11. Freeman, J. B., & Ambady, N. (2009). Motions of the hand expose the partial and parallel activation of stereotypes. Psychological Science, 20, 1183–1188.  https://doi.org/10.1111/j.1467-9280.2009.02422.x CrossRefGoogle Scholar
  12. Freeman, J. B., & Ambady, N. (2010). MouseTracker: Software for studying real-time mental processing using a computer mouse-tracking method. Behavior Research Methods, 42, 226–241.  https://doi.org/10.3758/BRM.42.1.226 CrossRefGoogle Scholar
  13. Freeman, J. B., & Ambady, N. (2011). Hand movements reveal the time-course of shape and pigmentation processing in face categorization. Psychonomic Bulletin & Review, 18, 705–712.  https://doi.org/10.3758/s13423-011-0097-6 CrossRefGoogle Scholar
  14. Freeman, J. B., Ambady, N., Rule, N. O., & Johnson, K. L. (2008). Will a category cue attract you? Motor output reveals dynamic competition across person construal. Journal of Experimental Psychology: General, 137, 673–690.  https://doi.org/10.1037/a0013875 CrossRefGoogle Scholar
  15. Freeman, J. B., & Dale, R. (2013). Assessing bimodality to detect the presence of a dual cognitive process. Behavior Research Methods, 45, 83–97.  https://doi.org/10.3758/s13428-012-0225-x CrossRefGoogle Scholar
  16. Freeman, J. B., Dale, R., & Farmer, T. A. (2011). Hand in motion reveals mind in motion. Frontiers in Psychology, 2, 59.  https://doi.org/10.3389/fpsyg.2011.00059 CrossRefGoogle Scholar
  17. Frisch, S., Dshemuchadse, M., Görner, M., Goschke, T., & Scherbaum, S. (2015). Unraveling the sub-processes of selective attention: Insights from dynamic modeling and continuous behavior. Cognitive Processing, 16, 377–388.  https://doi.org/10.1007/s10339-015-0666-0 CrossRefGoogle Scholar
  18. 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
  19. 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
  20. Hehman, E., Stolier, R. M., & Freeman, J. B. (2015). Advanced mouse-tracking analytic techniques for enhancing psychological science. Group Processes & Intergroup Relations, 18, 384–401.  https://doi.org/10.1177/1368430214538325 CrossRefGoogle Scholar
  21. Huette, S., & McMurray, B. (2010). Continuous dynamics of color categorization. Psychonomic Bulletin & Review, 17, 348–354.  https://doi.org/10.3758/PBR.17.3.348 CrossRefGoogle Scholar
  22. Johnson, K. L., Freeman, J. B., & Pauker, K. (2012). Race is gendered: How covarying phenotypes and stereotypes bias sex categorization. Journal of Personality and Social Psychology, 102, 116–131.  https://doi.org/10.1037/a0025335 CrossRefGoogle Scholar
  23. Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An integrated, open-source mouse-tracking package. Behavior Research Methods, 49, 1652–1667.  https://doi.org/10.3758/s13428-017-0900-z CrossRefGoogle Scholar
  24. 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
  25. 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
  26. Koop, G. J. (2013). An assessment of the temporal dynamics of moral decisions. Judgment and Decision Making, 8, 527–539.Google Scholar
  27. Koop, G. J., & Criss, A. H. (2016). The response dynamics of recognition memory: Sensitivity and bias. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42, 671–685.  https://doi.org/10.1037/xlm0000202 Google Scholar
  28. 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
  29. Koop, G. J., & Johnson, J. G. (2013). The response dynamics of preferential choice. Cognitive Psychology, 67, 151–185.  https://doi.org/10.1016/j.cogpsych.2013.09.001 CrossRefGoogle Scholar
  30. Lepora, N. F., & Pezzulo, G. (2015). Embodied choice: How action influences perceptual decision making. PLOS Computational Biology, 11, e1004110.  https://doi.org/10.1371/journal.pcbi.1004110 CrossRefGoogle Scholar
