Surface features can deeply affect artificial grammar learning
- PMID: 32200204
- DOI: 10.1016/j.concog.2020.102919
Surface features can deeply affect artificial grammar learning
Abstract
Three experiments explored the extent to which surface features explain discrimination between grammatical and non-grammatical strings in artificial grammar learning (AGL). Experiment 1 replicated Knowlton and Squire's (1996) paradigm using either letter strings as in the original study, or an analogous set of color strings to further explore if learning was affected by type of stimuli. Learning arose only with letter strings, but the results were mostly due to the discrimination of non-grammatical strings containing highly salient illegal features. Experiments 2 and 3 tested a new grammar devised to control for those features. Experiment 2 showed reduced grammar learning effects, and again only for letter materials. Experiment 3 explored the effect of additional practice with letter stimuli, and found increased learning only in the spaced practice condition, though additional practice also produced more explicit knowledge. These findings call for further research on the boundary conditions of learning in AGL paradigms.
Keywords: Artificial grammar learning; Chunk learning; Implicit learning.
Copyright © 2020 Elsevier Inc. All rights reserved.
Similar articles
-
Age affects chunk-based, but not rule-based learning in artificial grammar acquisition.Neurobiol Aging. 2012 Jul;33(7):1311-7. doi: 10.1016/j.neurobiolaging.2010.10.008. Epub 2010 Nov 18. Neurobiol Aging. 2012. PMID: 21093109
-
Using dual-task methodology to dissociate automatic from nonautomatic processes involved in artificial grammar learning.J Exp Psychol Learn Mem Cogn. 2013 Sep;39(5):1491-500. doi: 10.1037/a0032974. Epub 2013 Apr 29. J Exp Psychol Learn Mem Cogn. 2013. PMID: 23627281 Clinical Trial.
-
The effect of subjective awareness measures on performance in artificial grammar learning task.Conscious Cogn. 2018 Jan;57:116-133. doi: 10.1016/j.concog.2017.11.010. Epub 2017 Dec 6. Conscious Cogn. 2018. PMID: 29220702
-
What do animals learn in artificial grammar studies?Neurosci Biobehav Rev. 2017 Oct;81(Pt B):238-246. doi: 10.1016/j.neubiorev.2016.12.021. Epub 2016 Dec 22. Neurosci Biobehav Rev. 2017. PMID: 28017840 Review.
-
Does complexity matter? Meta-analysis of learner performance in artificial grammar tasks.Front Psychol. 2014 Sep 25;5:1084. doi: 10.3389/fpsyg.2014.01084. eCollection 2014. Front Psychol. 2014. PMID: 25309495 Free PMC article. Review.
Cited by
-
Implicit learning of regularities followed by realistic body movements in virtual reality.Psychon Bull Rev. 2023 Feb;30(1):269-279. doi: 10.3758/s13423-022-02175-0. Epub 2022 Sep 9. Psychon Bull Rev. 2023. PMID: 36085234
-
Implicit and explicit learning of socio-emotional information in a dynamic interaction with a virtual avatar.Psychol Res. 2023 Jun;87(4):1057-1074. doi: 10.1007/s00426-022-01709-4. Epub 2022 Aug 29. Psychol Res. 2023. PMID: 36036291 Free PMC article.
-
Explicit Instructions Do Not Enhance Auditory Statistical Learning in Children With Developmental Language Disorder: Evidence From Event-Related Potentials.Front Psychol. 2022 Jun 30;13:905762. doi: 10.3389/fpsyg.2022.905762. eCollection 2022. Front Psychol. 2022. PMID: 35846717 Free PMC article.
-
Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults.Front Hum Neurosci. 2022 Feb 23;16:805723. doi: 10.3389/fnhum.2022.805723. eCollection 2022. Front Hum Neurosci. 2022. PMID: 35280206 Free PMC article.
-
Not All Words Are Equally Acquired: Transitional Probabilities and Instructions Affect the Electrophysiological Correlates of Statistical Learning.Front Hum Neurosci. 2020 Sep 23;14:577991. doi: 10.3389/fnhum.2020.577991. eCollection 2020. Front Hum Neurosci. 2020. PMID: 33173474 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Miscellaneous
