Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development

Trends in Cognitive Sciences

Volumn 18, Issue 10, 2014, Pages 497-500

  Bonawitz, Elizabeth ^a   Denison, Stephanie ^b   Griffiths, Thomas L ^c   Gopnik, Alison ^c  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF WATERLOO   (Canada)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Approximate bayesian inference; Causal learning; Cognitive development; Sampling hypothesis

Indexed keywords

CHILD BEHAVIOR; COGNITION; COGNITIVE DEVELOPMENT; HUMAN; HYPOTHESIS; LEARNING; LEARNING ALGORITHM; MONTE CARLO METHOD; NOTE; PREDICTION; PROBABILITY; REINFORCEMENT; SAMPLING; STATISTICAL MODEL; ADOLESCENT; ALGORITHM; CHILD; CHILD DEVELOPMENT; PHYSIOLOGY;

ADOLESCENT; ALGORITHMS; CHILD; CHILD DEVELOPMENT; COGNITION; HUMANS; LEARNING; PROBABILITY;

EID: 84908067175     PISSN: 13646613     EISSN: 1879307X     Source Type: Journal    
DOI: 10.1016/j.tics.2014.06.006     Document Type: Note

References (15)

1. Gopnik A., Wellman H.M. Reconstructing constructivism: causal models, Bayesian learning mechanisms and the theory theory. Psychol. Bull. 2012, 38:1085-1108.
2. Marcus G.F., Davis E. How robust are probabilistic models of higher-level cognition?. Psychol. Sci. 2013, 24:2351-2360.
3. Marr D. Vision 1982, Freeman Publishers.
4. Bonawitz E.B., et al. Balancing theories and evidence in children's exploration, explanations, and learning. Cogn. Psychol. 2012, 64:215-234.
5. Kushnir T., Gopnik A. Children infer causal strength from probabilities and interventions. Psychol. Sci. 2005, 16:678-683.
6. Schulz L.E., et al. Can being scared make your tummy ache? Naive theories, ambiguous evidence and preschoolers' causal inferences. Dev. Psychol. 2007, 43:1124-1139.
7. Sobel D.M., et al. Children's causal inferences from indirect evidence: backwards blocking and Bayesian reasoning in preschoolers. Cogn. Sci. 2004, 28:303-333.
8. Vul E., Pashler H. Measuring the crowd within: probabilistic representations within individuals. Psychol. Sci. 2008, 19:645-647.
9. Goodman N.D., et al. A rational analysis of rule-based concept learning. Cogn. Sci. 2008, 32:108-154.
10. Mozer M.C., et al. Optimal predictions in everyday cognition: the wisdom of individuals or crowds?. Cogn. Sci. 2008, 32:1133-1147.
11. Vul E., et al. One and done? Optimal decisions from very few samples. Cogn. Sci. 2014, 38:599-637.
12. Siegler R.S. Emerging Minds: The Process of Change in Children's Thinking 1998, Oxford University Press.
13. Jones M.H., Liverant S. Effects of age differences on choice behavior. Child Dev. 1960, 31:673-680.
14. Denison S., et al. Rational variability in children's causal inferences: the sampling hypothesis. Cognition 2013, 126:285-300.
15. Bonawitz E., et al. Win-stay, lose-shift: a simple sequential algorithm for approximating Bayesian inference. Cogn. Psychol. 2014, (in press).