Kyle J. LaFollette

NHB
April 2026Nature Human Behaviour paper: Challenging the mechanism for the implicit association test.
Communications Psychology
February 2026Communications Psychology publication on transfer of learning across task, person, and experiential factors.
Scientific Reports
September 2025Scientific Reports publication on social perception and crowd emotion judgment.
Chicago Booth
July 2025Joined Chicago Booth as Principal Researcher (RF-CDR).
PNAS
July 2025PNAS publication on nonlinear reinforcement-learning dynamics.
NHB
April 2026Nature Human Behaviour paper: Challenging the mechanism for the implicit association test.
Communications Psychology
February 2026Communications Psychology publication on transfer of learning across task, person, and experiential factors.
Scientific Reports
September 2025Scientific Reports publication on social perception and crowd emotion judgment.
Chicago Booth
July 2025Joined Chicago Booth as Principal Researcher (RF-CDR).
PNAS
July 2025PNAS publication on nonlinear reinforcement-learning dynamics.
Kyle J. LaFollette

I am a Principal Researcher at the University of Chicago Booth School of Business, in the Roman Family Center for Decision Research.

In 2025, I completed my PhD in Psychology at Case Western Reserve University. My dissertation, Investigations of Affective Dynamics Using Equation Detection Algorithms, focused on how emotional and cognitive processes can be described as interacting nonlinear systems. I was advised by Heath A. Demaree, Brooke N. Macnamara, and Amit Goldenberg.

In 2017, I obtained a BS in Cognitive Science and Biopsychology from the University of Michigan. Between 2017-2020, I completed post-baccalaureate research assistantships at the University of Arizona and Stanford University.

Research Interests

My research centers on coupled emotion-decision systems. I model behavior as adaptive optimization in nonlinear dynamics, where choices and affect co-evolve under internal constraints and external inputs, and where emotions function as behavioral action tendencies that shape policy over time. Methodologically, I use equation discovery algorithms as an interpretable alternative to black-box AI, combining bottom-up model search with top-down theoretical constraints. This framework supports work on high-stakes judgment and choice, including adaptation under uncertainty, learning in changing environments, and context effects on attitudes, preferences, and behavior.