logo

SCIENTIA SINICA Informationis, Volume 48, Issue 11: 1487-1496(2018) https://doi.org/10.1360/N112018-00166

An empirical review of language learning based on the micro-artificial grammar-learning paradigm

More info
  • ReceivedJun 22, 2018
  • AcceptedAug 29, 2018
  • PublishedNov 14, 2018

Abstract

Since the American psychologist Reber designed and used the micro-miniature artificial grammar-learning paradigm for the first time in 1967, it has already had a history of half a century. This paper mainly discusses micro-artificial grammar development of four types (classic micro-artificial grammar learning of finite state grammar; micro-artificial grammar learning of finite state grammar without semantics; micro-artificial grammar learning of non-finite state grammar with adjustable semantics; and micro-artificial grammar learning of “mini” natural language) and their further significance in the field of language learning.


Funded by

国家重点基础研究发展计划(973)(2014CB340502)

国家社科基金青年项目(16CYY021)

江苏省社科基金青年项目(15YYC003)


References

[1] Reber A S. Implicit learning of artificial grammars. J Verbal Learning Verbal Behav, 1967, 6: 855-863 CrossRef Google Scholar

[2] Christiansen M H, Conway C M, Onnis L. Similar Neural Correlates for Language and Sequential Learning: Evidence from Event-Related Brain Potentials.. Language Cognitive Processes, 2012, 27: 231-256 CrossRef PubMed Google Scholar

[3] Kepinska O, de Rover M, Caspers J. Connectivity of the hippocampus and Broca's area during acquisition of a novel grammar.. NeuroImage, 2018, 165: 1-10 CrossRef PubMed Google Scholar

[4] Mathews R C, Buss R R, Stanley W B. Role of implicit and explicit processes in learning from examples: A synergistic effect.. J Exp Psychology-Learning Memory Cognition, 1989, 15: 1083-1100 CrossRef Google Scholar

[5] Mathews R C. The forgetting algorithm: How fragmentary knowledge of exemplars can abstract knowledge.. J Exp Psychology-General, 1991, 120: 117-119 CrossRef Google Scholar

[6] Yuan Y L. The limitations of the statistically-based NLP models. Appl Linguist, 2004, 2: 99--108. Google Scholar

[7] Brooks L R, Vokey J R. Abstract analogies and abstracted grammars: comments on Reber (1989) and Mathews et al. (1989). J Exp Psychol Gen, 1991, 120: 316--323. Google Scholar

[8] Ouellette G, van Daal V. Introduction to the Special Issue. Orthographic Learning and Mental Representations in Literacy: Striving for a Better Understanding of a Complex Lead Role. Sci Studies Reading, 2017, 21: 1-4 CrossRef Google Scholar

[9] Rosas R, Ceric F, Tenorio M. ADHD children outperform normal children in an artificial grammar Implicit learning task: ERP and RT evidence.. Consciousness Cognition, 2010, 19: 341-351 CrossRef PubMed Google Scholar

[10] Zimmerer V C, Cowell P E, Varley R A. Artificial grammar learning in individuals with severe aphasia.. Neuropsychologia, 2014, 53: 25-38 CrossRef PubMed Google Scholar

[11] Christiansen M H, Louise Kelly M, Shillcock R C. Impaired artificial grammar learning in agrammatism.. Cognition, 2010, 116: 382-393 CrossRef PubMed Google Scholar

[12] van Witteloostuijn M, Boersma P, Wijnen F. Visual artificial grammar learning in dyslexia: A meta-analysis.. Res Dev Disabilities, 2017, 70: 126-137 CrossRef PubMed Google Scholar

[13] Norman E, Scott R B, Price M C. The relationship between strategic control and conscious structural knowledge in artificial grammar learning.. Consciousness Cognition, 2016, 42: 229-236 CrossRef PubMed Google Scholar

[14] Shao Z F, Lu Z. Rethinking the study of implicit learning of artificial grammar style. J East China Normal Univ (Educ Sci), 2004, 22: 47--52. Google Scholar

[15] Zhang R L. Research on graded consciousness in undergraduates' learning of artificial grammar. Dissertation for Ph.D. Degree. Suzhou: Soochow University, 2013. Google Scholar

[16] Lian S F. A research on implicit and explicit processes in learning artificial grammar. Psychol Sci, 1990, 3: 19--24. Google Scholar

