Causal Learning: Psychology, Philosophy, and ComputationAlison Gopnik, Laura Schulz Understanding 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 9
... behavior, physical events, and biological transformations are an outgrowth of domain-specific modules. In particular, modular, domain-specific accounts of causal reasoning do not seem to explain how we identify particular causal rela ...
... behavior, physical events, and biological transformations are an outgrowth of domain-specific modules. In particular, modular, domain-specific accounts of causal reasoning do not seem to explain how we identify particular causal rela ...
Page 10
... behavior could be shaped by reinforcing or punishing outcomes . Operant learning has been demonstrated in nonhuman animals ranging from pigeons to primates ; unsurprisingly , it has been demonstrated in human babies as well . Thus ...
... behavior could be shaped by reinforcing or punishing outcomes . Operant learning has been demonstrated in nonhuman animals ranging from pigeons to primates ; unsurprisingly , it has been demonstrated in human babies as well . Thus ...
Page 11
... behavior in the presence of the cue long after the association has disappeared). In the human case, Patricia Cheng demonstrated, for instance, that the R-W approach fails to account for boundary conditions on causal inference (1997) ...
... behavior in the presence of the cue long after the association has disappeared). In the human case, Patricia Cheng demonstrated, for instance, that the R-W approach fails to account for boundary conditions on causal inference (1997) ...
Page 22
... behaviors carried out by animals do satisfy the conditions for an interven- tion. Moreover, I also think that ... behavior, and judgments are guided by principles like TC. The connection between interventions and human (and animal) ...
... behaviors carried out by animals do satisfy the conditions for an interven- tion. Moreover, I also think that ... behavior, and judgments are guided by principles like TC. The connection between interventions and human (and animal) ...
Page 26
... behavior is often plausibly regarded as at least a prima facie problem for a normative theory—a problem that the normative theory needs to address rather than ignore. In the spirit of these remarks, I explore, in the remain- der of this ...
... behavior is often plausibly regarded as at least a prima facie problem for a normative theory—a problem that the normative theory needs to address rather than ignore. In the spirit of these remarks, I explore, in the remain- der of this ...
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 |
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actions adults algorithms Bayesian inference Bayesian networks behavior beliefs birth control pills blicket detector Cambridge causal Bayes nets 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 Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Griffiths Hagmayer human independent infants intervention interventionist intuitive theories Journal of Experimental Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants 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