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MacLellan, Christopher J.; Harpstead, Erik; Patel, Rony; Koedinger, Kenneth R. – International Educational Data Mining Society, 2016
While Educational Data Mining research has traditionally emphasized the practical aspects of learner modeling, such as predictive modeling, estimating students knowledge, and informing adaptive instruction, in the current study, we argue that Educational Data Mining can also be used to test and improve our fundamental theories of human learning.…
Descriptors: Educational Research, Data Collection, Learning Theories, Recall (Psychology)
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Griffiths, Thomas L.; Lewandowsky, Stephan; Kalish, Michael L. – Cognitive Science, 2013
Information changes as it is passed from person to person, with this process of cultural transmission allowing the minds of individuals to shape the information that they transmit. We present mathematical models of cultural transmission which predict that the amount of information passed from person to person should affect the rate at which that…
Descriptors: Culture, Information Dissemination, Mathematical Models, Prediction
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Lee, Michael D.; Pooley, James P. – Psychological Review, 2013
The scale-invariant memory, perception, and learning (SIMPLE) model developed by Brown, Neath, and Chater (2007) formalizes the theoretical idea that scale invariance is an important organizing principle across numerous cognitive domains and has made an influential contribution to the literature dealing with modeling human memory. In the context…
Descriptors: Recall (Psychology), Memory, Models, Equations (Mathematics)
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Xu, Jing; Griffiths, Thomas L. – Cognitive Psychology, 2010
Many human interactions involve pieces of information being passed from one person to another, raising the question of how this process of information transmission is affected by the cognitive capacities of the agents involved. Bartlett (1932) explored the influence of memory biases on the "serial reproduction" of information, in which one…
Descriptors: Stimuli, Memory, Bayesian Statistics, Prediction
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Craig, Stewart; Lewandowsky, Stephan; Little, Daniel R. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2011
The assumption in some current theories of probabilistic categorization is that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report 2 probabilistic-categorization experiments in which we investigated error…
Descriptors: Evidence, Feedback (Response), Associative Learning, Classification
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Hilbig, Benjamin E.; Erdfelder, Edgar; Pohl, Rudiger F. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2011
A new process model of the interplay between memory and judgment processes was recently suggested, assuming that retrieval fluency--that is, the speed with which objects are recognized--will determine inferences concerning such objects in a single-cue fashion. This aspect of the fluency heuristic, an extension of the recognition heuristic, has…
Descriptors: Stimuli, Heuristics, Memory, Goodness of Fit
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Bowers, Jeffrey S.; Davis, Colin J. – Psychological Bulletin, 2012
According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak.…
Descriptors: Bayesian Statistics, Psychology, Brain, Theories
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Shiffrin, Richard M.; Lee, Michael D.; Kim, Woojae; Wagenmakers, Eric-Jan – Cognitive Science, 2008
This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues…
Descriptors: Bayesian Statistics, Generalization, Sciences, Models