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 6-10 of 75
Page 22
... distribution of Y) when all other variables in V besides X and Y are held fixed at some value by other independent interventions. BTP FIGURE 1-1 In the example under discussion, B counts as a direct 22 CAUSATION AND INTERVENTION.
... distribution of Y) when all other variables in V besides X and Y are held fixed at some value by other independent interventions. BTP FIGURE 1-1 In the example under discussion, B counts as a direct 22 CAUSATION AND INTERVENTION.
Page 23
... independent of this, intervene to change the value of B, then the value of T will change. The notion of X as a direct cause of Y is thus characterized in terms of the response of Y to a combination of interventions, including both ...
... independent of this, intervene to change the value of B, then the value of T will change. The notion of X as a direct cause of Y is thus characterized in terms of the response of Y to a combination of interventions, including both ...
Page 25
... independently on the eight ball (e.g., gluing it to the table), and so on. On this construal, detailed information about the operation of mechanisms is not, as is often supposed, something different in kind from information about ...
... independently on the eight ball (e.g., gluing it to the table), and so on. On this construal, detailed information about the operation of mechanisms is not, as is often supposed, something different in kind from information about ...
Page 26
... human causal judgment are (independently of temporal relations) highly sensitive to the contingency p between action A and outcome O, that is, to P(O/A) P(O/A). Although there are important 26 CAUSATION AND INTERVENTION.
... human causal judgment are (independently of temporal relations) highly sensitive to the contingency p between action A and outcome O, that is, to P(O/A) P(O/A). Although there are important 26 CAUSATION AND INTERVENTION.
Page 29
... some patients and withhold it from others, by the health of the patients; his decisions are voluntary and yet correlated with an independent cause of recovery in INTERVENTIONIST THEORIES OF CAUSATION IN PSYCHOLOGICAL PERSPECTIVE 29.
... some patients and withhold it from others, by the health of the patients; his decisions are voluntary and yet correlated with an independent cause of recovery in INTERVENTIONIST THEORIES OF CAUSATION IN PSYCHOLOGICAL PERSPECTIVE 29.
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 |
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