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Pirolli P, Lebiere C, Orr M. (2023) A computational cognitive model of behaviors and decisions that modulate pandemic transmission: Expectancy-value, attitudes, self-efficacy, and motivational intensity. Frontiers in Psychology. 13: 981983 |
Mitsopoulos K, Somers S, Schooler J, et al. (2021) Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture. Topics in Cognitive Science |
Orr MG, Lebiere C, Stocco A, et al. (2019) Multi-scale resolution of neural, cognitive and social systems Computational and Mathematical Organization Theory. 25: 4-23 |
Pirolli P, Mohan S, Venkatakrishnan A, et al. (2017) Implementation Intention and Reminder Effects on Behavior Change in a Mobile Health System: A Predictive Cognitive Model. Journal of Medical Internet Research. 19: e397 |
Hernández MA, Sharit J, Pirolli P, et al. (2017) Adapting Information Search Tools for use by Health Consumers: Challenges and Lessons for Software Designers International Journal of Human–Computer Interaction. 34: 445-456 |
Zhang Y, Paik J, Pirolli P. (2015) Reinforcement learning and counterfactual reasoning explain adaptive behavior in a changing environment. Topics in Cognitive Science. 7: 368-81 |
Chelian SE, Paik J, Pirolli P, et al. (2015) Reinforcement learning and instance-based learning approaches to modeling human decision making in a prognostic foraging task 5th Joint International Conference On Development and Learning and Epigenetic Robotics, Icdl-Epirob 2015. 116-122 |
Paik J, Pirolli P. (2015) ACT-R models of information foraging in geospatial intelligence tasks Computational and Mathematical Organization Theory. 21: 274-295 |
Vydiswaran VGV, Zhai C, Roth D, et al. (2015) Overcoming bias to learn about controversial topics Journal of the Association For Information Science and Technology. 66: 1655-1672 |
Chelian SE, Ziegler MD, Pirolli P, et al. (2014) Learning to prognostically forage in a neural network model of the interactions between neuromodulators and prefrontal cortex Energy Procedia. 41: 32-39 |