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Method Development

Cognitive processes themselves are unobservable, so that latent processes must be deduced from objective observable data. There are a number of methods for this purpose, which are based either on different patterns of responses, each of which points to different strategies, or on behavioural data collected during the problem-solving process (Bröder, 2019).

 

Response Pattern

Bröder (2000) has criticized the methods of decision research for often being based on data that cannot be clearly assigned to certain strategies. Therefore, corresponding conclusions about processes are questionable. He pleaded for a formal connection between theory and data, which under certain assumptions allows to determine the probability of the data under the assumption of different strategies, so that a conclusion on strategies is possible (Bröder & Schiffer, 2003). The method has since been further developed several times (Hilbig & Moshagen, 2014).

 

Search Pattern

In addition to the end product of a decision process (the choice), other data can be collected, such as the search for information before a decision is made. Based on the MouseLab paradigm, an experimental set-up was developed that can also capture the subjective structuring of information (Ettlin et al., 2015).

 

Mouse-Tracking

Mouse tracking is a method that tracks cognitive processes in which people choose between different options that are displayed as buttons on a computer screen. During the decision-making process, the movements of the mouse cursor are recorded and should provide information about how the preference for the individual options develops over time and how strong the conflict is that the person feels when deciding. We have developed free and open source software for the creation and analysis of mouse-tracking experiments: This includes the mousetrap plugin (Kieslich & Henninger, 2017) for the experimental software OpenSesame, which allows the creation of mouse-tracking experiments in the lab, and the mousetrap R package (Kieslich, Henninger, Wulff, Haslbeck, & Schulte-Mecklenbeck, 2019), which allows the processing, visualization and analysis of mouse-tracking data. In addition, we have conducted a number of experimental studies that focus on the effects of the experimental design on the validity of mouse-tracking data. Their ultimate goal is to develop evidence-based guidelines for the design of mouse-tracking experiments (Kieslich, Schoemann, Grage, Hepp, & Scherbaum, 2020).

  • Literature

    • Bröder, A. (2000). A methodological comment on behavioral decision research. Psychologische Beiträge, 42, 645-662.
    • Bröder, A. (2019). Methods for studying human thought. In R. J. Sternberg & J. Funke (Eds.). Introduction to the Psychology of Human Thought, (p.27-52). Heidelberg: Heidelberg University Publishing.
    • Bröder, A. & Schiffer, S. (2003). Bayesian strategy assessment in multi-attribute decision research. Journal of Behavioral Decision Making, 16, 193-213.
    • Ettlin, F., Bröder, A., & Henninger, M. (2015). A new task format for investigating information search and organization in multi-attribute decisions. Behavior Research Methods, 47, 506-518.
    • Hilbig, B. E., & Moshagen, M. (2014). Generalized outcome-based strategy classification: Comparing deterministic and probabilistic choice models. Psychonomic Bulletin & Review, 21(6), 1431–1443.
    • Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An integrated, open-source mouse-tracking package. Behavior Research Methods, 49(5), 1652-1667. https://doi.org/10.3758/s13428-017-0900-z
    • Kieslich, P. J., Henninger, F., Wulff, D. U., Haslbeck, J. M. B., & Schulte-Mecklenbeck, M. (2019). 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 (pp. 111-130). New York: Routledge. https://doi.org/10.4324/9781315160559-9
    • Kieslich, P. J., Schoemann, M., Grage, T., Hepp, J., & Scherbaum, S. (2020). Design factors in mouse-tracking: What makes a difference? Behavior Research Methods, 52(1), 317-341. https://doi.org/10.3758/s13428-019-01228-y