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Two proposals for causal grammar
- In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation
, 2007
"... In the previous chapter (Tenenbaum, Griffiths, & Niyogi, this volume), we introduced a framework for thinking about the structure, function, and acquisition of intuitive theories inspired by an analogy to the research program of generative grammar in linguistics. We argued that a principal function ..."
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Cited by 8 (6 self)
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In the previous chapter (Tenenbaum, Griffiths, & Niyogi, this volume), we introduced a framework for thinking about the structure, function, and acquisition of intuitive theories inspired by an analogy to the research program of generative grammar in linguistics. We argued that a principal function for intuitive theories, just as for grammars for natural
Modeling cross-domain causal learning in preschoolers as Bayesian inference
- In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 89–94). Mahwah, NJ: Lawrence Erlbaum Associates
, 2006
"... This study investigates the interaction between preschoolers ’ initial theories and their ability to learn causal relations from patterns of data. Children observed ambiguous evidence in which sets of two candidate causes co-occurred with an effect (e.g. A&B � E, A&C � E, A&D � E, etc). In one condi ..."
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Cited by 3 (1 self)
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This study investigates the interaction between preschoolers ’ initial theories and their ability to learn causal relations from patterns of data. Children observed ambiguous evidence in which sets of two candidate causes co-occurred with an effect (e.g. A&B � E, A&C � E, A&D � E, etc). In one condition, all candidate causes were from the appropriate domain (a biological cause for a biological effect); in another condition, the recurring candidate cause, A, crossed domains (a psychological cause for a biological effect). When all causes were domainappropriate, children were able to use the data to identify A as a cause. When the recurring cause crossed domains, children were less likely to endorse A. However, preschoolers were significantly more willing to accept cross-domain causes after seeing the evidence than at baseline. A Bayesian model is proposed to explain this interaction. Very young children have remarkably sophisticated causal knowledge about the world. Children reason about the causes of mental states (e.g., Meltzoff, 1995), physical systems (e.g., Bullock, Gelman, & Baillargeon, 1982; Shultz, 1982), and biological events (e.g., Gelman & Wellman, 1991; Kalish, 1996). Preschoolers can even make predictions about hidden variables and explain events in terms of unobservable causes (Schulz & Sommerville, in press). Many researchers have suggested that children’s causal knowledge can be characterized as intuitive theories: abstract, coherent, defeasible representations of causal

