Top

Gepubliceerd in:

01-09-2009 | Original Article

Connectionist models of artificial grammar learning: what type of knowledge is acquired?

Auteurs: Annette Kinder, Anja Lotz

Gepubliceerd in: Psychological Research | Uitgave 5/2009

Abstract

Two experiments are presented that test the predictions of two associative learning models of Artificial Grammar Learning. The two models are the simple recurrent network (SRN) and the competitive chunking (CC) model. The two experiments investigate acquisition of different types of knowledge in this task: knowledge of frequency and novelty of stimulus fragments (Experiment 1) and knowledge of letter positions, of small fragments, and of large fragments up to entire strings (Experiment 2). The results show that participants acquired all types of knowledge. Simulation studies demonstrate that the CC model explains the acquisition of all types of fragment knowledge but fails to account for the acquisition of positional knowledge. The SRN model, by contrast, accounts for the entire pattern of results found in the two experiments.

vorige artikel Planning paths to multiple targets: memory involvement and planning heuristics in spatial problem solving

volgende artikel Intermodal event files: integrating features across vision, audition, taction, and action

Alleen toegankelijk voor geautoriseerde gebruikers

There are a few differences between the version of the CC model desribed by Boucher and Dienes and the original model proposed by Servan-Schreiber and Anderson (1990). These are documented in detail by the former authors and do not seem to change the model’s predictions in any fundamental way.

It is common practice to use a higher number of training set repetitions in simulations of connectionist models than in the actual experiments. This is done for technical reasons in order to minimize catastrophic interference.

Note that adding an explicit marker for beginnings and endings in the CCM would make no difference because positions are already coded explicitly. Thus, coding the initial bigram JP (which is in fact is coded as “JP on position 1/2”) as AJP would not change anything in the network.

Berthouze, L., & Tijsseling, A. (2006). A neural model for context-dependent sequence learning. Neural Processing Letters, 23, 27–45.CrossRef

Boucher, L., & Dienes, Z. (2003). Two ways of learning associations. Cognitive Science, 27, 807–842.CrossRef

Cleeremans, A. (1993). Mechanisms of implicit learning: connectionist models of sequence processing. Cambridge, MA: MIT Press.

Cleeremans, A., & McClelland, J. L. (1991). Learning the structure of event sequences. Journal of Experimental Psychology: General, 120, 235–253.CrossRef

Dienes, Z. (1993). Computational models of implicit learning. In D. C. Berry & Z. Dienes (Eds.), Implicit learning: Theoretical and empirical issues (pp. 81–112). Hove, UK: Lawrence Erlbaum Associates.

Dienes, Z., Altmann, G. T. M., & Gao, S. (1999). Mapping across domains without feedback: A neural network model of transfer of implicit knowledge. Cognitive Science, 23, 53–82.CrossRef

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179–211.CrossRef

Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge, MA: The MIT Press.

Johnstone, T., & Shanks, D. R. (2001). Abstractionist and processing accounts of implicit learning. Cognitive Psychology, 42, 61–112.PubMedCrossRef

Kinder, A. (2000). The knowledge acquired during artificial grammar learning: Testing the predictions of two connectionist models. Psychological Research, 63, 95–105.PubMedCrossRef

Kinder, A. (2008). Models of artificial grammar learning: The case of overlapping chunks (Submitted).

Kinder, A., & Assmann, A. (2000). Learning artificial grammars: No evidence for the acquisition of rules. Memory & Cognition, 28, 1321–1332.

Kinder, A., & Shanks, D. R. (2001). Amnesia and the declarative/nondeclarative distinction: A recurrent network model of classification, recognition, and repetition priming. Journal of Cognitive Neuroscience, 13, 648–669.PubMedCrossRef

Kinder, A., Shanks, D. R., Cock, J., & Tunney, R. J. (2003). Recollection, Fluency, and the Explicit/Implicit Distinction in Artificial Grammar Learning. Journal of Experimental Psychology: General, 132(4), 551–565.CrossRef

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, 169–181.PubMedCrossRef

McClelland, J. L., & Rumelhart, D. E. (1988). Explorations in parallel distributed processing: A handbook of models, programs, and exercises. Cambridge, MA: MIT Press.

Meulemans, T., & Van der Linden, M. (1997). Associative chunk strength in artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 1007–1028.CrossRef

Perruchet, P., & Pacteau, C. (1990). Synthetic grammar learning: Implicit rule abstraction or explicit fragmentary knowledge? Journal of Experimental Psychology: General, 119, 264–275.CrossRef

Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: Two approaches, one phenomenon. Trends in Cognitive Sciences, 10, 233–238.PubMedCrossRef

Perruchet, P., & Vinter, A. (1998). PARSER: A model for word segmentation. Journal of Memory and Language, 39, 246–263.CrossRef

Pothos, E. M. (2005). The rules versus similarity distinction. Behavioral & Brain Sciences, 28, 1–49.

Reber, A. S. (1969). Transfer of syntactic structure in synthetic languages. Journal of Experimental Psychology, 81, 115–119.CrossRef

Reber, R., & Perruchet, P. (2003). The use of control groups in artificial grammar learning. Quarterly Journal of Experimental Psychology, A56, 97–115.

Redington, M., & Chater, N. (1996). Transfer in artificial grammar learning: A reevaluation. Experimental Psychology: General, 125, 123–138.CrossRef

Servan-Schreiber, E., & Anderson, J. R. (1990). Learning artificial grammars with competitive chunking. Journal of Experimental Psychology: Learning, Memory and Cognition, 16, 592–608.CrossRef

Shanks, D. R., Johnstone, T., & Staggs, L. (1997). Abstraction processes in artificial grammar learning. Quarterly Journal of Experimental Psychology, 50(A), 216–252.CrossRef

Vokey, J. R., & Brooks, L. R. (1992). Salience of item knowledge in artificial grammar learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 18, 328–344.CrossRef

Titel: Connectionist models of artificial grammar learning: what type of knowledge is acquired?
Auteurs: Annette Kinder
Anja Lotz
Publicatiedatum: 01-09-2009
Uitgeverij: Springer-Verlag
Gepubliceerd in: Psychological Research / Uitgave 5/2009
Print ISSN: 0340-0727
Elektronisch ISSN: 1430-2772
DOI: https://doi.org/10.1007/s00426-008-0177-z

Bohn Stafleu van Loghum

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Log in om toegang te krijgen

Andere artikelen Uitgave 5/2009

Strategic capacity sharing between two tasks: evidence from tasks with the same and with different task sets

The “Not Letting Go” phenomenon: accuracy instructions can impair behavioral and metacognitive effects of implicit learning processes

Do top and bottom contribute to object perception more than left and right?

Representations underlying skill in the discrete sequence production task: effect of hand used and hand position

Planning paths to multiple targets: memory involvement and planning heuristics in spatial problem solving

Intermodal event files: integrating features across vision, audition, taction, and action