My research focus is in decision neuroscience (neuroeconomics), centering on models of neural and motivational processes in decision making and learning. I have primarily been concerned with understanding the differences between descriptive choice and experiential choice. This research includes behavioral experiments and modeling -- using decision field theory -- as well as a functional magnetic resonance imaging (fMRI) examination, using an implementation of decision field theory which I extended to incorporate learning.
In August 2008, I obtained a joint Ph.D. in Cognitive Psychology and Cognitive Science from Indiana University in Bloomington, Indiana. While at IU, I was a member of Jerry Busemeyer's Judgment and Decision Making Lab and also worked with Josh Brown, Peter Todd, and Julie Stout.
In September 2008, I became a post-doctoral fellow and am now working with John O'Doherty in the Trinity College Institute of Neuroscience and School of Psychology at Trinity College Dublin, in Dublin, Ireland.
Professional Affiliations
Learning and Decision Making Papers
Other Papers Matlab Files Somewhat Research Related But Not Really
Association for Psychological Science
Cognitive Neuroscience Society
Society for Judgment and Decision Making
Society for Neuroeconomics
[Journal PDF]
[Submitted PDF]
Jessup, R. K., Busemeyer, J. R., & Brown, J. W. (in press). Error
effects in anterior cingulate cortex reverse when error likelihood is high. Journal of Neuroscience.
[Journal PDF]
[Submitted PDF]
Jessup, R. K., & O'Doherty, J. P. (2009). It was nice not seeing you today:
Perceptual learning about rewards in the absence of awareness. Neuron, 61, 649-650.
[Journal PDF]
[Submitted PDF]
Jessup, R. K., Veinott, E. S., Todd, P. M., & Busemeyer, J. R. (2009). Leaving the store empty-handed:
Testing explanations for the too much choice effect using decision field theory. Psychology & Marketing, 26, 299-320.
[Journal PDF]
[Submitted PDF] Jessup, R. K., Bishara, A. J., & Busemeyer, J. R. (2008).
Feedback produces divergence from Prospect Theory in descriptive choice. Psychological Science, 19, 1015-1022.
[Journal PDF]
[Submitted PDF] 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.
[Submitted PDF]
Busemeyer, J. R., & Jessup, R. K., & Dimperio, E. (2009). The dynamic interactions between situations and decisions. In P. Robbins, M. Aydede (Eds.),
The Cambridge Handbook of Situation Cognition (pp. 307-321). New York: Cambridge University Press.
[Submitted PDF] Busemeyer, J. R., Johnson, J. G., & Jessup, R. K. (2006).
Preferences constructed from dynamic micro-processing mechanisms. In S. Lichtenstein & P. Slovic (Eds.), The Construction of Preference
(pp. 220-234). New York: Cambridge University Press.
[Submitted PDF] Busemeyer, J. R., Dimperio, E., & Jessup, R. K. (2007).
Integrating emotional processes into decision making models. In W. D. Gray (Ed.), Integrated Models of Cognitive Systems (pp. 220-234).
Oxford University Press.
[Journal PDF]
[Submitted PDF] Jessup, R. K. (2009). Transfer of high domain knowledge to a similar domain. American Journal of Psychology, 122, 63-73.
[Journal PDF]
Beck, R. & Jessup, R. K. (2004). A multidimensional assessment of Quest motivation. Journal of Psychology and Theology, 32, 283-294.
This function is a fast reinforcement learning model algorithm for a single action (i.e., option or cue) that doesn't use loops.
This function is a fast reinforcement learning model algorithm for n actions (i.e., options or cues) that loops through the number of actions as opposed to trials.
Code for generating sample reinforcement learning data with two actions. This can be used to test the function on the preceding line.
A comparison file that demonstrates the computational equivalence of the new method with the traditional looping method. This can be used to verify that the new method is identical but computationally faster.
St. Petersburg Paradox
World Cup Soccer Simulations