Alison Gopnik Theoretical and Review Papers
Alison Gopnik Theoretical and Review Papers
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If interested in a general and readable review and explanation of work on probabilistic models of cognitive development, start with this Psychological Bulletin article:
A. Gopnik & H. M. Wellman (2012). Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory. Psychological Bulletin, 138(6):1085-1108, DOI: 10.1037/a0028044.
Other theoretical and review papers include:
Childhood as a solution to explore-exploit tensions
A. Gopnik (2020). Childhood as a solution to explore-exploit tensions. Philosophical Transactions of the Royal Society (B), DOI: 10.1098/rstb.2019.0502.
Life history, love and learning
Alison Gopnik (2019). Life history, love and learning. Nature Human Behaviour, News and Views, DOI: 10.1038/s41562-019-0673-8.
Alison Gopnik (2017). Making A.I. More Human. Scientific American, 60-65. June.
Changes in cognitive flexibility and hypothesis search across human life history
A. Gopnik, O’Grady, S., Lucas, C. G., Griffiths, T. L., Wente, A., Bridgers, S., Aboody, R., Fung. H & Dahl, R. E. (2017). Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood. Proceedings of the National Academy of Sciences, 114(30), 7892-7899.
Bayesian models of child development
A. Gopnik & E. Bonawitz (2015). Bayesian models of child development. Wiley Interdisciplinary Reviews (WIRES) Cognitive Science, 6:75-86, DOI: 10.1002/wcs.1330.
When younger learners can be better (or at least more open-minded) than older ones
A. Gopnik, T. L. Griffiths, & C. G. Lucas (2015). When younger learners can be better (or at least more open-minded) than older ones. Current Directions in Psychological Science, 24(2) 87-92, DOI: 10.1177/0963721414556653.
Sampling in cognitive development
E. Bonawitz, S. Denison, T. L. Griffiths, & A. Gopnik (2014). Probabilistic Models, Learning Algorithms, Response Variability: Sampling in Cognitive Development Trends in Cognitive Sciences, DOI: org/10.1016/j.tics.2014.06.006.
Pretense, counterfactuals, and Bayesian causal models
D. Weisberg & A. Gopnik (2013). Pretense, counterfactuals, and Bayesian causal models: Why what isn't real really matters. Cognitive Science 1-14 DOI: 10:1111/cogs.1206.
Causality, counterfactual reasoning, pretence
D. Buchsbaum, S. Bridgers, D. S. Weisberg, & A. Gopnik (2012). The power of possibility: Causal learning, counterfactual reasoning, and pretend play. Philosophical Transactions of the Royal Society B, 367:2202-2212.
C. Walker & A. Gopnik (2013). Causality and imagination. In M. Taylor (Ed.). The Development of Imagination. New York: Oxford University Press.
A. Gopnik (2012). Causality. In P. Zelazo (ed.) The Oxford Handbook of Developmental Psychology. New York: Oxford University Press.
A. Gopnik (2010). How babies think. Scientific American July 2010, 76-81.
Data-mining probabilists or experimental determinists? ...
T. Richardson, L. Schulz, & A. Gopnik (2007). Data-mining probabilists or experimental determinists? A dialogue on the principles underlying causal learning in children. In A. Gopnik & L. Schulz (Eds.). Causal learning: Psychology, philosophy, computation. New York: Oxford University Press. 208-230.
Causal Bayes nets for dummies, the psychology of causal inference for nerds - A correspondence
A. Gopnik & L. Schulz (2007). Introduction to A. Gopnik & L. Schulz (Eds.). Causal learning: Psychology, philosophy, computation. New York: Oxford University Press, 2007, 358 pp.
Learning from doing: Intervention and causal inference in children
L. Schulz, T. Kushnir, & A. Gopnik (2007). Learning from doing: Intervention and causal inference in children. In A. Gopnik & L. Schulz (Eds.). Causal learning: Psychology, philosophy, computation. New York: Oxford University Press.
Bayesian networks, Bayesian learning and cognitive development
A. Gopnik, & J. Tenenbaum (2007). Bayesian networks, Bayesian learning and cognitive development. Developmental Science (special section on Bayesian and Bayes-Net approaches to development) 10(3):281-287.
A brand new ball game: Bayes net and neural net learning mechanisms in children
A. Gopnik & C. Glymour (2006). A brand new ball game: Bayes net and neural net learning mechanisms in children. Processes of change in brain and cognitive development: Attention and performance xxi. Attention and Performance. 349-372.
Mechanisms of theory formation in young children
A. Gopnik & L. Schulz (2004). Mechanisms of theory-formation in young children. Trends in Cognitive Science 8:8.
A. Gopnik (2004). Finding our inner scientist. Daedalus 133(1):21-28.
A theory of causal learning in children: Causal maps and Bayes nets
A. Gopnik, C. Glymour, D. Sobel, L. Schulz, T. Kushnir, & D. Danks (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review 111(1):1-31.
The theory theory as an alternative to the innateness hypothesis
A. Gopnik (2003). The theory theory as an alternative to the innateness hypothesis. In L. Antony and N. Hornstein (Eds.), Chomsky and his critics. Oxford: Blackwells.
Words, kinds and causal powers: A theory theory perspective ...
A. Gopnik & T. Nazzi (2003). Words, kinds and causal powers: A theory theory perspective on early naming and categorization. In D. Rakison & L. Oakes (Eds.), Early category and concept development: Making sense of the blooming, buzzing confusion. New York: Oxford University Press. xxi, 303-329.
Causal maps and Bayes nets: A cognitive and computational account of theory-formation
A. Gopnik, & C. Glymour (2002). Causal maps and Bayes nets: A cognitive and computational account of theory-formation. In P. Carruthers, S. Stich, M. Siegal (Eds.), The cognitive basis of science. Cambridge: Cambridge University Press. 117-132.
What children will teach scientists
A. Gopnik (2002). What children will teach scientists. In J. Brockman (Ed.), The next fifty years: Science in the first half of the twenty-first century. New York: Vintage.
Explanation as orgasm and the drive for causal understanding: ...
A. Gopnik (2000). Explanation as orgasm and the drive for causal understanding: The evolution, function and phenomenology of the theory-formation system. In F. Keil & R. Wilson (Eds.), Cognition and explanation. Cambridge, Mass.: MIT Press. 299-323.