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. |
From inside the book
Results 1-5 of 88
Page 1
But, the data that actually reach us from the world are incomplete, fragmented, probabilistic, and concrete. So, the baffling thing for psychologists has been how we could get from that kind of data to those kinds of representations.
But, the data that actually reach us from the world are incomplete, fragmented, probabilistic, and concrete. So, the baffling thing for psychologists has been how we could get from that kind of data to those kinds of representations.
Page 4
... (like “having eyes” or “not having eyes”) or take a range of values (like color). Similarly, the direct causal relations can have many forms; they can be deterministic or probabilistic, generative or inhibitory, linear or nonlinear ...
... (like “having eyes” or “not having eyes”) or take a range of values (like color). Similarly, the direct causal relations can have many forms; they can be deterministic or probabilistic, generative or inhibitory, linear or nonlinear ...
Page 12
The Power Theory of Probabilistic Contrast Patricia Cheng (1997) proposes an account of human causal learning that resolves some of the difficulties with the R-W account. Cheng proposes that people innately treat covariation as an index ...
The Power Theory of Probabilistic Contrast Patricia Cheng (1997) proposes an account of human causal learning that resolves some of the difficulties with the R-W account. Cheng proposes that people innately treat covariation as an index ...
Page 14
Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufmann. Pearl, J. (2000). Causality. New York: Oxford University Press. Piaget, J. (1929). The child's conception of the world. New York: Harcourt, Brace.
Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufmann. Pearl, J. (2000). Causality. New York: Oxford University Press. Piaget, J. (1929). The child's conception of the world. New York: Harcourt, Brace.
Page 19
Probabilistic theories attempt to do this in terms of inequalities among conditional probabilities: A cause must raise or at least change the probability of its effect, conditional on some suitable set of background conditions.
Probabilistic theories attempt to do this in terms of inequalities among conditional probabilities: A cause must raise or at least change the probability of its effect, conditional on some suitable set of background conditions.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
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 |
Common terms and phrases
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 |