Cognition and Decision Making Processes

We study cognition by conducting human behavioral experiments and developing computational models that explain the observed behaviors. Based on the results from human experiments and computer simulations, we develop adaptive systems or machines that display effective learning in varying environments and devise decision and training aids that improve people's performance.

One application of this work is detecting hostile intent: How can we detect an attack before it is too late? We are particularly interested in asymmetric situations, where individuals or small teams are potential attackers. Human movement may reveal intent early enough to allow for interception. We are finding that movement patterns can provide early clues about an intended destination, even if the subject is trying to deceive.

A primary goal of this line of work is to apply our understanding of human cognition to help solve real world problems, such as detecting hostile intent, distributing sensors, understanding social networks, and improving education.

 

Publications

Corter, J. E., C. J., Esche, S., Chassapis, C., Ma, J., & Nickerson, J. V. Process and Learning Outcomes from Remotely-Operated, Simulated, and Hands-On Student Laboratories. Computers & Education (accepted).

Zahner, D., Nickerson, J. V., Tversky, B., Corter, J. E., and Ma. J.  A Fix for Fixation? Re-representing and abstracting as creative processes in the design of information systems, Artificial Intelligence in Engineering Design, Analysis and Manufacturing, (eds) Maher, M., Kim, Y. S., and Bonnardel, N., 24, 2, 2010, in press. 

Nickerson, J. V., Tversky, B., Corter, J.E., Yu, L. and Mason, D., Thinking with Networks, Proceedings of the 32st Annual Conference of the Cognitive Science Society, 2010

Yu, L.  Crowd Creativity through Combination, The ninth Annual SIG IS Cognitive Research Exchange Workshop, 2010

Voiklis, J. and Corter, J. E. (under review). Conventional Wisdom: Negotiating Conventions of Reference Enhances Category Discovery.

Kapur, M., Voiklis, J., & Kinzer, C. (under revision). A complexity-grounded model of or the emergence of convergence in CSCL groups. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing Interactions in CSCL: Methodologies, Approaches and Issues. Springer.

Kapur, M. & Voiklis, J. (under revision). Large-Scale Collective Dynamics: Theoretical and Methodological Arguments for Expanding the Computer-Supported Collaborative Learning (CSCL) Research Agenda. Manuscript submitted for publication.

Sakamoto, Y., & Love, B. C. (2009). You only had to ask me once: Long-term retention requires direct queries during learning. In N. Taatgen, H. van Rijn, L.

Schomaker, and J. Nerbonne (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.

Sakamoto, Y., Ma, J., and Nickerson, J. V. (2009). 2377 people like this article: The influence of others' decisions on yours. In N. Taatgen, H. van Rijn, L. Schomaker, and J. Nerbonne (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.

Sakamoto, Y., Jones, M., & Love, B. C. (2008). Putting the psychology back into psychological models: Mechanistic vs. rational approaches. Memory & Cognition, 36, 1057-1065.

Kapur, M., Voiklis, J., and Kinzer, C. K. (2008). Sensitivities to early exchange in synchronous computer-supported collaborative learning (cscl) groups. Computers & Education, 51(1):54-66.

Matsuka, T. Sakamoto, Y, Chouchourelou, A. & Nickerson, J. V. Toward a descriptive cognitive model of human learning. Neurocomputing. (71:13-15), August 2008, pp. 2446-2455.

Matsuka, T., Sakamoto, Y., & Chouchourelou, A. (2008). Modeling a flexible representation machinery of human concept learning. Neural Networks, 21, 289-302.

Matsuka, T., Sakamoto, Y., Nickerson, J.V., & Chouchourelou, A. "A Cognitive Model of Multi-Objective/Multi-Concept Formation", Lecture Notes on Computer Science (LNCS) Vol. 4131, (pp. 563 - 572). Berlin: Springer-Verlag.

Nickerson, J.V.  "Assembling Sensor Networks", Proceedings of the 40th Annual Hawaii International Conference on System Sciences, 2007.

Sakamoto, Y. & Matsuka, T. (2007). Incorporating forgetting in a category learning model. In Proceedings of International Joint Conference on Neural Networks.

Matsuka, T., & Sakamoto, Y. (2007). Integrating flexible representation machinery in a model of human concept learning. In Proceedings of International Joint Conference on Neural Networks.

Matsuka, T., & Sakamoto, Y. (2007). A cognitive model that describes the influence of prior knowledge on concept learning. Artificial Neural Networks, ICANN07, Lecture Notes in Computer Science (LNCS). Berlin: Springer-Verlag.

Matsuka, T., & Sakamoto, Y. (2007). A model of concept formation with a flexible representation system. Advances in Neural Networks, Lecture Notes in Computer Science (LNCS), Vol. 4491 Part I (pp. 1139-1147). Berlin: Springer-Verlag.

Corter, J. E., Nickerson, J. V., Esche, S. K. Chassapis, C. Im, S., Ma, J. "Constructing Reality: A study of remote, hands-on and simulated laboratories", ACM Transactions on Computer Human Interaction (14: 2) Article 7, August, 2007.

Nickerson, J.V., Corter, J.E., Esche, S.K., and Chassapis, C. "A Model for Evaluating the Effectiveness of Remote Engineering Laboratories and Simulations in Education," Computers & Education, (49:3) 2007, pp. 708-725.

Sakamoto, Y., & Love, B. C. (2006). Vancouver, Toronto, Montreal, Austin: Enhanced oddball memory through differentiation, not isolation. Psychonomic Bulletin & Review, 13, 474-479.

Jian, J-Y., Matsuka, T., & Nickerson, J. V. "Recognizing Deception in Trajectories". Proceedings of the 28th Annual Conference of the Cognitive Science Society, 2006.

Sakamoto, Y. (2006). Acquiring New Speech Sounds by Clustering. In R. Sun and N. Miyake (Eds.), Proceedings of the 5th International Conference of the Cognitive Science Society.

Sakamoto, Y., Love, B. C., & Jones, M. (2006). Tracking variability in learning: Contrasting statistical and similarity-based accounts. In R. Sun and N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society.

Sakamoto, Y., & Love, B. C. (2006). Sizable sharks swim swiftly: Learning correlations through inference in a classroom setting. In R. Sun and N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society.

Jian, J-Y., Matsuka, T., & Nickerson, J. V. "Towards Deceptive Intention: Finding Trajectories and Its Analysis", Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, 2006.

Matsuka, T. & Nickerson, J. V. "Modeling human hypothesis testing behaviors with simulated evolutionary processes", Proceedings of the IEEE World Congress on Computational Intelligence International Conference on Evolutionary Computation. 2006.

Matsuka, T., Nickerson, J. V., & Jian, J-Y. "A prototype model that learns and generalize Medin, Alton, Edelson, & Frecko (1982) XOR category structure like humans do", Proceedings of the 28th Annual Conference of the Cognitive Science Society, Vancouver, Canada. 2006.

Ma, J., and Nickerson, J. V., "Hands-on, Simulated and Remote Laboratories:  A Comparative Literature Review", ACM Computing Surveys, (38:3) Article 7, 2006, pp. 1-24. 

Corter, J.E., Nickerson, J.V., Esche, S.K., and Chassapis, C. "Remote vs. Hands-on Labs: Immersion, Learning modes, and Student Preferences," Frontiers in Education, 2004, pp. F1G17-21.

Nickerson, J.V., and Reilly, R.R. "A Model for Investigating the Effects of Machine Autonomy on Human Behavior," Proceedings of the 37th Annual Hawaii International Conference on System Sciences, 2004.