Publications in Individual Differences

Dimov, C. M., Anderson, J. R., & Betts, S. A.(2024). Tight resource-rational analysis. Cognitive Systems Research 86. https://doi.org/10.1016/j.cogsys.2024.101239

Xu, Y., Stocco, A. Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task. Comput Brain Behav (2021). https://doi.org/10.1007/s42113-021-00102-5

Stocco, A., Prat, C. S. Graham, L. K., & (2021). Individual differences in reward learning processes predict fluid reasoning abilities. Cognitive Science, 45(2), e12941.

Ceballos, J. M., Stocco, A., & Prat, C. S. (2020). The role of basal ganglia reinforcement learning in lexical ambiguity resolution. Topics in Cognitive Science, 12(1), 402–416.

Xu, Y. Reliable Idiographic Parameters From Noisy Behavioral Data: The Case of Individual Differences in a Reinforcement Learning Task.

Anderson, J.R., Bothell, D., Fincham, J.M., & Moon, J. (2016). The Sequential Structure of Brain Activation Predicts Skill. Neuropsychologia, 81, 94-106.

Deline S. & Juhel J. (2012). Cognitive modeling of different processing modes in task switching: towards an explanation of the effects of aging on switching cost. In N. Rußwinkel, U. Drewitz, & H. van Rijn (Eds.), Proceedings of ICCM 2012, 11th international Conference on Cognitive Modeling (pp. 281-282). Universitätsbibliothek, Berlin.

Gunzelmann, G., Moore, L. R., Gluck, K. A., Van Dongen, H. P. A., & Dinges, D. F. (2009). Examining sources of individual variation in sustained attention. In N. Taatgen, & H. van Rijn (Eds.), Proceedings of the Thirty-First Annual Meeting of the Cognitive Science Society (pp. 608-613). Austin, TX: Cognitive Science Society.

Gunzelmann, G., Moore, L. R., Gluck, K. A., Van Dongen, H. P. A., & Dinges, D. F. (2008). Individual differences in sustained vigilant attention: Insights from computational cognitive modeling. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the Thirtieth Annual Meeting of the Cognitive Science Society (pp. 2017-2022). Austin, TX: Cognitive Science Society.

Ritter, F. E., Schoelles, M., Klein, L. C., & Kase, S. E. (2007). Modeling the range of performance on the serial subtraction task. In Proceedings of the 8th International Conference on Cognitive Modeling. Lewis, R. L., Polk, T. A., Laird, J. L., (eds.). 299-304. Oxford, UK: Taylor & Francis/Psychology Press.

Serna, A., Pigot, H., & Rialle, V. (2007). Modeling the progression of Alzheimer's disease for cognitive assistance in smart homes. User Modeling and User-Adapted Interaction, 17, 415-438.

Ritter, F. E. & Norling, E. (2006) Including Human Variability in a Cognitive Architecture to Improve Team Simulation. In R. Sun (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press; New York, NY, 417-427.

Ritter, F. E., Reifers, A., Klein, L. C., Quigley, K., & Schoelles, M. (2004). Using cognitive modeling to study behavior moderations: Pre-task appraisal and anxiety. In Proceedings of the Human factors and Ergonomics Society (pp. 2121-2125). Santa Monica, CA: Human Factor and Ergonomics Society.

Rehling, J., Lovett, M., Lebiere, C., Reder, L. M., & Demiral, B. (2004) Modeling complex tasks: An individual difference approach. In proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 1137-1142) . August 4-7, Chicago, USA

Rehling, J., Demiral, B., Lebiere, C., Lovett, M., & Reder, L. M. (2003). Modeling individual difference factors in a complex task environment. In F. Detje, D. Doerner, & H. Schaub (Eds.), In Proceedings of the Fifth International Conference on Cognitive Modeling (pp. 287-288). Bamberg, Germany: Universitats-Verlag Bamberg.

Daily, L. Z., Lovett, M. C., & Reder, L. M. (2001). Modeling individual differences in working memory performance: A source activation account in ACT-R. Cognitive Science 25, 315-353.

Taatgen, N. A. (2001). A model of individual differences in learning Air Traffic Control. In Proceedings of the Fourth International Conference on Cognitive Modeling, pp. 211-216. Mahwah, NJ: Lawrence Erlbaum Associates.

Lovett, M. C., Daily, L. Z., & Reder, L. M. (2000). A source activation theory of working memory: Cross-task prediction of performance in ACT-R. Cognitive Systems Research, 1, 99-118.

Lovett, M. C., Reder, L. M., & Lebiere, C. (1999). Modeling working memory in a unified architecture: An ACT-R perspective. In A. Miyake & P. Shah (Eds.) Models of Working Memory, pp. 135-182. Cambridge, MA: Cambridge.

Jongman, L. & Taatgen, N. A. (1999). An ACT-R model of individual differences in changes in adaptivity due to mental fatigue (pp. 246-251). In Proceedings of the twenty-first annual conference of the cognitive science society. Mahwah, NJ: Erlbaum.

Lovett, M. C., Reder, L. M., & Lebiere, C. (1997). Modeling individual differences in a digit working memory task. In Proceedings of the Nineteenth Conference of the Cognitive Science Society, pp. 460-465. Mahwah, NJ: Erlbaum.

Corbett, A. T. & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253-278.

Corbett, A. T., Anderson, J. R., Carver, V. H. & Brancolini, S. A. (1994). Individual differences and predictive validity in student modeling. In A. Ram & K. Eiselt (Eds.) The Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum.

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Department of Psychology, Carnegie Mellon University