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. |
From inside the book
Results 1-5 of 54
Page 19
... manipulations of) on C. The interventionist theory does not require (although it permits) thinking of counterfactuals in terms of possible worlds and, as noted below, the specification of what sorts of changes count as interventions ...
... manipulations of) on C. The interventionist theory does not require (although it permits) thinking of counterfactuals in terms of possible worlds and, as noted below, the specification of what sorts of changes count as interventions ...
Page 20
... manipulating their effects: If it is possible to manipulate a cause in the right way, then there would be an associated change in its effect. Conversely, if under some appropriately characterized manipulation of one factor, there is an ...
... manipulating their effects: If it is possible to manipulate a cause in the right way, then there would be an associated change in its effect. Conversely, if under some appropriately characterized manipulation of one factor, there is an ...
Page 21
... manipulate the value of B by manipulating the value of A, then the value of S will change even though, in contradiction to (SC), B does not cause S. Intuitively, an experiment in which B is manipulated in this way is a badly designed ...
... manipulate the value of B by manipulating the value of A, then the value of S will change even though, in contradiction to (SC), B does not cause S. Intuitively, an experiment in which B is manipulated in this way is a badly designed ...
Page 22
... manipulated. Nonetheless, I think that it is plausible (see the Interventions and Voluntary Actions section) that ... manipulations of smoking were to be performed. Finally, a brief remark about an issue that will probably be of much ...
... manipulated. Nonetheless, I think that it is plausible (see the Interventions and Voluntary Actions section) that ... manipulations of smoking were to be performed. Finally, a brief remark about an issue that will probably be of much ...
Page 24
... manipulating whether lactose is present changes whether the enzyme is synthesized—but no spatiotemporally continuous process or transfer of energy, momentum, or force between lactose and the enzyme.3 Interventi-onist accounts along the ...
... manipulating whether lactose is present changes whether the enzyme is synthesized—but no spatiotemporally continuous process or transfer of energy, momentum, or force between lactose and the enzyme.3 Interventi-onist accounts along the ...
Other editions - View all
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