1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Developmental Psychology
  4. Volume 2, 2020
  5. Article

Annual Review of Developmental Psychology

Volume 2, 2020

Bayesian Models of Conceptual Development: Learning as Building Models of the World

Abstract

A Bayesian framework helps address, in computational terms, what knowledge children start with and how they construct and adapt models of the world during childhood. Within this framework, inference over hierarchies of probabilistic generative programs in particular offers a normative and descriptive account of children's model building. We consider two classic settings in which cognitive development has been framed as model building: () core knowledge in infancy and () the child as scientist. We interpret learning in both of these settings as resource-constrained, hierarchical Bayesian program induction with different primitives and constraints. We examine what mechanisms children could use to meet the algorithmic challenges of navigating large spaces of potential models, in particular the proposal of the child as hacker and how it might be realized by drawing on recent computational advances. We also discuss prospects for a unifying account of model building across scientific theories and intuitive theories, and in biological and cultural evolution more generally.

Keyword(s): Bayesian models, child as hacker, cognitive development, computational modeling, core knowledge, intuitive theories, program learning
Loading

Article metrics loading...

/content/journals/10.1146/annurev-devpsych-121318-084833
2020-12-15
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/devpsych/2/1/annurev-devpsych-121318-084833.html?itemId=/content/journals/10.1146/annurev-devpsych-121318-084833&mimeType=html&fmt=ahah

Literature Cited

  1. Alur R, Radhakrishna A, Udupa A 2017. Scaling enumerative program synthesis via divide and conquer. Proceedings of the 23rd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2017)319–36 Berlin: Springer
     [Google Scholar]
  2. Anderson EM, Hespos SJ, Rips LJ 2018. Five-month-old infants have expectations for the accumulation of nonsolid substances. Cognition 175:1–10
     [Google Scholar]
  3. Anderson JR. 1990. The Adaptive Character of Thought Hillsdale, NJ: Erlbaum
  4. Baillargeon R, Li J, Gertner Y, Wu D 2011. How do infants reason about physical events?. The Wiley-Blackwell Handbook of Childhood Cognitive Development U Goswami 11–48 Oxford, UK: Blackwell. , 2nd ed..
     [Google Scholar]
  5. Baillargeon R, Li J, Ng W, Yuan S 2008. An account of infants' physical reasoning. See Woodward & Needham 2008 66–116
  6. Baillargeon R, Spelke ES, Wasserman S 1985. Object permanence in five-month-old infants. Cognition 20:191–208
     [Google Scholar]
  7. Baker CL, Jara-Ettinger J, Saxe R, Tenenbaum JB 2017. Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nat. Hum. Behav. 1:0064
     [Google Scholar]
  8. Baker CL, Saxe R, Tenenbaum JB 2009. Action understanding as inverse planning. Cognition 113:329–49
     [Google Scholar]
  9. Balog M, Gaunt AL, Brockschmidt M, Nowozin S, Tarlow D 2017. DeepCoder: learning to write programs. arXiv:1611.01989 [cs.LG]
  10. Bates CJ, Yildirim I, Tenenbaum JB, Battaglia P 2019. Modeling human intuitions about liquid flow with particle-based simulation. PLOS Comput. Biol. 15:e1007210
     [Google Scholar]
  11. Battaglia PW, Hamrick JB, Tenenbaum JB 2013. Simulation as an engine of physical scene understanding. PNAS 110:18327–32
     [Google Scholar]
  12. Baum EB. 2004. What Is Thought? Cambridge, MA: MIT Press
  13. Bonawitz E, Ullman TD, Bridgers S, Gopnik A, Tenenbaum JB 2019. Sticking to the evidence? A behavioral and computational case study of micro-theory change in the domain of magnetism. Cogn. Sci. 43:e12765
     [Google Scholar]
  14. Carey S. 1985. Conceptual Change in Childhood Cambridge, MA: MIT Press/Bradford
