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Laura Schulz [10]Laura E. Schulz [6]
  1. Hyowon Gweon, Hannah Pelton, Jaclyn A. Konopka & Laura E. Schulz (2014). Sins of Omission: Children Selectively Explore When Teachers Are Under-Informative. Cognition 132 (3):335-341.
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  2. Laura Schulz (2012). The Origins of Inquiry: Inductive Inference and Exploration in Early Childhood. Trends in Cognitive Sciences 16 (7):382-389.
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  3. Elizabeth Bonawitz, Patrick Shafto, Hyowon Gweon, Noah D. Goodman, Elizabeth Spelke & Laura Schulz (2011). The Double-Edged Sword of Pedagogy: Instruction Limits Spontaneous Exploration and Discovery. Cognition 120 (3):322-330.
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  4. Claire Cook, Noah D. Goodman & Laura E. Schulz (2011). Where Science Starts: Spontaneous Experiments in Preschoolers' Exploratory Play. Cognition 120 (3):341-349.
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  5. Elizabeth Baraff Bonawitz, Darlene Ferranti, Rebecca Saxe, Alison Gopnik, Andrew N. Meltzoff, James Woodward & Laura E. Schulz (2010). Just Do It? Investigating the Gap Between Prediction and Action in Toddlers' Causal Inferences. Cognition 115 (1):104-117.
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  6. Hyowon Gweon & Laura E. Schulz (2010). Is It Me or the World? 16-Month-Olds Distinguish Competing Hypotheses About the Cause of Failed Interventions. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. 2846--2851.
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  7. Tamar Kushnir, Alison Gopnik, Chris Lucas & Laura Schulz (2010). Inferring Hidden Causal Structure. Cognitive Science 34 (1):148-160.
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  8. Laura E. Schulz, Noah D. Goodman, Joshua B. Tenenbaum & Adrianna C. Jenkins (2008). Going Beyond the Evidence: Abstract Laws and Preschoolers' Responses to Anomalous Data. Cognition 109 (2):211-223.
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  9. Andrew Shtulman & Laura Schulz (2008). The Relation Between Essentialist Beliefs and Evolutionary Reasoning. Cognitive Science 32 (6):1049-1062.
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  10. Alison Gopnik & Laura Schulz (eds.) (2007). Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
    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 (...)
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  11. Thomas Richardson, Laura Schulz & Alison Gopnik (2007). Data-Mining Probabilists or Experimental Determinists. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. 208--230.
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  12. Laura Schulz, Tamar Kushnir & Alison Gopnik (2007). Learning From Doing: Intervention and Causal Inference. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. 67--85.
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  13. Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...)
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  14. Alison Gopnik & Laura Schulz (2004). Questions for Future Research. Trends in Cognitive Sciences 8 (8):371-377.
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  15. Clark Glymour, Alison Gopnik, David M. Sobel & Laura E. Schulz, Causal Learning Mechanisms in Very Young Children: Two-, Three-, and Four-Year-Olds Infer Causal Relations From Patterns of Variation and Covariation.
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