  31. Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44, 314–324.  https://doi.org/10.3758/s13428-011-0168-7 CrossRefGoogle Scholar
  32. Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9, 97–113.  https://doi.org/10.1016/0028-3932(71)90067-4 CrossRefGoogle Scholar
  33. Papesh, M. H., & Goldinger, S. D. (2012). Memory in motion: Movement dynamics reveal memory strength. Psychonomic Bulletin & Review, 19, 906–913.  https://doi.org/10.3758/s13423-012-0281-3 CrossRefGoogle Scholar
  34. 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/
  35. Scherbaum, S., Dshemuchadse, M., Fischer, R., & Goschke, T. (2010). How decisions evolve: The temporal dynamics of action selection. Cognition, 115, 407–416.  https://doi.org/10.1016/j.cognition.2010.02.004 CrossRefGoogle Scholar
  36. Scherbaum, S., & Kieslich, P. J. (2018). Stuck at the starting line: How the starting procedure influences mouse-tracking data. Behavior Research Methods, 50, 2097–2110.  https://doi.org/10.3758/s13428-017-0977-4 CrossRefGoogle Scholar
  37. 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
  38. 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
  39. Spivey, M. J., & Dale, R. (2006). Continuous dynamics in real-time cognition. Current Directions in Psychological Science, 15, 207–211.  https://doi.org/10.1111/j.1467-8721.2006.00437.x CrossRefGoogle Scholar
  40. Spivey, M. J., Grosjean, M., & Knoblich, G. (2005). Continuous attraction toward phonological competitors. Proceedings of the National Academy of Sciences, 102, 10393–10398.  https://doi.org/10.1073/pnas.0503903102 CrossRefGoogle Scholar
  41. Stillman, P. E., Medvedev, D., & Ferguson, M. J. (2017). Resisting temptation: Tracking how self-control conflicts are successfully resolved in real time. Psychological Science, 28, 1240–1258.  https://doi.org/10.1177/0956797617705386 CrossRefGoogle Scholar
  42. Stillman, P. E., Shen, X., & Ferguson, M. J. (2018). How mouse-tracking can advance social cognitive theory. Trends in Cognitive Sciences, 22, 531–543.  https://doi.org/10.1016/j.tics.2018.03.012 CrossRefGoogle Scholar
  43. Stolier, R. M., & Freeman, J. B. (2016). Neural pattern similarity reveals the inherent intersection of social categories. Nature Neuroscience, 19, 795–797.  https://doi.org/10.1038/nn.4296 CrossRefGoogle Scholar
  44. Sullivan, N., Hutcherson, C., Harris, A., & Rangel, A. (2015). Dietary self-control is related to the speed with which attributes of healthfulness and tastiness are processed. Psychological Science, 26, 122–134.  https://doi.org/10.1177/0956797614559543 CrossRefGoogle Scholar
  45. 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
  46. Travers, E., Rolison, J. J., & Feeney, A. (2016). The time course of conflict on the Cognitive Reflection Test. Cognition, 150, 109–118.  https://doi.org/10.1016/j.cognition.2016.01.015 CrossRefGoogle Scholar
  47. 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
  48. 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
  49. Yu, Z., Wang, F., Wang, D., & Bastin, M. (2012). Beyond reaction times: Incorporating mouse-tracking measures into the Implicit Association Test to examine its underlying process. Social Cognition, 30, 289–306.  https://doi.org/10.1521/soco.2012.30.3.289 CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of Psychology, School of Social SciencesUniversity of MannheimMannheimGermany
  2. 2.Mannheimer Zentrum für Europäische Sozialforschung (MZES)University of MannheimMannheimGermany
  3. 3.Technische Universität DresdenDresdenGermany
  4. 4.Department of Psychosomatic Medicine, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg UniversityMannheimGermany

Personalised recommendations