Literature DB >> 21269608

A tutorial introduction to Bayesian models of cognitive development.

Amy Perfors1, Joshua B Tenenbaum, Thomas L Griffiths, Fei Xu.   

Abstract

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 developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.
Copyright © 2010 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21269608     DOI: 10.1016/j.cognition.2010.11.015

Source DB:  PubMed          Journal:  Cognition        ISSN: 0010-0277


  24 in total

1.  Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood.

Authors:  Alison Gopnik; Shaun O'Grady; Christopher G Lucas; Thomas L Griffiths; Adrienne Wente; Sophie Bridgers; Rosie Aboody; Hoki Fung; Ronald E Dahl
Journal:  Proc Natl Acad Sci U S A       Date:  2017-07-24       Impact factor: 11.205

2.  Rich analysis and rational models: inferring individual behavior from infant looking data.

Authors:  Steven T Piantadosi; Celeste Kidd; Richard Aslin
Journal:  Dev Sci       Date:  2014-02-07

3.  The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model.

Authors:  J Kiley Hamlin; Tomer Ullman; Josh Tenenbaum; Noah Goodman; Chris Baker
Journal:  Dev Sci       Date:  2013-03

4.  Bayesian data analysis for newcomers.

Authors:  John K Kruschke; Torrin M Liddell
Journal:  Psychon Bull Rev       Date:  2018-02

Review 5.  Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

Authors:  Jackie Chappell; Nick Hawes
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2012-10-05       Impact factor: 6.237

6.  Category effects on stimulus estimation: Shifting and skewed frequency distributions-A reexamination.

Authors:  Sean Duffy; John Smith
Journal:  Psychon Bull Rev       Date:  2018-10

7.  Explanation-based learning in infancy.

Authors:  Renée Baillargeon; Gerald F DeJong
Journal:  Psychon Bull Rev       Date:  2017-10

8.  Separate streams or probabilistic inference? What the N400 can tell us about the comprehension of events.

Authors:  Gina R Kuperberg
Journal:  Lang Cogn Neurosci       Date:  2016-01-20       Impact factor: 2.331

Review 9.  Bayesian statistics: relevant for the brain?

Authors:  Konrad Paul Kording
Journal:  Curr Opin Neurobiol       Date:  2014-01-24       Impact factor: 6.627

10.  What do we mean by prediction in language comprehension?

Authors:  Gina R Kuperberg; T Florian Jaeger
Journal:  Lang Cogn Neurosci       Date:  2015-11-13       Impact factor: 2.331

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