Psychonomic Bulletin & Review

, Volume 24, Issue 5, pp 1511–1526 | Cite as

Explanation-based learning in infancy

Article

Abstract

In explanation-based learning (EBL), domain knowledge is leveraged in order to learn general rules from few examples. An explanation is constructed for initial exemplars and is then generalized into a candidate rule that uses only the relevant features specified in the explanation; if the rule proves accurate for a few additional exemplars, it is adopted. EBL is thus highly efficient because it combines both analytic and empirical evidence. EBL has been proposed as one of the mechanisms that help infants acquire and revise their physical rules. To evaluate this proposal, 11- and 12-month-olds (n = 260) were taught to replace their current support rule (that an object is stable when half or more of its bottom surface is supported) with a more sophisticated rule (that an object is stable when half or more of the entire object is supported). Infants saw teaching events in which asymmetrical objects were placed on a base, followed by static test displays involving a novel asymmetrical object and a novel base. When the teaching events were designed to facilitate EBL, infants learned the new rule with as few as two (12-month-olds) or three (11-month-olds) exemplars. When the teaching events were designed to impede EBL, however, infants failed to learn the rule. Together, these results demonstrate that even infants, with their limited knowledge about the world, benefit from the knowledge-based approach of EBL.

Keywords

Infant cognition Knowledge acquisition Explanation-based learning 

Notes

Author note

This research was supported by a grant from the NICHD (HD-21104) to R.B. We thank Frank Keil and Alan Leslie for helpful suggestions; Stephanie Sloane and the research staff at the UIUC Infant Cognition Laboratory for their help with the data collection; and the parents and infants who participated in the research.