  15. Carey S. 2009. The Origin of Concepts Oxford, UK: Oxford Univ. Press
  16. Carey S, Spelke E. 1994. Domain-specific knowledge and conceptual change. Mapping the Mind: Domain Specificity in Cognition and Culture LA Hirschfeld, SA Gelman 169–200 Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  17. Carey S, Spelke E. 1996. Science and core knowledge. Philos. Sci. 63:515–33
     [Google Scholar]
  18. Chater N, Oaksford M. 2013. Programs as causal models: speculations on mental programs and mental representation. Cogn. Sci. 37:1171–91
     [Google Scholar]
  19. Chu J, Schulz LE. 2020. Play, curiosity, and cognition. Annu. Rev. Dev. Psychol. 2:317–43
     [Google Scholar]
  20. Craik K. 1943. The Nature of Explanation Cambridge, UK: Cambridge Univ. Press
  21. Cropper A, Muggleton SH. 2017. Learning higher-order logic programs through abstraction and invention. Proceedings of the 25th International Joint Conference on Artificial Intelligence1418–24 Palo Alto, CA: AAAI
     [Google Scholar]
  22. Csibra G. 2008. Goal attribution to inanimate agents by 6.5-month-old infants. Cognition 107:705–17
     [Google Scholar]
  23. Csibra G, Biró S, Koós O, Gergely G 2003. One-year-old infants use teleological representations of actions productively. Cogn. Sci. 27:111–33
     [Google Scholar]
  24. Czégel D, Zachar I, Szathmáry E 2018. Major evolutionary transitions as Bayesian structure learning. bioRxiv 359596. https://doi.org/10.1101/359596
    [Crossref]
  25. Dawkins R. 1986. The Blind Watchmaker London: Norton
  26. Dechter E, Malmaud J, Adams RP, Tenenbaum JB 2013. Bootstrap learning via modular concept discovery. Proceedings of the 23rd International Joint Conference on Artificial Intelligence1302–9 Menlo Park, CA: AAAI
     [Google Scholar]
  27. Devlin J, Uesato J, Bhupatiraju S, Singh R, Mohamed AR, Kohli P 2017. RobustFill: neural program learning under noisy I/O. Proceedings of the 34th International Conference on Machine Learning990–98 New York: ACM
     [Google Scholar]
  28. Dewar KM, Xu F. 2010. Induction, overhypothesis, and the origin of abstract knowledge: evidence from 9-month-old infants. Psychol. Sci. 21:1871–77
     [Google Scholar]
  29. Dirac PAM. 1963. The evolution of the physicist's picture of nature. Sci. Am. 208:45–53
     [Google Scholar]
  30. Eco U, Sebeok TA 1988. The Sign of Three: Dupin, Holmes, Peirce Bloomington: Indiana Univ. Press
  31. Ellis K, Morales L, Sablé-Meyer M, Solar-Lezama A, Tenenbaum J 2018. Learning libraries of subroutines for neurally-guided Bayesian program induction. Advances in Neural Information Processing Systems 31ed. S Bengio, H Wallach, H Larochelle, K Grauman, N Cesa-Bianchi, R Garnettpp. 7805–15 Red Hook, NY: Curran
     [Google Scholar]
  32. Ellis K, Solar-Lezama A, Tenenbaum J 2016. Sampling for Bayesian program learning. Advances in Neural Information Processing Systems 29ed. DD Lee, M Sugiyama, UV Luxburg, I Guyon, R Garnettpp. 1297–305 Red Hook, NY: Curran
     [Google Scholar]
  33. Ellis K, Wong C, Nye M, Sablé-Meyer M, Cary L et al. 2020. DreamCoder: growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning. arXiv:2006.08381 [cs.AI]
  34. Erdogan G, Jacobs RA. 2017. Visual shape perception as Bayesian inference of 3D object-centered shape representations. Psychol. Rev. 124:740–61
     [Google Scholar]
  35. Esposito A. 1969. A simplified method for analyzing hydraulic circuits by analogy. Mach. Des. 41:173–77
     [Google Scholar]
  36. Fleming I. 1959. Goldfinger London: Cape
  37. Fodor JA. 1975. The Language of Thought Cambridge, MA: Harvard Univ. Press
  38. Fodor JA. 1998. Concepts: Where Cognitive Science Went Wrong New York: Oxford Univ. Press
  39. Forbus K. 2019. Qualitative Representations: How People Reason and Learn About the Continuous World Cambridge, MA: MIT Press
  40. Gelman SA. 2009. Learning from others: children's construction of concepts. Annu. Rev. Psychol. 60:115–40
     [Google Scholar]
