Strengthening the case for stimulus-specificity in artificial grammar learning: no evidence for abstract representations with extended exposure.

Different theories have been proposed regarding the nature of the mental representations formed as a result of implicit learning of sequential regularities. Some theories postulate abstract surface-independent representations, while other theories postulate stimulus-specific representations. This article reports three experiments investigating the development of abstract representations in artificial grammar learning (AGL), using a methodological approach developed by Conway and Christiansen (2006). In all the experiments, the number of blocks during the exposure phase was manipulated (6 blocks vs. 18 blocks of exposure to sequences). Experiments 1 and 2 investigated both visual and auditory learning where sequences were presented element-by-element. Experiment 3 investigated visual learning using a sequence-by-sequence presentation technique more commonly used in visual AGL studies. Extending previous research (Conway & Christiansen, 2006) and in support of stimulus-specific accounts, the results of the experiments showed that extended observational learning results in increased stimulus-specific knowledge rather than abstraction towards surface-independent representations.

[1]  Sébastien Pacton,et al.  An attention-based associative account of adjacent and nonadjacent dependency learning. , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[2]  Z. Dienes,et al.  Modality Independence of Implicitly Learned Grammatical Knowledge , 2004 .

[3]  Guillén Fernández,et al.  Neural correlates of artificial syntactic structure classification , 2006, NeuroImage.

[4]  Shelley Channon,et al.  Is implicit learning spared in amnesia? Rule abstraction and item familiarity in artificial grammar learning , 2002, Neuropsychologia.

[5]  J. R. Vokey,et al.  Abstract analogies and positive transfer in artificial grammar learning. , 2005, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[6]  Richard J Tunney,et al.  Recollection, fluency, and the explicit/implicit distinction in artificial grammar learning. , 2003, Journal of experimental psychology. General.

[7]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[8]  B. W. Whittlesea,et al.  Incidentally, things in general are particularly determined: An episodic-processing account of implicit learning , 1993 .

[9]  Peter M. Vishton,et al.  Rule learning by seven-month-old infants. , 1999, Science.

[10]  John R. Vokey,et al.  Abstract analogies and abstracted grammars: Comments on Reber and Mathews et al. , 1991 .

[11]  Anja Lotz,et al.  Transfer in artificial grammar learning: the role of repetition information. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[12]  Gerry Altmann,et al.  Mapping across Domains Without Feedback: A Neural Network Model of Transfer of Implicit Knowledge , 1999, Cogn. Sci..

[13]  Gary F. Marcus,et al.  Reply to Seidenberg and Elman , 1999, Trends in Cognitive Sciences.

[14]  A. Reber.  Implicit learning and tacit knowledge , 1993 .

[15]  Morten H. Christiansen,et al.  The power of statistical learning: No need for algebraic rules , 2020, Proceedings of the Twenty First Annual Conference of the Cognitive Science Society.

[16]  R. Tunney.  Sources of confidence judgments in implicit cognition , 2005, Psychonomic bulletin & review.

[17]  Z. Dienes,et al.  On the modality independence of implicitly learned grammatical knowledge , 1995 .

[18]  Joan Y. Chiao,et al.  An Event-related fMRI Study of Artificial Grammar Learning in a Balanced Chunk Strength Design , 2004, Journal of Cognitive Neuroscience.

[19]  Z. Dienes,et al.  Differences in the types of musical regularity learnt in incidental- and intentional-learning conditions , 2006, Quarterly journal of experimental psychology.

[20]  John R. Vokey,et al.  Abstract analogies and abstracted grammars: Comments on Reber (1989) and Mathews et al. (1989). , 1991 .

[21]  D. Berry.  How implicit is implicit learning , 1997 .

[22]  R. Tunney,et al.  Two modes of transfer in artificial grammar learning. , 2001, Journal of experimental psychology. Learning, memory, and cognition.

[23]  N. Chater,et al.  Transfer in artificial grammar learning : A reevaluation , 1996 .

[24]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[25]  Karl Magnus Petersson,et al.  Artificial syntactic violations activate Broca's region , 2004 .

[26]  Z. Dienes,et al.  Measuring unconscious knowledge: distinguishing structural knowledge and judgment knowledge , 2005, Psychological research.

[27]  Robert L. Goldstone,et al.  The transfer of abstract principles governing complex adaptive systems , 2003, Cognitive Psychology.

[28]  J. Saffran.  Statistical Language Learning , 2003 .

[29]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[30]  Morten H. Christiansen,et al.  PSYCHOLOGICAL SCIENCE Research Article Statistical Learning Within and Between Modalities Pitting Abstract Against Stimulus-Specific Representations , 2022 .

[31]  Axel Cleeremans,et al.  Implicit learning out of the lab: the case of orthographic regularities. , 2001, Journal of experimental psychology. General.

[32]  David R. Shanks,et al.  Abstractionist and Processing Accounts of Implicit Learning , 2001, Cognitive Psychology.

[33]  J. Elman,et al.  Networks are not ‘hidden rules’ , 1999, Trends in Cognitive Sciences.

[34]  John R. Anderson,et al.  Learning Artificial Grammars With Competitive Chunking , 1990 .

[35]  M. Linden,et al.  Associative chunk strength in artificial grammar learning. , 1997 .

[36]  Morten H. Christiansen,et al.  Modality-constrained statistical learning of tactile, visual, and auditory sequences. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[37]  J. Saffran,et al.  Dog is a dog is a dog: Infant rule learning is not specific to language , 2007, Cognition.

[38]  Barbara J Knowlton,et al.  Visual feature learning in artificial grammar classification. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[39]  Arthur S. Reber,et al.  Representing artificial grammars: Transfer across stimulus forms and modalities. , 1997 .

[40]  E. Pothos.  Theories of artificial grammar learning. , 2007, Psychological bulletin.

[41]  Z. Dienes,et al.  Implicit learning of nonlocal musical rules: implicitly learning more than chunks. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[42]  Robert L. Goldstone,et al.  How abstract is symbolic thought? , 2007, Journal of experimental psychology. Learning, memory, and cognition.

[43]  D. Shanks,et al.  Two mechanisms in implicit artificial grammar learning? Comment on Meulemans and Van der Linden (1997). , 1999 .

[44]  Herman Aguinis,et al.  Cautionary Note on Reporting Eta-Squared Values from Multifactor ANOVA Designs , 2004 .

[45]  J. Saffran.  Constraints on Statistical Language Learning , 2002 .

[46]  R. Mathews,et al.  Role of Implicit and Explicit Processes in Learning From Examples : A Synergistic Effect , 2004 .

[47]  A. Vinter,et al.  The self-organizing consciousness. , 2002, The Behavioral and brain sciences.

[48]  P. Perruchet,et al.  Implicit learning and statistical learning: one phenomenon, two approaches , 2006, Trends in Cognitive Sciences.