[17] Guo X Y, Yang Z L. The key technique of implicit learning research--artificial grammar paradigm. Chin J Appl Psychol, 2001, 3: 45--50. Google Scholar

[18] Pan L, Yan G L. The brain mechanism of implicit learning by artificial grammar paradigm and its implications for education. Studies Psychol Behav, 2008, 2: 150--154. Google Scholar

[19] Berry D C, Broadbent D E. Explanation and Verbalization in a Computer-Assisted Search Task. Q J Exp Psychology Sect A, 1987, 39: 585-609 CrossRef Google Scholar

[20] Berry D C, Broadbent D E. Interactive tasks and the implicit-explicit distinction. British J Psychology, 1988, 79: 251-272 CrossRef Google Scholar

[21] Shanks D R, Johnstone T. Evaluating the relationship between explicit and implicit knowledge in a sequential reaction time task.. J Exp Psychology-Learning Memory Cognition, 1999, 25: 1435-1451 CrossRef Google Scholar

[22] Morgan-Short K, Deng Z Z, Brill-Schuetz K A. A VIEW OF THE NEURAL REPRESENTATION OF SECOND LANGUAGE SYNTAX THROUGH ARTIFICIAL LANGUAGE LEARNING UNDER IMPLICIT CONTEXTS OF EXPOSURE. Stud Second Lang Acquis, 2015, 37: 383-419 CrossRef Google Scholar

[23] Friederici A D, Steinhauer K, Pfeifer E. From the Cover: Brain signatures of artificial language processing: Evidence challenging the critical period hypothesis. Proc Natl Acad Sci USA, 2002, 99: 529-534 CrossRef PubMed ADS Google Scholar

[24] Silva S, Folia V, Hagoort P, et al. The P600 in implicit artificial grammar learning. Cogn Sci, 2016, 41: 137--157. Google Scholar

[25] Carpenter H S. A behavioral and electrophysiological investigation of different aptitudes for L2 grammar in learners equated for proficiency level. Dissertation for Ph.D. Degree. Washington: Georgetown University, 2008. Google Scholar

[26] Opitz B, Hofmann J. Concurrence of rule- and similarity-based mechanisms in artificial grammar learning.. Cognitive Psychology, 2015, 77: 77-99 CrossRef PubMed Google Scholar

[27] Jamieson R K, Nevzorova U, Lee G. Information theory and artificial grammar learning: inferring grammaticality from redundancy.. Psychological Res, 2016, 80: 195-211 CrossRef PubMed Google Scholar

[28] Kepinska O, de Rover M, Caspers J. Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities.. Behavioural Brain Res, 2017, 320: 333-346 CrossRef PubMed Google Scholar

[29] Kepinska O, Pereda E, Caspers J. Neural oscillatory mechanisms during novel grammar learning underlying language analytical abilities.. Brain Language, 2017, 175: 99-110 CrossRef PubMed Google Scholar

[30] Kepinska O, de Rover M, Caspers J. Connectivity of the hippocampus and Broca's area during acquisition of a novel grammar.. NeuroImage, 2018, 165: 1-10 CrossRef PubMed Google Scholar

[31] Tabullo , Sevilla Y, Pasqualetti G. Expectancy modulates a late positive ERP in an artificial grammar task.. Brain Res, 2011, 1373: 131-143 CrossRef PubMed Google Scholar

[32] Tabullo , Sevilla Y, Segura E. An ERP study of structural anomalies in native and semantic free artificial grammar: evidence for shared processing mechanisms.. Brain Res, 2013, 1527: 149-160 CrossRef PubMed Google Scholar

[33] Friederici A D. Processing local transitions versus long-distance syntactic hierarchies.. Trends Cognitive Sci, 2004, 8: 245-247 CrossRef PubMed Google Scholar

[34] Mueller J L, Hahne A, Fujii Y. Native and nonnative speakers' processing of a miniature version of Japanese as revealed by ERPs.. J Cognitive Neuroscience, 2005, 17: 1229-1244 CrossRef PubMed Google Scholar

[35] Morgan-Short K. A neurolinguistic investigation of late-learned second language knowledge: the effects of explicit and implicit conditions. Int Linguist Commu, 2008, 68: 3829. Google Scholar

