13 found
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  1.  19
    Memory for goals: an activation‐based model.Erik M. Altmann & J. Gregory Trafton - 2002 - Cognitive Science 26 (1):39-83.
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  2.  13
    “What if…”: The Use of Conceptual Simulations in Scientific Reasoning.Susan Bell Trickett & J. Gregory Trafton - 2007 - Cognitive Science 31 (5):843-875.
  3.  68
    Connecting internal and external representations: Spatial transformations of scientific visualizations. [REVIEW]J. Gregory Trafton, Susan B. Trickett & Farilee E. Mintz - 2005 - Foundations of Science 10 (1):89-106.
    Many scientific discoveries have depended on external diagrams or visualizations. Many scientists also report to use an internal mental representation or mental imagery to help them solve problems and reason. How do scientists connect these internal and external representations? We examined working scientists as they worked on external scientific visualizations. We coded the number and type of spatial transformations (mental operations that scientists used on internal or external representations or images) and found that there were a very large number of (...)
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  4.  17
    Episodes, events, and models.Sangeet S. Khemlani, Anthony M. Harrison & J. Gregory Trafton - 2015 - Frontiers in Human Neuroscience 9.
  5.  16
    An Account of Interference in Associative Memory: Learning the Fan Effect.Robert Thomson, Anthony M. Harrison, J. Gregory Trafton & Laura M. Hiatt - 2017 - Topics in Cognitive Science 9 (1):69-82.
    Associative learning is an essential feature of human cognition, accounting for the influence of priming and interference effects on memory recall. Here, we extend our account of associative learning that learns asymmetric item-to-item associations over time via experience by including link maturation to balance associations between longer-term stability while still accounting for short-term variability. This account, combined with an existing account of activation strengthening and decay, predicts both human response times and error rates for the fan effect for both target (...)
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  6.  54
    A generalized model for predicting postcompletion errors.Raj M. Ratwani & J. Gregory Trafton - 2010 - Topics in Cognitive Science 2 (1):154-167.
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  7. The psychology of uncertainty in scientific data analysis.Christian D. Schunn & J. Gregory Trafton - 2013 - In Gregory J. Feist & Michael E. Gorman (eds.), Handbook of the psychology of science. Springer Pub. Company, LLC.
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  8. The role of spatial information in referential communication: speaker and addressee preferences for disambiguating objects.Sarah Kriz, J. Gregory Trafton & J. Malcolm McCurry - 2007 - In McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society.
     
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  9.  15
    Examining the Role of Task Requirements in the Magnitude of the Vigilance Decrement.Daniel Gartenberg, Glenn Gunzelmann, Shiva Hassanzadeh-Behbaha & J. Gregory Trafton - 2018 - Frontiers in Psychology 9.
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  10. Brigitte cambon de lavalette, Charles tijus.Christine Leproux, Olivier Bauer, J. Gregory Trafton, Susan B. Trickett, Lorenzo Magnani & Matteo Piazza - 2005 - Foundations of Science 10:457-458.
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  11.  15
    “Memories for goals: an activation‐based model” [Cognitive Science 26 (2002) 39–83].Erik M. Altmanna & J. Gregory Trafton - 2002 - Cognitive Science 26 (2):233-233.
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  12.  2
    Validating and Refining Cognitive Process Models Using Probabilistic Graphical Models.Laura M. Hiatt, Connor Brooks & J. Gregory Trafton - 2022 - Topics in Cognitive Science 14 (4):873-888.
    We describe a new approach for developing and validating cognitive process models. In our methodology, graphical models (specifically, hidden Markov models) are developed both from human empirical data on a task and synthetic data traces generated by a cognitive process model of human behavior on the task. Differences between the two graphical models can then be used to drive model refinement. We show that iteratively using this methodology can unveil substantive and nuanced imperfections of cognitive process models that can then (...)
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  13.  26
    Puzzles and peculiarities: How scientists attend to and process anomalies during data analysis.Susan B. Trickett, Christian D. Schunn & J. Gregory Trafton - 2005 - In M. Gorman, R. Tweney, D. Gooding & A. Kincannon (eds.), Scientific and Technological Thinking. Erlbaum. pp. 97--118.