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Denison, Stephanie; Reed, Christie; Xu, Fei – Developmental Psychology, 2013
How do people make rich inferences from such sparse data? Recent research has explored this inferential ability by investigating probabilistic reasoning in infancy. For example, 8- and 11-month-old infants can make inferences from samples to populations and vice versa (Denison & Xu, 2010a; Xu & Denison, 2009; Xu & Garcia, 2008a). The…
Descriptors: Probability, Infants, Inferences, Young Children
Piantadosi, Steven T.; Kidd, Celeste; Aslin, Richard – Developmental Science, 2014
Studies of infant looking times over the past 50 years have provided profound insights about cognitive development, but their dependent measures and analytic techniques are quite limited. In the context of infants' attention to discrete sequential events, we show how a Bayesian data analysis approach can be combined with a rational cognitive…
Descriptors: Infants, Eye Movements, Infant Behavior, Cognitive Development
Scheibehenne, Benjamin; Rieskamp, Jorg; Wagenmakers, Eric-Jan – Psychological Review, 2013
Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox…
Descriptors: Cognitive Processes, Behavior, Models, Bayesian Statistics
Gopnik, Alison; Wellman, Henry M. – Psychological Bulletin, 2012
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…
Descriptors: Causal Models, Theory of Mind, Probability, Cognitive Development
Perfors, Amy; Tenenbaum, Joshua B.; Griffiths, Thomas L.; Xu, Fei – Cognition, 2011
We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the "what", the "how", and the "why" of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for…
Descriptors: Bayesian Statistics, Cognitive Psychology, Inferences, Cognitive Development
Levy, Roy; Mislevy, Robert J. – US Department of Education, 2004
The challenges of modeling students' performance in simulation-based assessments include accounting for multiple aspects of knowledge and skill that arise in different situations and the conditional dependencies among multiple aspects of performance in a complex assessment. This paper describes a Bayesian approach to modeling and estimating…
Descriptors: Probability, Markov Processes, Monte Carlo Methods, Bayesian Statistics

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