The Modeling Cognition page was last changed on February 13, 2018.
Our goal is to understand how the mind works.
People in our lab help move the field of cognitive science. We describe human cognition by modeling its output and process through precise, computational, predictive descriptions. Because humans exist in society, we show how cognition and communication allow us to function as teams and in networks. Our projects are focused on models that can explain human behavior for testing human-computer interfaces, to understand network formation, how moderators such as caffeine and stress influence behavior, and to serve as colleagues and opponents in simulations. Our cognitive simulations show how the human mind interacts with networked teams and communities and how these evolved properties give rise to emergent group cognition. We support this work with software to build models more easily, record behavior, and to build tutors, and through courses related to cognitive science and IST in general.
Feel free to access our growing list of papers for your own interests. If you would like to know more, please contact us.
Lab Directors: Frank E. Ritter, David Reitter
We are running several major projects. While some openings have been filled, there are still opportunities for future graduate students. Please be in touch for more information.
- Computational, distributed accounts of human memory: improving cognitive models (2017-)
(NSF, Perception, Action, Cognition \& Robust Intelligence, PI: D. Reitter)
Natural Language Processing, neural models of memory, distributed representations (Postdoc position available)
- Building trauma triage tutors for Air Force nurses and extending learning theory
(Air Force AFRL, PI: Ritter with Charles River Analysis)
- Maintenance Training Under Uncertainty: Expanding A Smart Tutoring System to Support Acquisition and Retention of Skills
(Navy ONR, PI: Ritter with Charles River Analysis)
- Updating the Militarized Dispute Data Through Crowdsourcing: MID5, 2011-2017
(NSF, Political Science, Co-PI: D. Reitter, collaboration with PolSci at PSU and UT Dallas)
Natural Language Processing, Deep Semi-Supervised Learning (
Postdoc position FILLED)
- Modeling syntactic priming in language production according to corpus data
(NSF, Linguistics, PI: D. Reitter)
- Alignment in web-forum discourse: computational models of adaptation and language change
(NSF CRII, Computer Science, PI: D. Reitter)