Abstract
Artificial grammar learning (AGL) is an experimental paradigm that has been used extensively in cognitive research for many years to study implicit learning, associative learning, and generalization on the basis of either similarity or rules. Without computer assistance, it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or nongrammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated software for selecting AGL stimuli. The software allows users to specify a grammar of interest, and to manipulate characteristics of training and test sequences, and their relationship to each other. The user therefore has direct control over stimulus features that may influence learning and generalization in AGL tasks. The software, AGL StimSelect, enables researchers to develop AGL designs that would not be feasible without automatic stimulus selection. It is implemented in MATLAB.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442–481.
Bailey, T. M., & Hahn, U. (2001). Determinants of wordlikeness: Phonotactics or lexical neighborhoods? Journal of Memory & Language, 44, 568–591.
Berry, D. C., & Dienes, Z. (1993). Implicit learning: Theoretical and empirical issues. Hove, UK.: Erlbaum.
Boucher, L., & Dienes, Z. (2003). Two ways of learning associations. Cognitive Science, 27, 807–842.
Brooks, L. R., & Vokey, R. J. (1991). Abstract analogies and abstracted grammars: Comments on Reber (1989) and Mathews et al. (1989). Journal of Experimental Psychology: General, 120, 316–323.
Cabrera, A. (1995). The “rational” number e: A functional analysis of categorization. In Proceedings of the 17th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Chomsky, N., & Miller, G. A. (1958). Finite state languages. Information & Control, 1, 91–112.
Dienes, Z., & Altmann, G. (2003). Measuring learning using an untrained control group: Comment on R. Reber and Perruchet. Quarterly Journal of Experimental Psychology, 56A, 117–123.
Dirlam, D. K. (1972). Most efficient chunk sizes. Cognitive Psychology, 3, 355–359.
Dulany, D. E., Carlson, R. A., & Dewey, G. I. (1984). A case of syntactical learning and judgment: How conscious and how abstract? Journal of Experimental Psychology: General, 113, 541–555.
Hahn, U., & Bailey, T. M. (2005). What makes words sound similar? Cognition, 97, 227–267.
Johnstone, T., & Shanks, D. R. (1999). Two mechanisms in implicit grammar learning? Comment on Meulemans and van der Linden (1997). Journal of Experimental Psychology: Leaning, Memory, & Cognition, 25, 524–531.
Knowlton, B. J., & Squire, L. R. (1994). The information acquired during artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, & 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, & Cognition, 22, 169–181.
Luce, P. A., & Pisoni, D. B. (1998). Recognizing spoken words: The neighborhood activation model. Ear & Hearing, 19, 1–36.
Meulemans, T., & van der Linden, M. (1997). Associative chunk strength in artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 1007–1028.
Meulemans, T., & van der Linden, M. (2003). Implicit learning of complex information in amnesia. Brain & Cognition, 52, 250–257.
Nosofsky, R. M. (1988). Similarity, frequency, and category representation. Journal of Experimental Psychology: Learning, Memory, & Cognition, 14, 54–65.
Perruchet, P., & Pacteau, C. (1990). Synthetic grammar learning: Implicit rule abstraction or explicit fragmentary knowledge? Journal of Experimental Psychology: General, 119, 264–275.
Perruchet, P., Vinter, A., Pacteau, C., & Gallego, J. (2002). The formation of structurally relevant units in artificial grammar learning. Quarterly Journal of Experimental Psychology, 55A, 485–503.
Poldrack, R. A., Clark, J., Paré-Blagoev, E. J., Shohamy, D., Creso Moyano, J., Myers, C., & Gluck, M. A. (2001). Interactive memory systems in the human brain. Nature, 414, 546–550.
Pothos, E. M. (2005). The rules versus similarity distinction. Behavioral & Brain Sciences, 28, 1–49.
Pothos, E. M. (2007). Theories of artificial grammar learning. Psychological Bulletin, 133, 227–244.
Pothos, E. M., & Bailey, T. M. (2000). The importance of similarity in artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26, 847–862.
Pothos, E. M., & Cox, W. M. (2002). Cognitive bias for alcohol-related information in inferential processes. Drug & Alcohol Dependence, 66, 235–241.
Pothos, E. M., & Kirk, J. (2004). Investigating learning deficits associated with dyslexia. Dyslexia, 10, 61–76.
Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning & Verbal Behavior, 6, 855–863.
Reber, A. S. (1976). Implicit learning of synthetic language. Journal of Experimental Psychology: Human Learning & Memory, 2, 88–94.
Reber, A. S. (1993). Implicit learning and tacit knowledge. New York: Oxford University Press.
Reber, A. S., & Allen, R. (1978). Analogic and abstraction strategies in synthetic grammar learning: A functional interpretation. Cognition, 6, 189–221.
Reber, P. J., & Squire, L. R. (1999). Intact learning of artificial grammars and intact category learning by patients with Parkinson’s disease. Behavioral Neuroscience, 113, 235–242.
Reber, R., & Perruchet, P. (2003). The use of control groups in artificial grammar learning. Quarterly Journal of Experimental Psychology, 56A, 97–115.
Redington, F. M., & Chater, N. (1996). Transfer in artificial grammar learning: Methodological issues and theoretical implications. Journal of Experimental Psychology: General, 125, 123–138.
Servan-Schreiber, E. (1991). The competitive chunking theory: Models of perception, learning, & memory. Unpublished doctoral dissertation. Carnegie-Mellon University.
Servan-Schreiber, E., & Anderson, J. R. (1990). Learning artificial grammars with competitive chunking. Journal of Experimental Psychology: Learning, Memory, & Cognition, 16, 592–608.
Shanks, D. R. (2005). Implicit learning. In K. Lamberts and R. Goldstone (Eds.), Handbook of cognition (pp. 202–220). London: Sage.
Sloman, S. A., & Rips, L. J. (1998). Similarity as an explanatory construct. Cognition, 65, 87–101.
Smith, E. E., Langston, C., & Nisbett, R. E. (1992). The case for rules in reasoning. Cognitive Science, 16, 1–40.
Smith, E. E., Patalano, A. L., & Jonides, J. (1998). Alternative strategies of categorization. Cognition, 65, 167–196.
Vokey, J. R., & Brooks, L. R. (1992). Salience of item knowledge in learning artificial grammars. Journal of Experimental Psychology: Learning, Memory, & Cognition, 18, 328–344.
Wasserman, E. A., & Miller, R. R. (1997). What’s elementary about associative learning? Annual Review of Psychology, 48, 573–607.
Witt, K., Nuehsman, A., & Deuschl, G. (2002). Intact artificial grammar learning in patients with cerebellar degeneration and advanced Parkinson’s disease. Neuropsychologia, 40, 1534–1540.
Author information
Authors and Affiliations
Corresponding authors
Additional information
This research was partly supported by ESRC Grant R000222655 and European Commission Grant 51652 (NEST).
Rights and permissions
About this article
Cite this article
Bailey, T.M., Pothos, E.M. AGL StimSelect: Software for automated selection of stimuli for artificial grammar learning. Behav Res 40, 164–176 (2008). https://doi.org/10.3758/BRM.40.1.164
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.3758/BRM.40.1.164