Publications in Time Perception

Moon,J., Fincham,J.M., Betts,S.A., & Anderson,J.R.,(2015). End effects and cross-dimensional interference in identification of time and length: Evidence for a common memory mechanism. Cognitive, Affective, & Behavioral Neuroscience, 15, 680-695.

Moon, J., & Anderson, J. R. (submitted). Is temporal memory special? The choose-short effect in time and length.

Moon, J., & Anderson, J. R. (2012). Modeling Millisecond Time Interval Estimation in Space Fortress Game. In Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 767-772). Sapporo, Japan.

Marewski, J. N., & Schooler, L. J. (2011). Cognitive Niches: An ecological model of strategy selection. Psychological Review, 118, 393-437.

Kennedy, W.G. and Trafton, J.G. (2011) How long is a moment: The perception and reality of task-related absences. International Journal of Social Robotics 3(3), pp 243-252.

Russwinkel, N., Urbas, L. and Thüring, M. (2011). Predicting temporal errors in complex task environments: A computational and experimental approach. Cognitive Systems Research, 12(3-4), 336-354.

Moore, L. R., Gunzelmann, G., & Brown, J. W. (2010). Modeling statistical learning and response inhibition with the change signal task. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 169-174). Philadelphia, PA: Drexel University.

Taatgen, N. A. & van Rijn, H. (2010). Nice Graphs, Good R2, but still a poor fit? How to be more sure your model explains your data. In D. D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 247-252). Philadelphia, PA: Drexel University.

Drewitz, U. & Thüring, M. (2009). Modeling the cofidence of predictions: A time based approach. In Proceedings of the 9th International Conference of Cognitive Modeling (paper 222), Manchester, United Kingdom.

Pape, N., & Urbas, L. (2009). Testing a Quantitative Model of Time Estimation in a Load Switch Scenario. In A. Howes, D. Peebles, R. Cooper (Eds.), 9th International Conference on Cognitive Modeling -- ICCM, Manchester, UK.

Pape, N., & Urbas, L. (2008). A Model of Time-Estimation Considering Working Memory Demands. In Proceedings of the 30th Annual Cognitive Science Society (pp. 1543-1548). Austin, TX: Cognitive Science Society.

Taatgen, N., Van Rijn, H., Anderson, J. R. (2007). An integrated theory of prospective time interval estimation: The role of cognition, attention, and learning. Psychological Review, 114(3), 577-598.

van Rijn, H. & Taatgen, N. A. (2007). A model of parallel time estimation. In proceedings of the 8th International Conference on Cognitive Modeling. Ann Arbor, Michigan, USA.

Dzaack, J., Troesterer, S., Pape, N., & Urbas, L. (2006). A computational model of retrospective time estimation. In Proceedings of the Seventh International Conference on Cognitive Modeling (pp. 100-104). Trieste, Italy.

Taatgen, N., Anderson, J. R., Dickison, D., & van Rijn, H. (2005) Time Interval Estimation: Internal Clock or Attentional Mechanism? In B. Bara, L. Barsalou & M. Bucciarelli (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

Pape, N., Dzaack, J., Leuchter,S. & Urbas, L. (2005). How to integrate time-duration estimation in ACT-R/PM. In Proceedings of the 12th Annual ACT-R Workshop, Trieste, Italy.

Cassenti, D. N. & Reifers, A. L. (2005) Counting ACT-R to Represent Time. In proceedings of Human Factors and Ergonomics Society Conference.

Taatgen, N., van Rijn, H., & Anderson, J. R. (2004). Time perception: Beyond simple interval estimation. In Proceedings of the sixth International Conference on Cognitive Modeling (pp. 296-301). Pittsburgh, PA: Carnegie Mellon University/University of Pittsburgh.

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Department of Psychology, Carnegie Mellon University