Johnson, J. G. , & Busemeyer, J. R. (in press). Computational models of decision making. In Sun., R. (Ed.), Cambridge Handbook of Computational Cognitive Sciences. Cambridge University Press.
Broeker, L., Johnson, J. G., de Oliveira, R. F., Ewolds, H. E., Kunzell, S., & Raab, M. (2022). Switch rates vary due to expected payoff but not due to individual risk tendency. Acta Psychologica, 224, 103521.
Frame, M. E., Houpt, J. W., & Johnson, J. G. (2021). Functional analysis of trajectories from multiple devices during a preferential choice task. Decision, 8(4), 346–366.
DeCaro, D. A., DeCaro, M. S., Hotaling, J. M., & Johnson, J. G. (2020). Procedural and economic utilities in consequentialist choice: Trading freedom of choice to minimize financial losses. Judgment and Decision Making, 15(4), 517-533.
Johnson, J.G., & Frame, M.E. (2019). Using process tracing data to define and test process models. In M. Schulte-Mecklenbeck, A. Kuhberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods (2nd Edition) (pp. 374-387). New York: Routledge.
Franco-Watkins, A.M., Hickey, H.K., & Johnson, J.G. (2019). Comparing process tracing paradigms: Tracking attention via mouse and eye movements. In M. Schulte-Mecklenbeck, A. Kuhberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods (2nd Edition) (pp. 96-110). New York: Routledge.
Frame, M. E., Johnson, J. G., & Thomas, R. D. (2018). A neural indicator of response competition in preferential choice. Decision, 5, 272-286.
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). The 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.
Glöckner, A., Heinen, T., Johnson, J. G., & Raab, M. (2012). Network approaches for expert decisions in sports. Human Movement Science, 31, 318-333.
Franco-Watkins, A., & Johnson, J. G. (2011b). 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.
Franco-Watkins, A. M., & Johnson, J. G. (2011a). Decision moving window: Using interactive eye tracking to examine decision processes. Behavioral Research Methods, 43, 853-863.
Johnson, J. G., & Busemeyer, J. R. (2010). Decision making under risk and uncertainty. Wiley Interdisciplinary Reviews (Cognitive science).
Johnson, J. G. (2009). Embodied cognition of movement decisions: A computational modeling approach. In M. Raab, J. G. Johnson, and H. Heekeren (Eds.), Mind and motion: The bidirectional link between thought and action. New York: Elsevier. 137-150.
Johnson, J. G. (2009). Cognitive models of athlete decision making. In D. Araujo, H. Ripoll, & M. Raab (Eds.), Perspectives on Cognition and Action in Sport. Hauppauge, NY: Nova Science Publishers. 171-180.
Johnson, J. G., Raab, M., and Heekeren, H. (2009). Mind and motion: Surveying successes and stumbles in looking ahead. In M. Raab, J. G. Johnson, and H. Heekeren (Eds.), Mind and motion: The bidirectional link between thought and action. New York: Elsevier. 319-328.
DeCaro, D. A., Bar-Eli, M., Conlin, J., Diederich, A., Johnson, J. G., & Plessner, H. (2009). How do motoric realities shape, and become shaped by, the way people evaluate and select potential courses of action? Towards a unitary framework of embodied decision making. In M. Raab, J. G. Johnson, and H. Heekeren (Eds.), Mind and motion: The bidirectional link between thought and action. New York: Elsevier. 189-203.
Otter, T., Johnson, J. G., Rieskamp, J., Allenby, G., et al. (2008). Sequential sampling models of choice: Some recent advances. Marketing Letters, 19, 255-267.
Busemeyer, J. R., & Johnson, J. G. (2008). Micro-process models of decision making. In R. Sun (Ed.), Cambridge handbook of computational psychology, 302-321. Cambridge University Press.
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. (2006). Cognitive modeling of decision making in sports. Psychology of Sport and Exercise, 7, 631-652.
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.
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.
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, 220-234. New York, NY: Cambridge University Press.
Busemeyer, J. R., & Johnson, J. G. (2006). A coherent computational framework for modeling component decision processes. In R. Sun (Ed.), Proceedings of the Twenty-eighth Annual Conference of the Cognitive Science Society, 2643.
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. 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.