Skip to main content Accessibility help
  • Print publication year: 2005
  • Online publication date: February 2010

18 - Natural Sampling of Stimuli in (Artificial) Grammar Learning


The capacity to learn implicitly complex structures from exemplars of this structure underlies many natural learning processes. This process is occasionally called grammar induction, whereby a grammar can be any structure or system generating exemplars. The most striking example of this process probably is natural language grammar induction: children learning (to use the rules of) the language of their caregivers, by exposure to utterances of this language. This case of grammar induction has been argued by linguists to be such a complex task that its occurrence cannot be explained without invoking a special inborn language device, containing prior information about these natural grammars. The contribution of experience, then, is to provide the learner with additional information needed to “set the parameters” of this device. This is the well-known linguistic position (Chomsky, 1977; Pinker, 1989).

The nativist view was put forward as an alternative to the empiricist position, explaining language learning as a process of imitation and conditioning on the basis of verbal stimuli (Reber, 1973). According to linguistics, the psychological position cannot fully explain the acquisition of the rules of natural grammar because the sample of exemplars to which a child is exposed during the language-acquisition period is demonstrably insufficient to master all these complex rules. This argument against the experience-based explanation of grammar acquisition is known as the “poverty of stimuli” argument (Chomsky, 1977; Haegeman, 1991; Marcus, 1993; Pinker, 1994).

Related content

Powered by UNSILO
Brown, R., & Hanlon, C. (1970). Derivational complexity and order of acquisition in child speech. In Hayes, J. (Ed.), Cognition and the development of language (pp. 11–53). New York: Wiley
Charniak, E. (1993). Statistical language learning. Cambridge. MIT Press
Chater, N., & Vitanyi, P. (2003). A simplicity principle for language learning: Re-evaluating what can be learned from positive evidence. Unpublished manuscript
Chomsky, N. (1977). Language and responsibility. New York: Pantheon Books
Gigerenzer, G. (2000). Adaptive thinking. Oxford: Oxford University Press
Gold, E. M. (1967). Language identification in the limit. Information and Control, 16, 447–474
Haegeman, L. (1991). Introduction to government and binding theory. Oxford: Blackwell Science
Hornstein, N. & Lightfoot, D. W. (1981). Explanation in linguistics: the logical problem of language acquisition. London: Longman
Johnstone, T. & Shanks, D. R. (1999). Two mechanisms in artificial grammar learning? Comment on Meulemans and van der Linden (1997). Journal of Experimental Psychology: Learning, Memory and Cognition, 25, 524–531
Knowlton, B. J., & Squire, L. R. (1994). The information acquired during artificial grammar learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 20, 79–91
Knowlton, B. J., & Squire, L. R. (1996). Artificial grammar learning depends on implicit acquisition of both abstract and exemplar specific information. Journal of Experimental Psychology: Learning, Memory and Cognition, 22, 168–181
Marcus, G. F. (1993). Negative evidence in language acquisition. Cognition, 46, 53–85
Meulemans, T., & Linden, M. (1997). associative chunk strength in artificial grammar learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 23(4), 1007–1028
Newport, E. L. (1990). Maturational constraints on language learning. Cognitive Science, 14, 11–28
Perruchet, P., & Pacteau, C. (1990). Synthetic grammar learning: Implicit rule abstraction of explicit fragmentary knowledge?Journal of Experimental Psychology: General, 119, 264–275
Pinker, S. (1989). Language acquisition. In Posner, M. I. (Ed.), Foundations of cognitive science. Cambridge, MA: MIT Press
Pinker, S. (1994). The language instinct. Harmondsworth, UK: Penguin
Poletiek, F. H. (2001). Hypothesis testing behaviour. Hove, UK: Psychology Press
Poletiek, F. H. (2002). Learning recursion in an artifical grammar learning task, Acta Psychologica, 111, 323–335
Poletiek, F. H. (2003). The influence of stimulus set size on performance in Artifcial Grammar Learning. Paper presented at the 44th annual meeting of the Psychonomic Society, Vancouver, Canada, November 6–9, 2003
Poletiek, F. H., & Wolters, G. (2004). One probabilistic measure for grammaticality and chunk associativeness in Artificial Grammar Learning. Manuscript submitted for publication
Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6, 317–327
Reber, A. S. (1973). On psycho-linguistic paradigms. Journal of psycholinguistic research, 2, 289–318
Reber, A. S. (1993). Implicit learning and tacit knowledge: An essay on the cognitive unconscious. New York: Oxford University Press
Reber, A., Kassin, S., Lewis, S., & Cantor, G. (1980). On the relationship between implicit and explicit modes in the learning of a complex rule structure. Journal of Experimental Psychology: Human learning and memory, 6, 492–502
Redington, M., & Chater, N. (1996). Transfer in artificial grammar learning: A reevaluation. Journal of Experimental Psychology: General, 125, 123–138
Redington, M., Chater, N., & Finch, S. (1998). Distributional information: A powerful cue for acquiring syntactic categories. Cognitive Science, 22, 425–469
Wason, P. C. (1968). Reasoning about a rule. Quarterly Journal of Experimental Psychology, 20, 273–281
Wexler, K., & Cullicover, P. (1980). Formal principles of language acquisition. Cambrdige, MA: MIT Press
Wolff, J. G. (1982). Language acquisition, data compression and generalization. Language and Communication, 2, 57–89
Wolff, J. G. (1988). Learning syntax and meanings through optimisation and distributional analysis. In Levy, Y., Schlesinger, I. M., & Braine, M. D. S. (Eds.), Categories and processes in language acquisition (pp. 179–215). Hillsdale, NJ: Lawrence Erlbaum