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Rafferty, Anna N.; Brunskill, Emma; Griffiths, Thomas L.; Shafto, Patrick – Cognitive Science, 2016
Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model…
Descriptors: Markov Processes, Educational Planning, Decision Making, Models
Rafferty, Anna N.; LaMar, Michelle M.; Griffiths, Thomas L. – Cognitive Science, 2015
Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we…
Descriptors: Inferences, Knowledge Level, Educational Games, Computer Simulation
Martin, Jay B.; Griffiths, Thomas L.; Sanborn, Adam N. – Cognitive Science, 2012
Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as…
Descriptors: Markov Processes, Monte Carlo Methods, Correlation, Efficiency
Sanborn, Adam N.; Griffiths, Thomas L.; Shiffrin, Richard M. – Cognitive Psychology, 2010
A key challenge for cognitive psychology is the investigation of mental representations, such as object categories, subjective probabilities, choice utilities, and memory traces. In many cases, these representations can be expressed as a non-negative function defined over a set of objects. We present a behavioral method for estimating these…
Descriptors: Markov Processes, Multidimensional Scaling, Cognitive Psychology, Probability
Griffiths, Thomas L.; Kalish, Michael L. – Cognitive Science, 2007
Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…
Descriptors: Probability, Diachronic Linguistics, Statistical Inference, Language Universals