Jeffrey K. Bye

Lecturer, Educational Psychology

University of Minnesota

Hello,

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.

Interests

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

Education

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

Experience

Lecturer

Department of Educational Psychology,
University of Minnesota

Jan 2019 – Present Minneapolis, MN

Roles:

Teach graduate courses on laboratory research methods, open source programming, and math cognition in the Educational Psychology Department.
Coordinate the Mathematics, Abstraction, Learning, & Thinking Lab.
Lab Coordinator and Affiliate Faculty at the Learning Informatics Lab.
Direct the Mathematics, Abstraction, Learning, & Thinking Lab.
Research Scientist with Michèle Mazzocco in the Math & Numeracy Lab at the Institute of Child Development since June 2019: research related to children’s and adults’ attention to numerosity.
Research Associate at the Research Methodology & Consulting Center from June 2019 to December 2021: co-taught workshops, organized open science events, and provided research and statistical consulting.

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

Roles:

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.

Featured Publications

Jeffrey K. Bye, Rina M. Harsch, Sashank Varma

July 2022 Journal of Numerical Cognition

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.

PDF DOI

V. N. Vimal Rao, Jeffrey K. Bye, Sashank Varma

April 2022 Topics in Cognitive Science

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.

DOI

Michèle M. M. Mazzocco, Jenny Yun-Chen Chan, Jeffrey K. Bye, Emily R. Padrutt, Taylor Praus-Singh, Sarah Lukowski, Ethan Brown, Rachel E. Olson

September 2020 Mathematical Thinking and Learning

Attention to numerosity varies across individuals and task contexts

When asked to identify one or more matches to a target picture from an array of four options, the frequency with which preschoolers and adults identify a numerosity-based match varies as a function of the features on which the remaining match options are based (i.e., how salient they are).

DOI

Jeffrey K. Bye, Pei-Jung Chuang, Lauren E. Anthony, Patricia W. Cheng

April 2020 In ICLS 2020 Proceedings

Teaching the purpose and meaning of algebraic variables through systems-of-equations story problems: Multimedia approaches

Varying instructional method across conditions, we developed multimedia story sequences with systems-of-equations problems whose solution requires using variables.

PDF Link

See all publications

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.

Jeffrey K. Bye, Rina M. Harsch, Sashank Varma

PDF DOI

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.

Jeffrey K. Bye, Patricia W. Cheng

PDF Link

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.

Vijay Marupudi, Rina M. Harsch, V. N. Vimal Rao, Jimin Park, Jeffrey K. Bye, Sashank Varma

PDF Link

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.

V. N. Vimal Rao, Jeffrey K. Bye, Sashank Varma

DOI

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.

Jooyong Park, Shannon McGillivray, Jeffrey K. Bye, Patricia W. Cheng

PDF DOI