Dr. Joseph G. Johnson, Director

Department of Psychology
Division of Brain and Cognitive Science
Miami University
Oxford, OH 45056

Current and former lab members

Daniel DeCaro (Ph.D. 2010), Assistant Professor, University of Louisville

Gregory Koop (Ph.D. 2012), Assistant Professor, Eastern Mennonite University

Mary Frame (Ph.D. 2017), Research Scientist, Wright State Research Institute

Evan Bristow (M.A. 2010), Analyst, Walker Information

Ruohui Zhang (M. A. 2012), Management Analyst

Xiaolei Zhou (M.A. 2013), Product research

Click here to download lab research summary

Research interests: Judgment and decision making, mathematical and computational modeling of cognitive processes, dynamic systems, experimental methodology and process measurement, parameter estimation and model fitting, individual differences
Current projects: Measuring attention and information acquisition. In conjunction with Ana Franco-Watkins, I am interested in exploring the use of new technologies (i.e. eye-tracking) to reveal processes of attention and information use in deliberation. That is, rather than simply examining indvidual's overt decisions, I feel it is important to understand the underlying processes that give rise to these decisions. We are developing new metrics and analyses for considering such "process-tracing" data, as well as new methods for quantitative model comparison using process and RT data.
Response dynamics in decision making. Recently, a new "action dynamics" paradigm has developed in cognitive science that explores the development of the response associated with various tasks (cf. Rick Dale, Michael Spivey). Theoretically, this work extends our understanding of behavior by looking beyond the discrete button presses and instead looking at how manipulations can affect thought processes as revealed by the arm or hand in producing the response. This work thus suggests a decidedly "embodied" view of cognition, incorporating true perceptual-cognitive-motor interactions in a systems perspective. Our lab is applying this paradigm to standard decision tasks such as choices among gambles and the Iowa Gambling Task.
Decision making under stress. People are often required to make decisions while being subjected to multiple stressors such as time pressure, performance pressure, or competition for attentional resources from secondary or non-relevant tasks. These circumstances are ubiquitous in both trivial decisions as well as those with significant consequences. This research, also performed jointly with Dr. Franco-Watkins, provides an avenue towards a comprehensive understanding of how different stressors affect decision making by looking at multiple stressors (independently and collectively), how individual differences in working memory interact with different stressors, and the impact of changes in task complexity.
Presentation and representation of decision options. With my graduate student Gregory Koop, we have developed a series of experiments to systematically investigate how the representation format of choice options affects subsequent decision behavior. Previous research has distinguished between decisions based on (1) repeated experience with options and their outcomes; (2) explicit presentation of aggregated descriptions of such experiences; and (3) reliance on recalled (aggregated) descriptions. However, these different methods had not been formally manipulated in a single study, as we do. Additionally, we examine process measures and interactions involving several other manipulations.
Motivation and decision making. This line of research, initiated by former graduate student Daniel DeCaro, investigates the influence that various types of motivation have on deliberation and decision making. This project includes the development of a new psychometric scale to measure six distinct types of motivation, and an application of this scale to an experimental choice task. The results of this experiment will be used to incorporate motivational influences formally into models of decision making.
Reference-dependent valuation. Perhaps the most popular descriptive theory of risky choice, prospect theory, is characterized by the notion that individuals use a reference point to evaluate outcomes. This reference point, often assumed to reflect the "status quo," suggests valuation is relative rather than absolute. In this collaborative effort with Dr. X.T. Wang, we propose that multiple reference points in fact determine valuation of outcomes. Specifically, in addition to the status quo, we propose that individuals simultaneously consider minimum requirements and goals. This theory makes strong testable predictions regarding valuation and choice, which we have formalized in a mathematical model and are testing empirically.
Modes of thought. In many domains in psychology, a common (and usually controversial) supposition is that there are distinct and qualitatively different modes of thought; in cognitive psychology this suggests different modes of processing information, and decision making researchers have proposed "intuitive" and "analytic" dichotomies for arriving at a decision. We conjecture that a single information sampling model can more parsimoniously describe decision making behavior, and that behavior attributed to "intuitive" or "analytic" processes can arise from specific parameterization of this common model.
Decision making in sports. We have a longstanding collaboration with Dr. Markus Raab, investigating various aspects of athletes' decision processes. This research has provided an applied setting for testing the theoretical models we have developed, including a model for how people generate options in real, dynamic situations, and how they select from among them. We have also developed methods for incorporating personality variables into formal models, and have detailed the relationship between learning styles and subsequent behavior. Currently, we are working on efforts that use athletes' dynamic streams of attention, as measured by eye-tracking, to predict their choices.