References

  1. Baillargeon, R. (1995). A model of physical reasoning in infancy. In C. Rovee-Collier & L. P. Lipsitt (Eds.), Advances in infancy research (Vol. 9, pp. 305–371). Norwood: Ablex.Google Scholar
  2. Baillargeon, R. (1998). Infants’ understanding of the physical world. In M. Sabourin, F. Craik, & M. Robert (Eds.), Advances in psychological science (Vol. 2, pp. 503–529). London: Psychology Press.Google Scholar
  3. Baillargeon, R. (1999). Young infants’ expectations about hidden objects: A reply to three challenges. Developmental Science, 2, 115–132. doi: 10.1111/1467-7687.00061 CrossRefGoogle Scholar
  4. Baillargeon, R. (2008). Innate ideas revisited: For a principle of persistence in infants’ physical reasoning. Perspectives on Psychological Science, 3, 2–13. doi: 10.1111/j.1745-6916.2008.00056.x CrossRefPubMedPubMedCentralGoogle Scholar
  5. Baillargeon, R., & Carey, S. (2012). Core cognition and beyond: The acquisition of physical and numerical knowledge. In S. Pauen (Ed.), Early childhood development and later outcome (pp. 33–65). Cambridge: Cambridge University Press.Google Scholar
  6. Baillargeon, R., & DeVos, J. (1991). Object permanence in 3.5- and 4.5-month-old infants: Further evidence. Child Development, 62, 1227–1246. doi: 10.2307/1130803 CrossRefPubMedGoogle Scholar
  7. Baillargeon, R., Li, J., Gertner, Y., & Wu, D. (2011). How do infants reason about physical events? In U. Goswami (Ed.), The Wiley-Blackwell handbook of childhood cognitive development (2nd ed., pp. 11–48). Oxford: Blackwell.Google Scholar
  8. Baillargeon, R., Li, J., Ng, W., & Yuan, S. (2009). An account of infants’ physical reasoning. In A. Woodward & A. Needham (Eds.), Learning and the infant mind (pp. 66–116). New York: Oxford University Press.Google Scholar
  9. Baillargeon, R., Needham, A., & DeVos, J. (1992). The development of young infants’ intuitions about support. Early Development and Parenting, 1, 69–78. doi: 10.1002/edp.2430010203 CrossRefGoogle Scholar
  10. Baillargeon, R., Spelke, E. S., & Wasserman, S. (1985). Object permanence in 5-month-old infants. Cognition, 20, 191–208. doi: 10.1016/0010-0277(85)90008-3 CrossRefPubMedGoogle Scholar
  11. Baillargeon, R., Stavans, M., Wu, D., Gertner, Y., Setoh, P., Kittredge, A. K., & Bernard, A. (2012). Object individuation and physical reasoning in infancy: An integrative account. Language Learning and Development, 8, 4–46. doi: 10.1080/15475441.2012.630610 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Baillargeon, R., Wu, D., Yuan, S., & Luo, Y. (2009). Young infants’ expectations about self-propelled objects. In B. Hood & L. Santos (Eds.), The origins of object knowledge (pp. 285–352). Oxford: Oxford University Press.Google Scholar
  13. Bishop, C. (2006). Pattern recognition and machine learning. New York: Springer.Google Scholar
  14. Carey, S. (2009). The origin of concepts. New York: Oxford University Press.CrossRefGoogle Scholar
  15. Chow, C. K., & Liu, C. N. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14, 462–467.CrossRefGoogle Scholar
  16. Dan, N., Omori, T., & Tomiyasu, Y. (2000). Development of infants’ intuitions about support relations: Sensitivity to stability. Developmental Science, 3, 171–180. doi: 10.1111/1467-7687.00110 CrossRefGoogle Scholar
  17. Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. New York: Cambridge University Press.CrossRefGoogle Scholar
  18. DeJong, G. F. (Ed.). (1993). Investigating explanation-based learning. Boston: Kluwer Academic Press.Google Scholar
  19. DeJong, G. F. (2014). Explanation-based learning. In T. Gonzalez, J. Diaz-Herrera, & A. Tucker (Eds.), CRC computing handbook: Computer science and software engineering (3rd ed., pp. 66.1–66.26). Boca Raton: CRC Press.Google Scholar
  20. Gelman, R. (1990). First principles organize attention to and learning about relevant data: Number and the animate-inanimate distinction as examples. Cognitive Science, 14, 79–106. doi: 10.1207/s15516709cog1401_5 CrossRefGoogle Scholar
  21. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). Boca Raton: CRC Press.Google Scholar
  22. Gratch, J., & DeJong, G. F. (1996). A decision-theoretic approach to adaptive problem solving. Artificial Intelligence, 88, 101–142.CrossRefGoogle Scholar
  23. Greiner, R., & Jurisica, I. (1992). A statistical approach to solving the EBL utility problem. Proceedings of the Tenth National Conference on Artificial Intelligence (San Jose, CA), pp. 241–248.Google Scholar
  24. Hastie, T., Tibsirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). New York: Springer.CrossRefGoogle Scholar
  25. Hespos, S. J., & Baillargeon, R. (2001). Knowledge about containment events in very young infants. Cognition, 78, 207–245. doi: 10.1016/S0010-0277(00)00118-9 CrossRefPubMedGoogle Scholar
  26. Hespos, S. J., & Baillargeon, R. (2006). Décalage in infants’ knowledge about occlusion and containment events: Converging evidence from action tasks. Cognition, 99, B31–B41. doi: 10.1016/j.cognition.2005.01.010 CrossRefPubMedGoogle Scholar
  27. Hespos, S. J., & Baillargeon, R. (2008). Young infants’ actions reveal their developing knowledge of support variables: Converging evidence for violation-of-expectation findings. Cognition, 107, 304–316. doi: 10.1016/j.cognition.2007.07.009 CrossRefPubMedGoogle Scholar
  28. Huettel, S. A., & Needham, A. (2000). Effects of balance relations between objects on infants’ object segregation. Developmental Science, 3, 415–427. doi: 10.1111/1467-7687.00136
