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2012
AbstractThis chapter is an introduction to the psychology of causal inference using a computational perspective, with the focus on causal discovery. It explains the nature of the problem of causal discovery and illustrates the goal of the process with everyday and hypothetical examples.
Patricia W. Cheng, Marc J. Buehner
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AbstractThis chapter is an introduction to the psychology of causal inference using a computational perspective, with the focus on causal discovery. It explains the nature of the problem of causal discovery and illustrates the goal of the process with everyday and hypothetical examples.
Patricia W. Cheng, Marc J. Buehner
+4 more sources
WIREs Cognitive Science, 2014
How do children learn the causal structure of the environment? We first summarize a set of theories from the adult literature on causal learning, including associative models, parameter estimation theories, and causal structure learning accounts, as applicable to developmental science.
David M, Sobel, Cristine H, Legare
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How do children learn the causal structure of the environment? We first summarize a set of theories from the adult literature on causal learning, including associative models, parameter estimation theories, and causal structure learning accounts, as applicable to developmental science.
David M, Sobel, Cristine H, Legare
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2007
Abstract This book outlines the recent revolutionary work in cognitive science formulating a “probabilistic model” theory of learning and development. It provides an accessible and clear introduction to the probabilistic modeling in psychology, including causal model, Bayes net, and Bayesian approaches. It also outlines new cognitive and
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Abstract This book outlines the recent revolutionary work in cognitive science formulating a “probabilistic model” theory of learning and development. It provides an accessible and clear introduction to the probabilistic modeling in psychology, including causal model, Bayes net, and Bayesian approaches. It also outlines new cognitive and
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Multitask Causal Contrastive Learning
IEEE Transactions on Neural Networks and Learning SystemsMultitask learning (MTL) aims to improve the performance of multiple tasks by sharing knowledge among multiple different tasks, which has attracted increasing interest and shown success in various fields. However, MTL often suffers from negative transfer since the model may utilize useless features and face interference among tasks' optimization ...
Chaoyang Li +4 more
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2005
AbstractThis chapter offers a selection of theories of causal learning. Some of the theories come out of psychology, while others come out of rational analyses of causal learning. All tend to focus on how people use correlations — information about which events go together — to figure out what is causing what.
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AbstractThis chapter offers a selection of theories of causal learning. Some of the theories come out of psychology, while others come out of rational analyses of causal learning. All tend to focus on how people use correlations — information about which events go together — to figure out what is causing what.
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2003
Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories ...
Danks, David +2 more
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Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories ...
Danks, David +2 more
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1993
The essence of causality can be identified with a graphical structure representing relevance relationships between variables. In this paper the problem of infering causal relations from patterns of dependence is considered. We suppose that there exists a causal model, which is representable by a polytree structure and present an approach to the ...
Juan F. Huete, Luis M. de Campos
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The essence of causality can be identified with a graphical structure representing relevance relationships between variables. In this paper the problem of infering causal relations from patterns of dependence is considered. We suppose that there exists a causal model, which is representable by a polytree structure and present an approach to the ...
Juan F. Huete, Luis M. de Campos
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2007
Abstract This chapter shows that the perception of others' actions and production of self-action are mapped onto commensurate representations starting from birth. This allows infants not only to learn interventions through their own manipulations but also to multiply greatly their learning opportunities by observing the manipulations of ...
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Abstract This chapter shows that the perception of others' actions and production of self-action are mapped onto commensurate representations starting from birth. This allows infants not only to learn interventions through their own manipulations but also to multiply greatly their learning opportunities by observing the manipulations of ...
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Science, 2011
Children are amazing learners, and how this happens is a question that has nourished several disciplines. Tenenbaum et al. (p. [1279][1]) review recent developments in the frameworks used for studying how humans learn concepts and causal relations.
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Children are amazing learners, and how this happens is a question that has nourished several disciplines. Tenenbaum et al. (p. [1279][1]) review recent developments in the frameworks used for studying how humans learn concepts and causal relations.
openaire +1 more source