Trisha Van Zandt
Neuroscience · The Ohio State University
Publications
74
Citations
7,871
Est. group size
—
Recurring co-author estimate
Active years
36
Publishing since 1990
Trisha Van Zandt works on mathematical and statistical modeling of human cognition, particularly how people make decisions and respond to stimuli over time (response times). Her work develops and applies Bayesian statistical methods, including techniques for approximating complex probability calculations, to better understand mental processes such as attention, memory, and decision-making. This research combines cognitive psychology with quantitative modeling and simulation-based statistics.
Publication output peaked around 2018 with a burst of related book chapters, then declined and has remained low and irregular over the past five years, averaging under two publications per year.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Redefine statistical significance
Artefactual Field Experiments · 2025
- Correction: Assessing the distortions introduced when calculating d’: A simulation approach
Behavior Research Methods · 2025
- Assessing the distortions introduced when calculating d’: A simulation approach
Behavior Research Methods · 2024
- Cognitive-attentional mechanisms of cooperation—with implications for attention-deficit hyperactivity disorder and cognitive neuroscience
Cognitive Affective & Behavioral Neuroscience · 2023
- Approximate Bayesian Computation
Cambridge University Press eBooks · 2023
- Mutual interference in working memory updating: A hierarchical Bayesian model
Journal of Mathematical Psychology · 2022
- Hierarchical Hidden Markov Models for Response Time Data
Computational Brain & Behavior · 2020
- Preregistration of Modeling Exercises May Not Be Useful
Computational Brain & Behavior · 2019
- A Bayesian race model for response times under cyclic stimulus discriminability
The Annals of Applied Statistics · 2019
- Likelihood-Free Methods for Cognitive Science
Computational approaches to cognition and perception · 2018
- Approximating Bayesian Inference through Model Simulation
Trends in Cognitive Sciences · 2018
- Likelihood-Free Algorithms
Computational approaches to cognition and perception · 2018
- Applications
Computational approaches to cognition and perception · 2018
- Conclusions
Computational approaches to cognition and perception · 2018
- Motivation
Computational approaches to cognition and perception · 2018
- Computational approaches to cognition and perception×8
- Computational Brain & Behavior×3
- Trends in Cognitive Sciences×2
- Journal of Mathematical Psychology×2
- Behavior Research Methods×2
- Darryl W. Schneider
Neuroscience · Purdue University West Lafayette
- Yanjun Liu
Neuroscience · Indiana University
- Rachael Gwinn
Neuroscience · The Ohio State University
- Melissa T. Buelow
Neuroscience · The Ohio State University
- Seah Chang
Neuroscience · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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