  41. Gelman SA, Carlin JB, Stern HS, Rubin DB 2013. Bayesian Data Analysis New York: Chapman & Hall. , 3rd ed..
  42. Geman S, Geman D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6:721–41
     [Google Scholar]
  43. Gentner D. 1989. The mechanisms of analogical learning. Similarity and Analogical Reasoning S Vosniadou, A Ortony 199–241 Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  44. Gentner D, Stevens AL 1983. Mental Models Hillsdale, NJ: Erlbaum
  45. Gershman SJ, Goodman ND. 2014. Amortized inference in probabilistic reasoning. Proceedings of the 36th Annual Conference of the Cognitive Science Society517–22 Austin, TX: Cogn. Sci. Soc.
     [Google Scholar]
  46. Gershman SJ, Horvitz EJ, Tenenbaum JB 2015. Computational rationality: a converging paradigm for intelligence in brains, minds, and machines. Science 349:273–78
     [Google Scholar]
  47. Gerstenberg T, Tenenbaum JB. 2017. Intuitive theories. Oxford Handbook of Causal Reasoning MR Waldmann 515–48 Oxford, UK: Oxford Univ. Press
     [Google Scholar]
  48. Gilden DL, Proffitt DR. 1989. Understanding collision dynamics. J. Exp. Psychol. Hum. Percept. Perform. 15:372–83
     [Google Scholar]
  49. Goodman ND. 1983. Fact, Fiction, and Forecast Cambridge, MA: Harvard Univ. Press
  50. Goodman ND, Mansinghka VK, Roy DM, Bonawitz K, Tenenbaum JB 2008. Church: a language for generative models. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence220–29 New York: ACM
     [Google Scholar]
  51. Goodman ND, Tenenbaum JB, Gerstenberg T 2014. Concepts in a probabilistic language of thought Tech. Rep., Cent. Brains Minds Mach., MIT Cambridge, MA:
  52. Goodman ND, Tenenbaum JBProbMods Contrib 2016. Probabilistic Models of Cognition http://probmods.org , 2nd ed..
  53. Goodman ND, Ullman T, Tenenbaum J 2011. Learning a theory of causality. Psychol. Rev. 118:110–19
     [Google Scholar]
  54. Gopnik A. 1996. The scientist as child. Philos. Sci. 63:485–514
     [Google Scholar]
  55. Gopnik A, Glymour C, Sobel D, Schulz L, Kushnir T, Danks D 2004. A theory of causal learning in children: causal maps and Bayes nets. Psychol. Rev. 111:1–31
     [Google Scholar]
  56. Gopnik A, Griffiths TL, Lucas CG 2015. When younger learners can be better (or at least more open-minded) than older ones. Curr. Dir. Psychol. Sci. 24:87–92
     [Google Scholar]
  57. Gopnik A, Meltzoff AN. 1997. Words, Thoughts, and Theories Cambridge, MA: MIT Press
  58. Gopnik A, O'Grady S, Lucas CG, Griffiths TL, Wente A et al. 2017. Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood. PNAS 114:7892–99
     [Google Scholar]
  59. Gopnik A, Wellman HM. 1992. Why the child's theory of mind really is a theory. Mind Lang 7:145–71
     [Google Scholar]
  60. Gopnik A, Wellman HM. 1994. The theory theory. Mapping the Mind: Domain Specificity in Cognition and Culture LA Hirschfeld, SA Gelman 257–93 Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  61. Gopnik A, Wellman HM. 2012. Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory. Psychol. Bull. 138:1085–108