  29. Keil, F. C. (1995). The growth of causal understandings of natural kinds. In D. Sperber, D. Premack, & A. J. Premack (Eds.), Causal cognition: A multidisciplinary debate (pp. 234–262). Oxford: Oxford University Press, Clarendon Press.Google Scholar
  30. Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge: MIT Press.Google Scholar
  31. Lee, M. (Ed.). (2011). Hierarchical Bayesian models (Special issue). Journal of Mathematical Psychology, 55(1).Google Scholar
  32. Leonard, T., & Hsu, J. (1999). Bayesian methods. Cambridge: Cambridge University Press.Google Scholar
  33. Leslie, A. M. (1995). A theory of agency. In D. Sperber, D. Premack, & A. J. Premack (Eds.), Causal cognition: A multidisciplinary debate (pp. 121–149). Oxford: Oxford University Press, Clarendon Press.Google Scholar
  34. Loh, P., & Wainwright, M. J. (2013). Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. Annals of Statistics, 41, 3022–3049.CrossRefGoogle Scholar
  35. Luo, Y., & Baillargeon, R. (2005). When the ordinary seems unexpected: Evidence for incremental physical knowledge in young infants. Cognition, 95, 297–328. doi: 10.1016/j.cognition.2004.01.010 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Luo, Y., Kaufman, L., & Baillargeon, R. (2009). Young infants’ reasoning about physical events involving inert and self-propelled objects. Cognitive Psychology, 58, 441–486. doi: 10.1016/j.cogpsych.2008.11.001 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Minton, S., Carbonell, J. G., Etzioni, O., Knoblock, C. A., & Kuokka, D. R. (1987). Acquiring effective search control rules: Explanation-based learning in the PRODIGY system. In P. Langley (Ed.), Proceedings of the Fourth International Workshop on Machine Learning (pp. 122–133). Amsterdam: Elsevier.CrossRefGoogle Scholar
  38. Mitchell, T. (1997). Machine learning. New York: McGraw Hill.Google Scholar
  39. Murphy, K. (2012). Machine learning: A probabilistic perspective. Cambridge: MIT Press.Google Scholar
  40. Needham, A., & Baillargeon, R. (1993). Intuitions about support in 4.5-month-old infants. Cognition, 47, 121–148. doi: 10.1016/0010-0277(93)90002-D CrossRefPubMedGoogle Scholar
  41. Needham, A., & Baillargeon, R. (1997). Object segregation in 8-month-old infants. Cognition, 62, 121–149. doi: 10.1016/S0010-0277(96)00727-5 CrossRefPubMedGoogle Scholar
  42. Oates, C., Smith, J., & Mukherjee, S. (2016). Estimating causal structure using conditional DAG models. Journal of Machine Learning Research, 17, 1–23.Google Scholar
  43. Perfors, A., Tenenbaum, J., Griffiths, T., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120, 302–321. doi: 10.1016/j.cognition.2010.11.015 CrossRefPubMedGoogle Scholar
  44. Rebane, G., & Pearl, J. (1987). The recovery of causal poly-trees from statistical data. In Proceedings of the 3rd Workshop on Uncertainty in Artificial Intelligence (pp. 222–228). Arlington: AUAI Press.Google Scholar
  45. Setoh, P., Wu, D., Baillargeon, R., & Gelman, R. (2013). Young infants have biological expectations about animals. Proceedings of the National Academy of Sciences, 110, 15937–15942. doi: 10.1073/pnas.1314075110
  46. Siegler, R. S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8, 481–520. doi: 10.1016/0010-0285(76)90016-5 CrossRefGoogle Scholar
  47. Siegler, R. S., & Chen, Z. (1998). Developmental differences in rule learning: A microgenetic analysis. Cognitive Psychology, 36, 273–310. doi: 10.1006/cogp.1998.0686 CrossRefPubMedGoogle Scholar
  48. Spelke, E. S. (1994). Initial knowledge: Six suggestions. Cognition, 50, 431–445. doi: 10.1016/0010-0277(94)90039-6 CrossRefPubMedGoogle Scholar
  49. Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. (1992). Origins of knowledge. Psychological Review, 99, 605–632. doi: 10.1037/0033-295X.99.4.605 CrossRefPubMedGoogle Scholar
  50. Spelke, E. S., Phillips, A., & Woodward, A. L. (1995). Infants’ knowledge of object motion and human action. In D. Sperber, D. Premack, & A. J. Premack (Eds.), Causal cognition: A multidisciplinary debate (pp. 44–78). Oxford: Oxford University Press, Clarendon Press.Google Scholar
  51. Wang, S., & Baillargeon, R. (2006). Infants’ physical knowledge affects their change detection. Developmental Science, 9, 173–181. doi: 10.1111/j.1467-7687.2006.00477.x CrossRefPubMedPubMedCentralGoogle Scholar
  52. Wang, S., & Baillargeon, R. (2008). Can infants be “taught” to attend to a new physical variable in an event category? The case of height in covering events. Cognitive Psychology, 56, 284–326. doi: 10.1016/j.cogpsych.2007.06.003 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Wang, S., Baillargeon, R., & Paterson, S. (2005). Detecting continuity violations in infancy: A new account and new evidence from covering and tube events. Cognition, 95, 129–173. doi: 10.1016/j.cognition.2002.11.001 CrossRefPubMedPubMedCentralGoogle Scholar
  54. Wang, S., & Kohne, L. (2007). Visual experience enhances 9-month-old infants’ use of task-relevant information in an action task. Developmental Psychology, 43, 1513–1522. doi: 10.1037/0012-1649.43.6.1513 CrossRefPubMedGoogle Scholar
  55. Wang, S., Zhang, Y., & Baillargeon, R. (2016). Young infants view physically possible support events as unexpected: New evidence for rule learning. Cognition, 157, 100–105. doi: 10.1016/j.cognition.2016.08.021 CrossRefPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  1. 1.University of Illinois at Urbana-ChampaignChampaignUSA
  2. 2.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations