Senior Research Scientist

Kevin is a cognitive scientist excited about the many ways our improving understanding of the human mind can and will have broad societal benefit. His research spans basic and applied science and technology, with particular emphases on computational theories of the human cognitive architecture, cognitive robustness and resilience in response to fatigue and other stressors, decision making, personalized instruction, formal model evaluation, comparison, and selection, interactive task learning, and lifespan cognition.

He earned his B.A. in psychology at Trinity University and his M.S. and Ph.D. in cognitive psychology at Carnegie Mellon University. Previously Kevin was a federal civilian scientist with the Air Force Research Laboratory (AFRL), was a visiting scientist (Gastwissenschaftler) at the Max Planck Institute for Human Development in Berlin, worked for nearly four years in private sector R&D, and held adjunct faculty appointments at ASU Polytechnic, Wright State University, and the University of Central Florida. Kevin has authored more than 100 peer-reviewed publications, is an inventor on two U.S. patents, is lead editor on two books, including Interactive Task Learning: Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions, and is a Fellow of the Psychonomic Society.

Professional service, scientific community building, teaching, mentorship and outreach have also figured prominently in Kevin’s career. He had a lead role in the organization and management of 15 international conferences and workshops. He served 14 years on the Governing Board of the Cognitive Science Society, including three years on the Executive Council and as Society President in 2020-2021. Kevin is currently an associate editor for the Cognitive Science section of the journal Frontiers in Psychology. He has mentored dozens of students and early career researchers, from college undergraduates to postdocs, as interns and salaried trainees, and also by serving on thesis and dissertation committees. Since arriving at IHMC in 2023, Kevin has contributed actively to the institute’s public outreach activities, leading three Science Saturdays to engage Pensacola youth with cognitive science, co-hosting a STEM-Talk podcast interview with Hans Van Dongen on How Fatigue and Sleep Loss Lead to Cognitive Deficits, and delivering an evening lecture on Things We Now Know about the Human Mind.

Research Areas and Selected Publications

Cognitive Architectures, Modeling Methods, and Generative AI

What makes a computational model an explanation of cognition rather than simply an accurate predictor of behavior? This research thread treats cognitive architectures as integrated, mechanistic theories of mind and develops methods for testing, fitting, comparing, and validating them. Generative AI and large language models sharpen these questions: they can produce striking behavioral predictions, but prediction alone does not establish explanatory adequacy, psychological validity, or domain-generality. Current work therefore examines how cognitive architectures and foundation models can be evaluated, combined, and constrained in ways that preserve the scientific goals of computational cognitive science.

Orr, M., Cranford, E. A., Ford, K., Gluck, K. A., Hancock, W., Lebiere, C., Pirolli, P., Ritter, F., & Stocco, A. (2026). Zenon’s demon and the denial of domain-generality for transformer-based computational models of human behavior. arXiv. https://doi.org/10.48550/arXiv.2608.12396

Orr, M., Cranford, E. A., Ford, K., Gluck, K. A., Hancock, W., Lebiere, C., Pirolli, P., Ritter, F., & Stocco, A. (2025). Not even wrong: On the limits of prediction as explanation in cognitive science. arXiv. https://doi.org/10.48550/arXiv.2510.03311

Stocco, A., Mitsopoulos, K., Yang, Y. C., Hake, H. S., Haile, T., Leonard, B., & Gluck, K. A. (2024). Fitting, evaluating, and comparing cognitive architecture models using likelihood: A primer with examples in ACT-R. arXiv. https://doi.org/10.48550/arXiv.2410.18055

Mitsopoulos, K., Bose, R., Mather, B., Bhatia, A., Gluck, K., Dorr, B., Lebiere, C., & Pirolli, P. (2024). Psychologically-valid generative agents: A novel approach to agent-based modeling in social sciences. Proceedings of the AAAI Symposium Series, 2(1), 340-348. https://doi.org/10.1609/aaaiss.v2i1.27698

Blaha, L. M., & Gluck, K. A. (2023). Model validation, comparison, and selection. In R. Sun (Ed.), The Cambridge handbook of computational cognitive sciences (pp. 1165-1200). Cambridge University Press. https://doi.org/10.1017/9781108755610.042

Gluck, K. A., & King, J. M. (2022). Cognitive architectures for human factors in aviation and aerospace. In J. R. Keebler, E. H. Lazzara, K. A. Wilson, & E. L. Blickensderfer (Eds.), Human Factors in Aviation and Aerospace, 3rd Edition (pp. 279-307). Cambridge, MA: Academic Press.

Veksler, V. D., Myers, C. W., & Gluck, K. A. (2015). Model flexibility analysis. Psychological Review, 122(4), 755-769. https://doi.org/10.1037/a0039657

Gluck, K. A., Stanley, C. T., Moore, L. R., Reitter, D., & Halbrugge, M. (2010). Exploration for understanding in cognitive modeling. Journal of Artificial General Intelligence, 2(2), 88-107. https://doi.org/10.2478/v10229-011-0011-7

Gluck, K. A., & Pew, R. W. (Eds.). (2005). Modeling human behavior with integrated cognitive architectures: Comparison, evaluation, and validation. Lawrence Erlbaum Associates.

Interactive Task Learning and Human-Machine Co-Learning

How can humans and intelligent machines rapidly teach and learn entirely new tasks from one another through natural interaction? Interactive Task Learning (ITL) frames learning and instruction as bidirectional processes that require language, perception, action, reasoning, memory, common ground, and metacognition to work together. The long-term objective is human-machine co-learning and co-teaching in which agents and robots acquire task knowledge in situ, explain what they know, ask productive questions, and adapt to novel circumstances rather than remaining confined to skills represented in prior programming or training data.

Gluck, K. A., & Laird, J. E. (Eds.). (2019). Interactive task learning: Humans, robots, and agents acquiring new tasks through natural interactions (Strungmann Forum Reports, Vol. 26). MIT Press.

Gluck, K. A., & Laird, J. E. (2019). Looking forward to interactive task learning. In K. A. Gluck & J. E. Laird (Eds.), Interactive task learning: Humans, robots, and agents acquiring new tasks through natural interactions (pp. 1-7). MIT Press.

Shah, J. A., Gluck, K. A., Belpaeme, T., Koedinger, K. R., Rohlfing, K. J., van der Maas, H. L. J., Van Eecke, P., VanLehn, K., Vollmer, A., & Yee-King, M. (2019). Task instruction. In K. A. Gluck & J. E. Laird (Eds.), Interactive task learning: Humans, robots, and agents acquiring new tasks through natural interactions (pp. 169-192). MIT Press.

Laird, J. E., Gluck, K. A., Anderson, J. R., Forbus, K., Jenkins, O., Lebiere, C., Salvucci, D., Scheutz, M., Thomaz, A., Trafton, G., Wray, R., Mohan, S., & Kirk, J. (2017). Interactive task learning. IEEE Intelligent Systems, 32(4), 6-21.

Hough, A. R., & Gluck, K. A. (2019). The understanding problem in cognitive science. Advances in Cognitive Systems, 8, 13-32.

Effects of Stress on Human Cognition and the emerging Compu-Cognomics

How do fatigue, sleep loss, acute stress, and other perturbations alter the mechanisms that support cognition, and why are some individuals more resilient than others? A long-running line of work uses cognitive architectures and mathematical models to move from descriptive performance decrements toward mechanistic accounts of degraded attention, memory access, cognitive control, and multitasking. The emerging compu-cognomics approach extends this logic by treating fitted cognitive-model parameters as interpretable computational phenotypes that may help connect stress exposure, physiology, individual differences, and observed performance across tasks and time.

Cranford, E. A., Gluck, K. A., Lieberman, H. R., Margolis, L. M., Rood, J. C., Pasiakos, S. M., McAdam, J., Siedlik, J. A., & Bamman, M. The compu-cognome as a cognitive architecture-based framework for explaining individual differences: An application to human stress response. Manuscript in preparation.

O’Connor, W., Fredette, L., Judy, K., Manion, C., Azari, D. P., Cranford, E. A., & Gluck, K. A. (2025). Effects of stress on cognitive performance and resilience. In Proceedings of the 2025 Systems and Information Engineering Design Symposium (pp. 324-329). IEEE. https://doi.org/10.1109/SIEDS65500.2025.11021138

Cranford, E. A., Judy, K., Manion, C., O’Connor, W., Mortimore, K., & Gluck, K. A. (2024). A prospective look at the role of the compu-cognome for explaining warfighter stress resilience in retrospective cognitive performance data [Poster presentation]. Military Health System Research Symposium.

Gunzelmann, G., Veksler, B. Z., Walsh, M. M., & Gluck, K. A. (2015). Understanding and predicting the cognitive effects of sleep loss through simulation. Translational Issues in Psychological Science, 1(1), 106-115. https://doi.org/10.1037/tps0000017

Gunzelmann, G., Gluck, K. A., Moore, L. R., & Dinges, D. F. (2012). Diminished access to declarative knowledge with sleep deprivation. Cognitive Systems Research, 13(1), 1-11. https://doi.org/10.1016/j.cogsys.2010.09.001

Gunzelmann, G., Moore, L. R., Gluck, K. A., Van Dongen, H. P. A., & Dinges, D. F. (2011). Fatigue in sustained attention: Generalizing mechanisms for time awake to time on task. In P. L. Ackerman (Ed.), Cognitive fatigue: Multidisciplinary perspectives on current research and future applications (pp. 83-101). American Psychological Association.

Gunzelmann, G., Moore, L. R., Salvucci, D. D., & Gluck, K. A. (2011). Sleep loss and driver performance: Quantitative predictions with zero free parameters. Cognitive Systems Research, 12(2), 154-163. https://doi.org/10.1016/j.cogsys.2010.07.009

Gunzelmann, G., Gross, J. B., Gluck, K. A., & Dinges, D. F. (2009). Sleep deprivation and sustained attention performance: Integrating mathematical and cognitive modeling. Cognitive Science, 33(5), 880-910. https://doi.org/10.1111/j.1551-6709.2009.01032.x

Gunzelmann, G., Byrne, M. D., Gluck, K. A., & Moore, L. R. (2009). Using computational cognitive modeling to predict dual-task performance with sleep deprivation. Human Factors, 51(2), 251-260. https://doi.org/10.1177/0018720809334592

Lifespan Cognition and Cognitive Health

How can cognitive function be characterized efficiently across adulthood, and which individual and modifiable health factors are meaningfully associated with cognitive outcomes? This emerging thread connects scalable cognitive assessment and computational phenotyping with broader questions about cognitive health, individual differences, and change across the lifespan. Recent work examines relationships between diet and predicted cognitive function while also drawing on methods that connect laboratory measures to performance on complex real-world tasks.

Conti, F., Gluck, K. A., Stocco, A., & Wood, T. R. (2026). Bias-reduced Regression Analysis in Nutrition (BRAiN) index: Self-reported diet and predicted cognitive function. Frontiers in Nutrition, 13, 1830204. https://doi.org/10.3389/fnut.2026.1830204

Robustness, Performance, and Decision Making

How do cognitive systems continue to function effectively when environments, information, goals, or internal states change? This research treats robustness as a measurable property of cognition and examines mechanisms that support stable performance across perturbations, including feedback, redundancy, adaptation, and flexible strategy selection. A complementary line investigates how people choose among heuristics and more complex strategies, how those strategies can be inferred from behavior, and when people switch strategies as task structure or experience changes.

Lee, M. D., & Gluck, K. A. (2021). Modeling strategy switches in multi-attribute decision making. Computational Brain & Behavior, 4, 148-163. https://doi.org/10.1007/s42113-020-00092-w

Lee, M. D., Gluck, K. A., & Walsh, M. W. (2019). Understanding the complexity of simple decisions: Modeling multiple behaviors and switching strategies. Decision, 6(4), 335-368. https://doi.org/10.1037/dec0000105

Gluck, K. A. (2019). What does it mean for psychological modeling to be more robust? Computational Brain & Behavior, 2, 154-156. https://doi.org/10.1007/s42113-019-00065-8

Walsh, M. W., & Gluck, K. A. (2016). Verbalization of decision strategies in multiple-cue probabilistic inference. Journal of Behavioral Decision Making, 29(1), 78-91. https://doi.org/10.1002/bdm.1878

Walsh, M. W., & Gluck, K. A. (2015). Mechanisms for robust cognition. Cognitive Science, 39(6), 1131-1171. https://doi.org/10.1111/cogs.12192

Walsh, M. W., Einstein, E. H., & Gluck, K. A. (2013). A quantification of robustness. Journal of Applied Research in Memory and Cognition, 2, 137-148. https://doi.org/10.1016/j.jarmac.2013.07.002

Gluck, K. A., McNamara, J. M., Brighton, H., Dayan, P., Kareev, Y., Krause, J., Kurzban, R., Selten, R., Stevens, J. R., Voelkl, B., & Wimsatt, W. C. (2012). Robustness in a variable environment. In J. R. Stevens & P. Hammerstein (Eds.), Evolution and the mechanisms of decision making (pp. 195-214). MIT Press.

Learning, Memory, and Personalized Instruction

How should practice be scheduled to maximize learning, long-term retention, and readiness while minimizing unnecessary training? This research develops quantitative models of learning and forgetting that capture effects of recency, frequency, spacing, and individual performance history, then uses those models to predict future performance and prescribe individualized training intervals. The work spans basic questions about memory dynamics and relearning as well as technology transition, including the Predictive Performance Optimizer (PPO) for personalized prediction and optimization of training schedules.

Oermann, M. H., Krusmark, M. A., Kardong-Edgren, S., Jastrzembski, T. S., & Gluck, K. A. (2022). Personalized training schedules for retention and sustainment of cardiopulmonary resuscitation skills. Simulation in Healthcare, 17(1), e59-e67. https://doi.org/10.1097/SIH.0000000000000559

Oermann, M. H., Krusmark, M. A., Kardong-Edgren, S., Jastrzembski, T. S., & Gluck, K. A. (2020). Training interval in cardiopulmonary resuscitation. PLOS ONE, 15(1), e0226786. https://doi.org/10.1371/journal.pone.0226786

Gluck, K. A., Jastrzembski, T. S., & Krusmark, M. A. (2019). Prospective comments on performance prediction for aviation psychology. In M. A. Vidulich & P. S. Tsang (Eds.), Improving aviation performance through applying engineering psychology: Advances in aviation psychology (Vol. 3, pp. 79-98). CRC Press. https://doi.org/10.4324/9780429492181-4

Sense, F., Maass, S. C., Gluck, K. A., & van Rijn, H. (2019). Within-subject performance on a real-life, complex task and traditional lab experiments: Measures of word learning, Raven matrices, tapping, and CPR. Journal of Cognition, 2(1), Article 12, 1-10. https://doi.org/10.5334/joc.65

Walsh, M. W., Gluck, K. A., Gunzelmann, G., Jastrzembski, T. S., & Krusmark, M. A. (2018). Evaluating the theoretical adequacy and applied potential of computational models of the spacing effect. Cognitive Science, 42(S3), 644-691. https://doi.org/10.1111/cogs.12602

Walsh, M. W., Gluck, K. A., Gunzelmann, G., Jastrzembski, T. S., Krusmark, M. A., Myung, J. I., Pitt, M. A., & Zhou, R. (2018). Mechanisms underlying the spacing effect in learning: A comparison of three computational models. Journal of Experimental Psychology: General, 147(9), 1325-1348. https://doi.org/10.1037/xge0000416

Jastrzembski, T. S., Gluck, K. A., & Rodgers, S. M. (2009). The Predictive Performance Optimizer: An adaptive analysis cognitive tool for performance prediction. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 53(21), 1642-1646. https://doi.org/10.1177/154193120905302102

Anderson, J. R., & Gluck, K. A. (2001). What role do cognitive architectures play in intelligent tutoring systems? In D. Klahr & S. M. Carver (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 227-262). Lawrence Erlbaum Associates.

Related technology and patents

Jastrzembski, T. S., Rodgers, S. M., Gluck, K. A., & Krusmark, M. A. (2013). Predictive performance optimizer (U.S. Patent No. 8,568,145 B2). U.S. Patent and Trademark Office. Continuation: U.S. Patent No. 8,777,628 B2 (2014).