This interdisciplinary research focuses on the development of theoretical and computational cognitive theories of explanatory and deductive reasoning. We use techniques such as computational cognitive modeling, human subjects experimentation, eye-tracking, and protocol analysis to build models of how people reason. We also build interfaces and prototypes to explore computer science ideas advanced by our theoretical frameworks. Our group consists of cognitive psychologists and computer scientists. Its primary goal is to build psychological and computational theories of complex inferential behavior by attempting to scale up current theories to more complex embodied domains. We use what we know about cognition to build better artificial intelligence systems.

References

• Hiatt L, Narber C, Bekele E, Khemlani S, Trafton G: Human modeling for human-robot collaboration. International Journal of Robotics Research 36: 580-596, 2017.
• Johnson-Laird PN, Khemlani S, Goodwin GP: Logic, probability, and human reasoning. Trends in Cognitive Sciences 19: 201-214, 2015
• Khemlani S: Automating human inference. Proceedings of the 2nd IJCAI Workshop on Bridging the Gap Between Human and Automated Reasoning, 1-4, 2016
• Khemlani S, Johnson-Laird PN: Why machines don't (yet) reason like people. Ku¨nstliche Intelligenz, 2019.
• Khemlani S, Harrison AM, Trafton JG: Episodes, events, and models. Frontiers in Human Neuroscience 9: 590, 1-13, 2015
• Khemlani S, Mackiewicz R, Bucciarelli M, Johnson-Laird PN: Kinematic mental simulations in abduction and deduction. Proceedings of the National Academy of Sciences 110: 16766-16771, 2013

 

key words

Reasoning; Cognition; Deduction; Mental models; Explanation; Thinking; Cognitive modeling; Cognitive science; Robotics; Causality;

Citizenship:  Open to U.S. citizens and permanent residents

Level:  Open to Postdoctoral applicants