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Minds and Machines

, Volume 15, Issue 3–4, pp 359–382 | Cite as

Two Apparent ‘Counterexamples’ To Marcus: A Closer Look

  • Marius Vilcu
  • Robert F. Hadley
Article

Abstract

Marcus et al.’s experiment (1999) concerning infant ability to distinguish between differing syntactic structures has prompted connectionists to strive to show that certain types of neural networks can mimic the infants’ results. In this paper we take a closer look at two such attempts: Shultz and Bale [Shultz, T.R. and Bale, A.C. (2001), Infancy 2, pp. 501–536] Altmann and Dienes [Altmann, G.T.M. and Dienes, Z. (1999) Science 248, p. 875a]. We were not only interested in how well these two models matched the infants’ results, but also whether they were genuinely learning the grammars involved in this process. After performing an extensive set of experiments, we found that, at first blush, Shultz and Bale’s model (2001) replicated the infant’s known data, but the model largely failed to learn the grammars. We also found serious problems with Altmann and Dienes’ model (1999), which fell short of matching any of the infant’s results and of learning the syntactic structure of the input patterns.

Keywords:

artificial grammars connectionism grammar learning neural networks simple recurrent networks syntactic structures 

Abbreviations

SRN

simple recurrent network

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References

  1. Altmann G.T.M., Dienes Z. (1999). Rule learning by seven-month-old infants and neural networks. Science 284:875aCrossRefGoogle Scholar
  2. Dienes Z., Altmann G.T.M., Gao S.J. (1999) Mapping across domains without feedback: A neural network model of implicit learning. Cognitive Science 23:53–82CrossRefGoogle Scholar
  3. Christiansen, M.H. and Curtin, S.L. (1999), ‘The Power of Statistical Learning: No Need for Algebraic Rules’, in Proceedings of the 21st Annual Conference of the Cognitive Science Society, pp. 114–119Google Scholar
  4. Christiansen, M.H., Conway, C.M., and Curtin, S.L. (2001), ‘A Connectionist Single-mechanism Account of Rule-like Behavior in Infancy’, in Proceedings of the 22nd Annual Conference of the Cognitive Science Society, pp. 83–88Google Scholar
  5. Elman, J.L. (1999), Generalization, rules, and neural networks: A simulation of Marcus et al. www.crl.ucsd.edu/∼ ∼elman/Papers/MVRVsim.html Google Scholar
  6. Fahlman S.E., Labiere C. (1990) The Cascade-correlation learning architecture. Advances in Neural Information Processing Systems 2:524–532Google Scholar
  7. Hadley R.F. (1995) The Explicit-Implicit Distinction. Minds and Machines 5(2):219–242CrossRefMathSciNetGoogle Scholar
  8. Marcus G.F., Vijayan S., Bandi Rao S., Vishton P.M. (1999) Rule learning by seven-month-old infants. Science 283:77–80CrossRefPubMedGoogle Scholar
  9. Marcus G.F. (1999) Response to Almann and Dienes. Science 284:875aCrossRefGoogle Scholar
  10. Marcus, G.F. (2001), The Algebraic Mind, The MIT PressGoogle Scholar
  11. Shultz, T.R. (1999), ‘Rule Learning by Habituation can be Simulated in Neural Networks’, in Proceedings of the 21st Annual Conference of the Cognitive Science Society, pp. 665–670Google Scholar
  12. Shultz T.R., Bale A.C. (2001) Neural network simulation of infant familiarization to artificial sentences: rule-like behavior without explicit rules and variables. Infancy 2:501–536CrossRefGoogle Scholar
  13. Vilcu, M., and Hadley, R.F. (2001), ‘Generalization in Simple Recurrent Networks’, Proceedings of the 23rd Annual Conference of the Cognitive Science Society, pp. 1072–1077Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

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