Cognitive cost and information gain trade off in a large-scale number guessing game. - Felix Jedidja Binder; Cameron Jones; Robert A. Kaufman; Naomi T. Lin; Crystal R. Poole; Ed Vul (CogSci, 2021)

Expert-Informed, User-Centric Explanations for Machine Learning. - Michael J. Pazzani; Severine Soltani; Robert A. Kaufman; Samson Qian; Albert Hsiao (AAAI, 2022)

Cognitive Differences in Human and AI Explanation. - Robert A. Kaufman; David Kirsh (CogSci, 2022)

User-Centric Enhancements to Explainable AI Algorithms for Image Classification. - Severine Soltani; Robert A. Kaufman; Michael J. Pazzani (CogSci, 2022)

Who's in the Crowd Matters: Cognitive Factors and Beliefs Predict Misinformation Assessment Accuracy. - Robert A. Kaufman; Michael Robert Haupt; Steven P. Dow (Proc. ACM Hum. Comput. Interact., 2022)

Explainable AI And Visual Reasoning: Insights From Radiology. - Robert A. Kaufman; David Kirsh (CoRR, 2023)

Developing Situational Awareness for Joint Action with Autonomous Vehicles. - Robert A. Kaufman; David Kirsh; Nadir Weibel (CoRR, 2024)

What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidence. - Robert A. Kaufman; Aaron Broukhim; David Kirsh; Nadir Weibel (CHI, 2025)

Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning. - Robert A. Kaufman; Emi Lee; Manas Satish Bedmutha; David Kirsh; Nadir Weibel (CHI, 2025)