Jeffrey K. Bye

Jeffrey K. Bye

Lecturer, Educational Psychology

University of Minnesota


I am a Lecturer in the Educational Psychology department at the University of Minnesota, and the Lab Coordinator and Affiliate Faculty of the Learning Informatics Lab.

How do children and adults learn and think about math, data, science, and programming? How do we form abstract knowledge from concrete experiences, and when is that useful? What can this tell us about learning and teaching in the classroom and beyond? My research blends cognitive science, learning science, and educational psychology approaches to try to answer these questions. I am passionate about teaching these and related topics, and helping social scientists learn open-source programming for reproducible research workflows.

My academic background is grounded in computational cognitive science and experimental psychology. As an undergraduate at Pomona College, I received a BA in Cognitive Science with a subconcentration in Computer Science and minor in Philosophy. I then received my MA and PhD in Cognitive Psychology at UCLA, with a specialization in Computational Cognition and minor in Quantitative Psychology.

During graduate school, I was president of Psychology in Action, where I also blogged and organized five interdisciplinary symposia.


  • Math Learning
  • Mathematical Cognition
  • Causal Learning
  • Programming Instruction
  • Statistical Reasoning
  • Open Science


  • PhD in Cognitive Psychology (Computational Cognition), 2016

    University of California, Los Angeles

  • MA in Cognitive Psychology, 2011

    University of California, Los Angeles

  • BA in Cognitive Science, 2009

    Pomona College




Department of Educational Psychology,
University of Minnesota

Jan 2019 – Present Minneapolis, MN


Visiting Assistant Professor

Psychology Department, Macalester College

Sep 2018 – Dec 2018 Saint Paul, MN
Taught introductory psychology and cognitive science courses.

Postdoctoral Scholar & Lecturer

Psychology Department, UCLA

Jul 2016 – Jun 2018 Los Angeles, CA

  • Postdoctoral Scholar with Patricia Cheng in the Reasoning Lab researching causal learning and developing multimedia video lessons to teach algebraic concepts.
  • Taught multiple undergraduate classes in cognitive psychology, research methods, and programming in the Psychology Department.

Most Recent Publications

Decoding fact fluency and strategy flexibility in solving one-step algebra problems: An individual differences analysis

A novel response time paradigm allows us to ‘decode’ individual differences in solving one-step algebra problems (e.g., x - 7 = 2). We find that adults vary in their strategy choices and flexibility for these problems. We also extend problem size effects from arithmetic to the algebraic context, but only for addition (not subtraction) problems.

Causal invariance guides inference of empirical integration rules

We report an experiment in which people observe causal data that follow either conjunctive or disjunctive decomposition functions. Among reasoners who generalize their empirical function (conjunction or disjunction) to novel stimuli in the same domain, they nonetheless apply causal invariance to the ‘whole cause’ level. This appropriate ‘switch’ between levels of representation is consistent with having analytic knowledge of causal invariance that guides causal learning.

Use of clustering in human solutions of the traveling salesperson problem

We report an experiment in which U.S. undergraduate participants cluster 40 different instances of points, and later treat the same set of instances as Traveling Salesperson Problems (TSPs) to solve. Strikingly, participants’ TSP solutions perfectly followed their clusters for 52% of the stimuli, and this was more likely the more statistically clustered the points were. This provides strong evidence that humans solve TSPs efficiently by use of clustering.

Categorical Perception of p-Values

Statistically trained graduate students are more likely to judge two p-values as being different (vs. similar) when they cross the traditional .05 significance boundary (e.g., .046 vs. .052), compared to when they did not (.026 vs. .032), after controlling for known numerical cognition effects. This is consistent with a categorical perception effect for p-values at the .05 boundary.

Causal invariance as a tacit aspiration: Analytic knowledge of invariance functions

Given the same prior knowledge and training data, people make different intuitive causal judgments according to their perception of the outcome variable type as either continuous or binary. Our causal invariance hypothesis explains why this reasoning is adaptive to our representation-dependent mind.

Blog Posts

Psychology Classics: Wason Selection Task (Part II)

Part II of the history of the Wason Selection Task and what it tells us about reasoning.

Psychology Classics: Wason Selection Task (Part I)

Part I of the history of the Wason Selection Task and what it tells us about reasoning.

Music Cognition

Short summary of music cognition research.

Desirable Difficulties in Math Teaching

How desirable difficulties apply to math education.

Desirable Difficulties in the Classroom

When making learning harder makes learning last.




An R package with Ethan C. Brown for tidyverse-friendly simulations and power analysis.


  • +1 (612) 301-3067
  • 250 Education Sciences Building, 56 East River Rd, Minneapolis, MN 55455
  • Enter building and go down to level 1, office 176 on the river side.