Causal Learning: Psychology, Philosophy, and ComputationUnderstanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. |
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Page 1
What we empirical psychologists see is that learners infer abstract, structured hierarchical representations of the world. And those representations are true—they really do get us to a better picture of the world.
What we empirical psychologists see is that learners infer abstract, structured hierarchical representations of the world. And those representations are true—they really do get us to a better picture of the world.
Page 2
Insofar as our representations are accurate, it is because of a long phylogenetic evolutionary history, not a brief ontogenetic inferential one. And, there is no real learning involved in development but only triggering or enrichment.
Insofar as our representations are accurate, it is because of a long phylogenetic evolutionary history, not a brief ontogenetic inferential one. And, there is no real learning involved in development but only triggering or enrichment.
Page 4
In causal Bayes nets, causal hypotheses are represented by directed acyclic graphs like that of Figure I-1. The graphs consist of variables, representing types of events or states of the world and directed edges (arrows) representing ...
In causal Bayes nets, causal hypotheses are represented by directed acyclic graphs like that of Figure I-1. The graphs consist of variables, representing types of events or states of the world and directed edges (arrows) representing ...
Page 5
Bayes Nets and Interventions Why think of these graphs as representations of causal relations among variables, rather than simply thinking of them as a convenient way to represent the probabilities of variables?
Bayes Nets and Interventions Why think of these graphs as representations of causal relations among variables, rather than simply thinking of them as a convenient way to represent the probabilities of variables?
Page 6
If I simply decide to stop drinking wine, then my intervention alone will determine the value of wine drinking; partying will no longer have any effect. This can be represented by replacing the original graph with an altered graph in ...
If I simply decide to stop drinking wine, then my intervention alone will determine the value of wine drinking; partying will no longer have any effect. This can be represented by replacing the original graph with an altered graph in ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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actions adults algorithms Bayesian inference Bayesian networks behavior beliefs birth control pills blicket detector Cambridge causal Bayes nets causal chain causal inference causal knowledge causal learning causal Markov condition causal model causal networks causal power causal reasoning causal relations causal relationships causal strength causal structure causal system chapter Cognitive Science common cause computational condition conditional independence conditional probabilities correlation counterfactuals covariation cues deterministic Development Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Hagmayer human independent infants intervention interventionist intuitive theories Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants people’s Piston predictions prior probabilistic probabilistic graphical models probability distribution psychological question Reichenbach represent representation Schulz Sloman Sobel specific statistical stickball Tenenbaum thrombosis tion trials underlying understanding unobserved cause variables Waldmann Wellman Woodward
Bibliographic information
| Title | Causal Learning: Psychology, Philosophy, and Computation Oxford Series in Cognitive Development |
| Editors | Alison Gopnik, Laura Schulz |
| Publisher | Oxford University Press, 2007 |
| ISBN | 019803928X, 9780198039280 |
| Length | 384 pages |
| Subjects | › Psychology / Cognitive Psychology & Cognition |
| Export Citation | BiBTeX EndNote RefMan |