Representative publications: Frame, M. E., Johnson, J. G., & Thomas, R. D. (in press). A neural indicator of response competition in preferential choice. Decision, advance online.
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.
Franco-Watkins, A. & Johnson, J. G. (2016). The ticking time bomb: Using eye-tracking methodology to capture attentional processing during gradual time constraints. Attention, Perception, & Psychophysics, 78, 2363-2372.
Johnson, J. G., & Busemeyer, J. R. (2016). A Computational Model of the Attention Process in Risky Choice.. Decision, 3, 254-280.
Ashby, N. A., Johnson, J. G., Krajbich, I., & Wedel, M. (2016). Applications and innovations of eye-movement research in judgment and decision making. Journal of Behavioral Decision Making, 29, 96-102.
Koop, G. J. & Johnson, J. G. (2013). Response dynamics of preferential choice. Cognitive Psychology, 67, 151-185.
Wang, X. T., & Johnson, J. G. (2012). A tri-reference point theory of decision making under risk. Journal of Experimental Psychology: General, 141, 743-756.
Koop, G. J., & Johnson, J. G. (2012). The use of multiple reference points in risky decision making. Journal of Behavioral Decision Making, 25, 49-62.
Franco-Watkins, A. M., & Johnson, J. G. (2011). Decision moving window: Using interactive eye tracking to examine decision processes. Behavioral Research Methods, 43, 853-863.
Franco-Watkins, A. M., & Johnson, J. G. (2011). Applying the decision moving window to risky choice: Comparison of eye-tracking and mouse-tracing methods. Judgment and Decision Making, 6, 740-749.
Koop, G. J., & Johnson, J. G. (2011). Response dynamics: A new window on the decision process. Judgment and Decision Making, 6, 750-758.
Glöckner, A., Heinen, T., Johnson, J. G., & Raab, M. (in press). Network approaches for expert decisions in sports. Human Movement Science.
Johnson, J. G., & Busemeyer, J. R. (2010). Decision making under risk and uncertainty. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 736-749.
Raab, M., Johnson, J. G., & Heekeren, H., Eds. (2009). Mind and motion: The bidirectional link between thought and action. Progress in Brain Research, Vol. 174. Elsevier.
  • Includes my individual research chapter, group research chapter, and closing chapter.
  • Busemeyer, J. R., & Johnson, J. G. (2008). Microprocess models of decision making. In R. Sun (Ed.), Cambridge Handbook of Computational Psychology, 302-321. Cambridge University Press.
    Otter, T., Johnson, J. G., Rieskamp, J., et al (2008). Sequential sampling models of choice: Some recent advances. Marketing Letters, 19, 255-267.
    Raab, M., & Johnson, J. G. (2007). Expertise-based differences in search and option generation strategies. Journal of Experimental Psychology: Applied, 13, 158-170.
    Raab, M., & Johnson, J. G. (2007). Implicit learning as a means to intuitive decision making in sports. In H. Plessner, C. Betsch, & T. Betsch (Eds.), A new look on intuition in judgment and decision making, 119-133. Mahwah, NJ: Lawrence Erlbaum
    Johnson, J. G., & Busemeyer, J. R. (2006). A unified computational modeling approach to decision making. In D. Fum, F. Del Missier, & A. Stocco (Eds.), Proceedings of the Seventh International Conference on Cognitive Modeling, 154-159.
    Hanoch, Y., Johnson, J. G., & Wilke, A. (2006). Domain specificity in experimental measures and participant recruitment: An application to risk-taking behavior. Psychological Science, 17, 300-304.
    Busemeyer, J. R., Jessup, R. K., Johnson, J. G., & Townsend, J. T. (2006). Building bridges between neural models and complex decision making behavior. Neural Networks, 19, 1047-1058.
    Busemeyer, J. R., Johnson, J. G., & Jessup, R. K. (2006). Preferences constructed from dynamic micro-processing mechanisms. In P. Slovic & S. Lichtenstein (Eds.), The Construction of Preference.
    Johnson, J. G. (2006). Cognitive modeling of decision making in sports. Psychology of Sport and Exercise, 7, 631-652.
    Johnson, J. G., & Busemeyer, J. R. (2005). A dynamic, stochastic, computational model of preference reversal phenomena. Psychological Review, 112, 841-861.
    Johnson, J. G. & Busemeyer, J. R. (2005). Rule-based Decision Field Theory: A dynamic computational model of transitions among decision-making strategies. In Betsch, T., & Haberstroh, S. (Eds.), The Routines of Decision Making, 3-20. Mahwah, NJ: Lawrence Erlbaum Associates.
    Busemeyer, J. R. & Johnson, J. G. (2004). Computational models of decision making. In D. Koehler & N. Harvey (Eds.), Blackwell Handbook of Judgment and Decision Making. Oxford, UK: Blackwell Publishing Co. 133-154.
    Raab, M. & Johnson, J. G. (2004). Individual differences of action-orientation for risk-taking in sports. Research Quarterly for Exercise and Sport, 75(3), 326-336.
    Johnson, J. G., Wilke, A. & Weber, E. U. (2004). Beyond a trait view of risk-taking: A domain-specific scale measuring risk perceptions, expected benefits, and perceived-risk attitude in German-speaking populations. Polish Psychological Bulletin, 35(3), 153-163.
    Johnson, J. G. & Raab, M. (2003). Take the first: Option generation and resulting choices. Organizational Behavior and Human Decision Processes, 91(2), 215-229.
    Johnson, J. G. (2003). Incorporating motivation, individual differences, and other psychological variables in utility-based choice models. Utility Theory and Applications. Dipartimento di Matematica applicata Bruno de Finetti, Università di Trieste, Italy. 123-142.
    Johnson, J. G. & Busemeyer, J. R. (2001). Multiple-stage decision-making: The effect of planning horizon length on dynamic consistency. Theory and Decision, 51(2-4), 217-246.
    Links of interest: Society for Judgment and Decision Making
    Society for Mathematical Psychology

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