     [Google Scholar]
  62. Gregory J. 2018. Game Engine Architecture Boca Raton, FL: CRC
  63. Griffiths TL, Chater N, Kemp C, Perfors A, Tenenbaum JB 2010. Probabilistic models of cognition: exploring representations and inductive biases. Trends Cogn. Sci. 14:357–64
     [Google Scholar]
  64. Griffiths TL, Kemp C, Tenenbaum JB 2008. Bayesian models of cognition. The Cambridge Handbook of Computational Psychology R Sun 59–100 Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  65. Griffiths TL, Lieder F, Goodman ND 2015. Rational use of cognitive resources: levels of analysis between the computational and the algorithmic. Top. Cogn. Sci. 7:217–29
     [Google Scholar]
  66. Griffiths TL, Tenenbaum JB. 2005. Structure and strength in causal induction. Cogn. Psychol. 51:285–386
     [Google Scholar]
  67. Griffiths TL, Tenenbaum JB. 2009. Theory-based causal induction. Psychol. Rev. 116:661–716
     [Google Scholar]
  68. Griffiths TL, Vul E, Sanborn AN 2012. Bridging levels of analysis for probabilistic models of cognition. Curr. Dir. Psychol. Sci. 21:263–68
     [Google Scholar]
  69. Gulwani S, Hernndez-Orallo J, Kitzelmann E, Muggleton HS, Schmid U, Zorn GB 2015. Inductive programming meets the real world. Commun. ACM 58:1190–99
     [Google Scholar]
  70. Gulwani S, Polozov A, Singh R 2017. Program Synthesis: Foundations and Trends in Programming Languages, Vol. 4 Delft, Neth: Now
  71. Hamrick JB, Battaglia PW, Griffiths TL, Tenenbaum JB 2016. Inferring mass in complex scenes by mental simulation. Cognition 157:61–76
     [Google Scholar]
  72. Henderson L, Goodman ND, Tenenbaum JB, Woodward JF 2010. The structure and dynamics of scientific theories: a hierarchical Bayesian perspective. Philos. Sci. 77:172–200
     [Google Scholar]
  73. Hewitt LB, Le TA, Tenenbaum JB 2020. Learning to learn generative models with Memoised Wake-Sleep Paper presented at the Conference on Uncertainty in Artificial Intelligence, online, Aug. 3–6
  74. Jara-Ettinger J, Gweon H, Schulz LE, Tenenbaum JB 2016. The nave utility calculus: computational principles underlying commonsense psychology. Trends Cogn. Sci. 20:589–604
     [Google Scholar]
  75. Jaynes ET. 2003. Probability Theory: The Logic of Science Cambridge, UK: Cambridge Univ. Press
  76. Johnson-Laird PN. 2004. The history of mental models. Psychology of Reasoning K Manktelow, MC Chung 189–222 New York: Psychology
     [Google Scholar]
  77. Johnston AM, Sheskin M, Keil FC 2019. Learning the relevance of relevance and the trouble with truth: evaluating explanatory relevance across childhood. J. Cogn. Dev. 20:555–72
     [Google Scholar]
  78. Kemp C. 2008. The acquisition of inductive constraints PhD Thesis, MIT Cambridge, MA:
  79. Kemp C, Goodman ND, Tenenbaum JB 2008. Theory acquisition and the language of thought. Proceedings of the 30th Annual Conference of the Cognitive Science Society1606–11 Austin, TX: Cogn. Sci. Soc.
     [Google Scholar]
  80. Kemp C, Goodman ND, Tenenbaum JB 2010. Learning to learn causal models. Cogn. Sci. 34:1185–243
     [Google Scholar]
  81. Kemp C, Perfors A, Tenenbaum J 2007. Learning overhypotheses with hierarchical Bayesian models. Dev. Sci. 10:307–21
     [Google Scholar]
  82. Kemp C, Tenenbaum JB. 2008. The discovery of structural form. PNAS 105:10687–92
     [Google Scholar]
  83. Kemp C, Tenenbaum JB. 2009. Structured statistical models of inductive reasoning. Psychol. Rev. 116:20–58
     [Google Scholar]
  84. Kiley Hamlin J, Ullman T, Tenenbaum JB, Goodman N, Baker C 2013. The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model. Dev. Sci. 16:209–26
     [Google Scholar]
  85. Kirkpatrick S, Gelatt CD, Vecchi MP 1983. Optimization by simulated annealing. Science 220:671–80
     [Google Scholar]
  86. Koza JR. 1994. Genetic Programming II Cambridge, MA: MIT Press
  87. Kubricht J, Zhu Y, Jiang C, Terzopoulos D, Zhu S, Lu H 2017. Consistent probabilistic simulation underlying human judgment in substance dynamics. Proceedings of the 39th Annual Conference of the Cognitive Science Society700–5 Austin, TX: Cogn. Sci. Soc.
     [Google Scholar]
  88. Kuhn TS. 2012. The Structure of Scientific Revolutions Chicago: Univ. Chicago Press
  89. Lake BM, Salakhutdinov R, Tenenbaum JB 2015. Human-level concept learning through probabilistic program induction. Science 350:1332–38
     [Google Scholar]
  90. Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ 2017. Building machines that learn and think like people. Behav. Brain Sci. 40:e253
     [Google Scholar]
  91. Landau B, Gleitman LR. 1985. Language and Experience: Evidence from the Blind Child Cambridge, MA: Harvard Univ. Press
  92. Lerer A, Gross S, Fergus R 2016. Learning physical intuition of block towers by example. arXiv:1603.01312 [cs.AI]
  93. Levin LA. 1973. Universal sequential search problems. Probl. Pereda. Inf. 9:115–16
     [Google Scholar]
  94. Li M, Vitányi P. 2008. An Introduction to Kolmogorov Complexity and Its Applications Berlin: Springer. , 3rd ed..
  95. Lieder F, Griffiths TL. 2020. Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behav. Brain Sci. 43:e1
     [Google Scholar]
  96. Lin D, Dechter E, Ellis K, Tenenbaum JB, Muggleton S 2014. Bias reformulation for one-shot function induction. Proceedings of the 21st European Conference on Artificial Intelligence525–30 Amsterdam: IOS
     [Google Scholar]
  97. Liu S, Ullman TD, Tenenbaum JB, Spelke ES 2017. Ten-month-old infants infer the value of goals from the costs of actions. Science 358:1038–41
     [Google Scholar]
  98. Lombrozo T. 2016. Explanatory preferences shape learning and inference. Trends Cogn. Sci. 20:748–59
     [Google Scholar]
  99. Lucas CG, Griffiths TL, Xu F, Fawcett C, Gopnik A et al. 2014. The child as econometrician: a rational model of preference understanding in children. PLOS ONE 9:e92160
     [Google Scholar]
  100. Mach E. 1910. Populär-wissenschaftliche Vorlesungen Leipzig, Ger: Barth
  101. MacKay DJ. 2003. Information Theory, Inference and Learning Algorithms Cambridge, UK: Cambridge Univ. Press
  102. Marr D. 1982. Vision London: Freeman
  103. Murphy GL, Medin DL. 1985. The role of theories in conceptual coherence. Psychol. Rev. 92:289–316
     [Google Scholar]
  104. Nersessian NJ. 1992. How do scientists think? Capturing the dynamics of conceptual change in science. Cogn. Models Sci. 15:3–44
     [Google Scholar]
  105. Newman GE, Keil FC, Kuhlmeier VA, Wynn K 2010. Early understandings of the link between agents and order. PNAS 107:17140–45
     [Google Scholar]
  106. Nye M, Hewitt L, Tenenbaum J, Solar-Lezama A 2019. Learning to infer program sketches. J. Mach. Learn. Res. 97:4861–70
     [Google Scholar]
  107. Osera PM, Zdancewic S. 2015. Type-and-example-directed program synthesis. Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation619–30 New York: ACM
     [Google Scholar]
  108. Pearl J. 1988. Probabilistic reasoning in intelligent systems: networks of plausible inference. Berlin: Springer
  109. Pearl J. 2000. Causality: Models, Reasoning, and Inference Cambridge, UK: Cambridge Univ. Press
  110. Perfors A, Tenenbaum JB, Griffiths TL, Xu F 2011. A tutorial introduction to Bayesian models of cognitive development. Cognition 120:302–21
     [Google Scholar]
  111. Perfors A, Tenenbaum JB, Wonnacott E 2010. Variability, negative evidence, and the acquisition of verb argument constructions. J. Child Lang. 37:607–42
     [Google Scholar]
  112. Phillips J, Morris A, Cushman F 2019. How we know what not to think. Trends Cogn. Sci. 23:1026–40
     [Google Scholar]
  113. Piantadosi ST, Tenenbaum JB, Goodman ND 2012. Bootstrapping in a language of thought: a formal model of numerical concept learning. Cognition 123:199–217
     [Google Scholar]
  114. Piantadosi ST, Tenenbaum JB, Goodman ND 2016. The logical primitives of thought: empirical foundations for compositional cognitive models. Psychol. Rev. 123:392–424
     [Google Scholar]
  115. Polozov O, Gulwani S. 2015. FlashMeta: a framework for inductive program synthesis. Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2015)107–26 New York: ACM
     [Google Scholar]
  116. Rule JS, Schulz E, Piantadosi ST, Tenenbaum JB 2018. Learning list concepts through program induction. Proceedings of the 40th Annual Conference of the Cognitive Science Society2370–75 Austin, TX: Cogn. Sci. Soc.
     [Google Scholar]
  117. Rule JS, Tenenbaum JB, Piantadosi ST 2020. The child as hacker. Trends Cogn. Sci. In press
    [Google Scholar]
  118. Russell SJ, Norvig P. 2002. Artificial Intelligence: A Modern Approach Englewood Cliffs, NJ: Prentice Hall. , 2nd ed..
  119. Russell SJ, Norvig P. 2020. Artificial Intelligence: A Modern Approach London: Pearson. , 4th ed..
  120. Saad FA, Cusumano-Towner MF, Schaechtle U, Rinard MC, Mansinghka VK 2019. Bayesian synthesis of probabilistic programs for automatic data modeling. Proc. ACM Program. Lang. 3:37
     [Google Scholar]
  121. Sanborn AN, Mansinghka VK, Griffiths TL 2013. Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychol. Rev. 120:411–37
     [Google Scholar]
  122. Saxe R, Tenenbaum J, Carey S 2005. Secret agents: inferences about hidden causes by 10- and 12-month-old infants. Psychol. Sci. 16:995–1001
     [Google Scholar]
  123. Schulz L. 2012a. Finding new facts; thinking new thoughts. Adv. Child Dev. Behav. 43:269–94
     [Google Scholar]
  124. Schulz L. 2012b. The origins of inquiry: inductive inference and exploration in early childhood. Trends Cogn. Sci. 16:382–89
     [Google Scholar]
  125. Shi L, Griffiths TL, Feldman NH, Sanborn AN 2010. Exemplar models as a mechanism for performing Bayesian inference. Psychon. Bull. Rev. 17:443–64
     [Google Scholar]
  126. Siegler RS. 2007. Cognitive variability. Dev. Sci. 10:104–9
     [Google Scholar]
  127. Siegler RS, Chen Z. 1998. Developmental differences in rule learning: a microgenetic analysis. Cogn. Psychol. 36:273–310
     [Google Scholar]
  128. Simon HA. 1956. Rational choice and the structure of the environment. Psychol. Rev. 63:129–38
     [Google Scholar]
  129. Simon HA. 1962. An information processing theory of intellectual development. Monogr. Soc. Res. Child Dev. 27:154–55
     [Google Scholar]
  130. Smith A. 1776. The Wealth of Nations London: Strahan
  131. Smith DR. 1984. Synthesis of LISP programs from examples: a survey. Automatic Program Construction Techniques AW Biermann, G Guiho, Y Kodratoff 307–24 London: Macmillan
     [Google Scholar]
  132. Smith DR. 1985. Top-down synthesis of divide-and-conquer algorithms. Artif. Intell. 27:43–96
     [Google Scholar]
  133. Smith KA, Mei L, Yao S, Wu J, Spelke E et al. 2019. Modeling expectation violation in intuitive physics with coarse probabilistic object representations. Advances in Neural Information Processing Systems 32ed. H Wallach, H Larochelle, A Beygelzimer, F d'Alché-Buc, E Fox, R Garnettpp. 8985–95 Red Hook, NY: Curran
     [Google Scholar]
  134. Smith KA, Mei L, Yao S, Wu J, Spelke E et al. 2020. The fine structure of surprise in intuitive physics: When, why, and how much?. Proceedings of the 42nd Annual Conference of the Cognitive Science Society3048–54 Austin, TX: Cogn. Sci. Soc.
     [Google Scholar]
  135. Solar-Lezama A. 2009. The sketching approach to program synthesis. Proceedings of the Asian Symposium on Programming Languages and Systems (ASPLAS)4–13 Berlin: Springer
     [Google Scholar]
  136. Spelke ES. 1990. Principles of object perception. Cogn. Sci. 14:29–56
     [Google Scholar]
  137. Spelke ES. 1995. Initial knowledge: six suggestions. Cognition 50:433–47
     [Google Scholar]
  138. Spelke ES, Breinlinger K, Macomber J, Jacobson K 1992. Origins of knowledge. Psychol. Rev. 99:605–32
     [Google Scholar]
  139. Spelke ES, Kestenbaum R, Simons DJ, Wein D 1995. Spatiotemporal continuity, smoothness of motion and object identity in infancy. Br. J. Dev. Psychol. 13:113–42
     [Google Scholar]
  140. Spelke ES, Kinzler K. 2007. Core knowledge. Dev. Sci. 10:89–96
     [Google Scholar]
  141. Stahl AE, Feigenson L. 2015. Observing the unexpected enhances infants' learning and exploration. Science 348:91–94
     [Google Scholar]
  142. Stanley KO, Clune J, Lehman J, Miikkulainen R 2019. Designing neural networks through neuroevolution. Nat. Mach. Intell. 1:24–35
     [Google Scholar]
  143. Sutton RS, Barto AG. 2018. Reinforcement Learning: An Introduction Cambridge, MA: MIT Press
  144. Téglás E, Vul E, Girotto V, Gonzalez M, Tenenbaum JB, Bonatti LL 2011. Pure reasoning in 12-month-old infants as probabilistic inference. Science 332:1054–59
     [Google Scholar]
  145. Tenenbaum JB, Griffiths TL, Kemp C 2006. Theory-based Bayesian models of inductive learning and reasoning. Trends Cogn. Sci. 10:309–18
     [Google Scholar]
  146. Tenenbaum JB, Griffiths TL, Niyogi S 2007. Intuitive theories as grammars for causal inference. Causal Learning: Psychology, Philosophy, and Computation A Gopnik, L Schulz 301–22 Oxford, UK: Oxford Univ. Press
     [Google Scholar]
  147. Tenenbaum JB, Kemp C, Griffiths TL, Goodman ND 2011. How to grow a mind: statistics, structure, and abstraction. Science 331:1279–85
     [Google Scholar]
  148. Thagard P. 2008. Conceptual change in the history of science: life, mind, and disease. International Handbook of Research on Conceptual Change S Vosniadou 374–87 New York: Taylor & Francis
     [Google Scholar]
  149. Turing AM. 2009. Computing machinery and intelligence. Parsing the Turing Test R Epstein, G Roberts, G Beber 23–65 Berlin: Springer
     [Google Scholar]
  150. Ullman TD. 2015. On the nature and origin of intuitive theories: learning, physics and psychology PhD Thesis, MIT Cambridge, MA:
  151. Ullman TD, Goodman ND, Tenenbaum JB 2012. Theory learning as stochastic search in the language of thought. Cogn. Dev. 27:455–80
     [Google Scholar]
  152. Ullman TD, Siegel MH, Tenenbaum JB, Gershman S 2016. Coalescing the vapors of human experience into a viable and meaningful comprehension. Proceedings of the 38th Annual Conference of the Cognitive Science Society1493–98 Austin, TX: Cogn. Sci. Soc.
