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  1. Mindreading: An Integrated Account of Pretence, Self-Awareness and Understanding Other Minds.J. Heal - 2005 - Mind 114 (453):181-184.
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  • Theory of Probability.Harold Jeffreys - 1940 - Philosophy of Science 7 (2):263-264.
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  • The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference.Naomi H. Feldman, Thomas L. Griffiths & James L. Morgan - 2009 - Psychological Review 116 (4):752-782.
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  • Bayesian generic priors for causal learning.Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng & Keith J. Holyoak - 2008 - Psychological Review 115 (4):955-984.
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  • From covariation to causation: A causal power theory.Patricia W. Cheng - 1997 - Psychological Review 104 (2):367-405.
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  • A Computational Model of Early Argument Structure Acquisition.Afra Alishahi & Suzanne Stevenson - 2008 - Cognitive Science 32 (5):789-834.
    How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of (...)
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  • Categorization as nonparametric Bayesian density estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Navarro & J. Daniel - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
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  • Letting structure emerge: connectionist and dynamical systems approaches to cognition.James L. McClelland, Matthew M. Botvinick, David C. Noelle, David C. Plaut, Timothy T. Rogers, Mark S. Seidenberg & Linda B. Smith - 2010 - Trends in Cognitive Sciences 14 (8):348-356.
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  • Categorization as nonparametric Bayesian density estimation.Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Daniel J. Navarro - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
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  • Learning phonetic categories by learning a lexicon.Naomi H. Feldman, Thomas L. Griffiths & James L. Morgan - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  • Iterated learning in populations of Bayesian agents.Kenny Smith - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 697--702.
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  • A Bayesian framework for word segmentation: Exploring the effects of context.Sharon Goldwater, Thomas L. Griffiths & Mark Johnson - 2009 - Cognition 112 (1):21-54.
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  • An abstract to concrete shift in the development of biological thought: the insides story.Daniel J. Simons & Frank C. Keil - 1995 - Cognition 56 (2):129-163.
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  • Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers.D. Sobel - 2004 - Cognitive Science 28 (3):303-333.
    Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector,” a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine's activation that (...)
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  • Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Cognition.Linda B. Smith James L. McClelland, Matthew M. Botvinick, David C. Noelle, David C. Plaut, Timothy T. Rogers, Mark S. Seidenberg - 2010 - Trends in Cognitive Sciences 14 (8):348.
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  • Constraints Children Place on Word Meanings.Ellen M. Markman - 1990 - Cognitive Science 14 (1):57-77.
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  • Language Evolution by Iterated Learning With Bayesian Agents.Thomas L. Griffiths & Michael L. Kalish - 2007 - Cognitive Science 31 (3):441-480.
    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 a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior (...)
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  • The Child is a Theoretician, Not an Inductivist.Annette Karmiloff-Smith - 1988 - Mind and Language 3 (3):183-196.
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  • Categorization and Naming in Children: Problems of Induction.Ellen M. Markman - 1989 - MIT Press.
    In this landmark work on early conceptual and lexical development, Ellen Markman explores the fascinating problem of how young children succeed at the task of ...
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  • Structural ontology.Fred Sommers - 1971 - Philosophia 1 (1-2):21-42.
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