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 8
... actions of others. Andrew Meltzoff will show you something like the reverse: how babies take information they only observe and turn it into actions of their own. Sprogs do all sorts of other things: make good interventions, discriminate ...
... actions of others. Andrew Meltzoff will show you something like the reverse: how babies take information they only observe and turn it into actions of their own. Sprogs do all sorts of other things: make good interventions, discriminate ...
Page 9
... action as goal directed and self-initiated (Meltzoff, 1995; A. L. Woodward, 1998; A. L. Woodward, Phillips, & Spelke, 1993). Thus, for instance, babies expect physical objects to move through contact (Leslie & Keeble, 1987; Oakes ...
... action as goal directed and self-initiated (Meltzoff, 1995; A. L. Woodward, 1998; A. L. Woodward, Phillips, & Spelke, 1993). Thus, for instance, babies expect physical objects to move through contact (Leslie & Keeble, 1987; Oakes ...
Page 10
... Actions with positive consequences are likely to be repeated and actions with negative consequences avoided (1911/2000). A large body of research on learning subsequently elaborated the ways in which behavior could be shaped by ...
... Actions with positive consequences are likely to be repeated and actions with negative consequences avoided (1911/2000). A large body of research on learning subsequently elaborated the ways in which behavior could be shaped by ...
Page 21
... action at any point may thus qualify as an intervention if it has the right causal characteristics. Conversely, a manipulation carried out by a human being will fail to qualify as an intervention if it lacks the right causal ...
... action at any point may thus qualify as an intervention if it has the right causal characteristics. Conversely, a manipulation carried out by a human being will fail to qualify as an intervention if it lacks the right causal ...
Page 22
... Actions section) that, as a matter of contingent, empirical fact, many voluntary human actions as well as many behaviors carried out by animals do satisfy the conditions for an intervention. Moreover, I also think that it is a plausible ...
... Actions section) that, as a matter of contingent, empirical fact, many voluntary human actions as well as many behaviors carried out by animals do satisfy the conditions for an intervention. Moreover, I also think that it is a plausible ...
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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 |
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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