Modeling Cognition

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 define computational models to explain human behavior for testing human-computer interfaces, describe language-based interaction through dialogue, examine network formation, test how moderators such as caffeine and stress influence behavior, serve as colleagues and opponents in simulations, and improve artificial intelligence and machine learning through a better understanding of human memory. 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 information science 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
Research Statement:  David Reitter

We are running several major projects. There are opportunities for future graduate students and post-docs. Please be in touch for more information.

  • ClassInSight: Insight on Teacher Learning by Scaffolding Noticing and Reflection (2018-)
    (McDonnell Foundation; contact: Reitter, PIs: Clarke (UCSD), Ogan (CMU))
    Cognitive Modeling, Dialogue (Postdoc position available as of 11/2017)
  • 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 as of 11/2017)
  • 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)
  • Alignment in web-forum discourse: computational models of adaptation and language change
    (NSF CRII, Computer Science, PI: D. Reitter)