     [Google Scholar]
  153. Ullman TD, Spelke E, Battaglia P, Tenenbaum JB 2017. Mind games: game engines as an architecture for intuitive physics. Trends Cogn. Sci. 21:649–65
     [Google Scholar]
  154. Ullman TD, Stuhlmüller A, Goodman ND, Tenenbaum JB 2018. Learning physical parameters from dynamic scenes. Cogn. Psychol. 104:57–82
     [Google Scholar]
  155. Vul E, Goodman N, Griffiths TL, Tenenbaum JB 2014. One and done? Optimal decisions from very few samples. Cogn. Sci. 38:599–637
     [Google Scholar]
  156. Waxman SR, Markow DB. 1995. Words as invitations to form categories: evidence from 12- to 13-month-old infants. Cogn. Psychol. 29:257–302
     [Google Scholar]
  157. Wellman HM. 2014. Making Minds: How Theory of Mind Develops Oxford, UK: Oxford Univ. Press
  158. Wellman HM, Fang F, Peterson CC 2011. Sequential progressions in a theory-of-mind scale: longitudinal perspectives. Child Dev 82:780–92
     [Google Scholar]
  159. Wellman HM, Gelman S. 1998. Knowledge acquisition in foundational domains. Handbook of Child Psychology, Vol. 2: Cognition, Perception, and Language W Damon 523–73 New York: Wiley. , 6th ed..
     [Google Scholar]
  160. Woodward AL. 1998. Infants selectively encode the goal object of an actor's reach. Cognition 69:1–34
     [Google Scholar]
  161. Woodward AL. 1999. Infants' ability to distinguish between purposeful and non-purposeful behaviors. Infant Behav. Dev. 22:145–60
     [Google Scholar]
  162. Woodward AL, Needham Aeds 2008. Learning and the Infant Mind Oxford, UK: Oxford Univ. Press
  163. Wu J, Yildirim I, Lim JJ, Freeman B, Tenenbaum J 2015. Galileo: perceiving physical object properties by integrating a physics engine with deep learning. Advances in Neural Information Processing Systems 28ed. C Cortes, ND Lawrence, DD Lee, M Sugiyama, R Garnettpp. 127–35 Red Hook, NY: Curran
     [Google Scholar]
  164. Xu F. 2019. Towards a rational constructivist theory of cognitive development. Psychol. Rev. 126:841–64
     [Google Scholar]
  165. Xu F, Tenenbaum JB. 2007. Word learning as Bayesian inference. Psychol. Rev. 114:245–62
     [Google Scholar]
/content/journals/10.1146/annurev-devpsych-121318-084833
Loading
Bayesian Models of Conceptual Development: Learning as Building Models of the World
Annual Review of Developmental Psychology 2, 533 (2020); https://doi.org/10.1146/annurev-devpsych-121318-084833
/content/journals/10.1146/annurev-devpsych-121318-084833
/content/journals/10.1146/annurev-devpsych-121318-084833
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error