[36] Morgan-Short K, Finger I, Grey S, et al. Second language processing shows increased native-like neural responses after months of no exposure. Plos One, 2012, 7: 1--8. Google Scholar

[37] Morgan-Short K, Sanz C, Steinhauer K. Second Language Acquisition of Gender Agreement in Explicit and Implicit Training Conditions: An Event-Related Potential Study.. Language Learning, 2010, 60: 154-193 CrossRef PubMed Google Scholar

[38] Morgan-Short K, Steinhauer K, Sanz C. Explicit and implicit second language training differentially affect the achievement of native-like brain activation patterns.. J Cognitive Neuroscience, 2012, 24: 933-947 CrossRef PubMed Google Scholar

[39] Hultén A, Karvonen L, Laine M. Producing speech with a newly learned morphosyntax and vocabulary: an magnetoencephalography study.. J Cognitive Neuroscience, 2014, 26: 1721-1735 CrossRef PubMed Google Scholar

[40] Kersten A W, Earles J L. Less Really Is More for Adults Learning a Miniature Artificial Language. J Memory Language, 2001, 44: 250-273 CrossRef Google Scholar

[41] Qi Z, Beach S D, Finn A S, et al. Native-language N400 and P600 predict dissociable language-learning abilities in adults. Neuropsychologia, 2016, 98: 177--191. Google Scholar

[42] Mueller J L, Girgsdies S, Friederici A D. The impact of semantic-free second-language training on ERPs during case processing.. NeuroSci Lett, 2008, 443: 77-81 CrossRef PubMed Google Scholar

[43] Bastarrika A, Davidson D J. An event related field study of rapid grammatical plasticity in adult second-language learners. Front Hum Neurosci, 2017, 11: 12. Google Scholar

[44] Mueller J L, Hirotani M, Friederici A D. ERP evidence for different strategies in the processing of case markers in native speakers and non-native learners.. BMC Neurosci, 2007, 8: 18 CrossRef PubMed Google Scholar

[45] Davidson D J, Indefrey P. An event-related potential study on changes of violation and error responses during morphosyntactic learning.. J Cognitive Neuroscience, 2009, 21: 433-446 CrossRef PubMed Google Scholar

[46] Davidson D J, Indefrey P. Plasticity of grammatical recursion in German learners of Dutch. Language Cognitive Processes, 2009, 24: 1335-1369 CrossRef Google Scholar

[47] Davidson D J, Indefrey P. Error-related activity and correlates of grammatical plasticity. Front Psychol, 2011, 2: 219. Google Scholar

[48] Weber-Fox C M, Neville H J. Maturational Constraints on Functional Specializations for Language Processing: ERP and Behavioral Evidence in Bilingual Speakers.. J Cognitive Neuroscience, 1996, 8: 231-256 CrossRef PubMed Google Scholar

[49] Hahne A. What's different in second-language processing? evidence from event-related brain potentials. J Psycholinguistic Res, 2001, 30: 251-266 CrossRef Google Scholar

[50] Wartenburger I, Heekeren H R, Abutalebi J. Early Setting of Grammatical Processing in the Bilingual Brain. Neuron, 2003, 37: 159-170 CrossRef Google Scholar

[51] Rossi S, Gugler M F, Friederici A D. The impact of proficiency on syntactic second-language processing of German and Italian: evidence from event-related potentials.. J Cognitive Neuroscience, 2006, 18: 2030-2048 CrossRef PubMed Google Scholar

[52] Petersson K M, Folia V, Hagoort P. What artificial grammar learning reveals about the neurobiology of syntax.. Brain Language, 2012, 120: 83-95 CrossRef PubMed Google Scholar

[53] Petersson K M, Hagoort P. The neurobiology of syntax: beyond string sets.. Philos Trans R Soc B-Biol Sci, 2012, 367: 1971-1983 CrossRef PubMed Google Scholar

  • 1   Table 1Four types of the micro AGL
    Items Types
    AGL of classic under finite state grammar AGL without semantics of finite state grammar AGL with controllable semantics of non-finite state grammar AGL of “mini” natural language
    Features Generate sentences according to the nodes and paths on the state transition graph. Generate sentences according to certain grammatical rules in natural language.
    Methods The finite state grammar is used to construct a string for the participants to learn, and then judge if the new string conforms to the grammar rules previously learned implicitly. Giving the artificial words some picture content or matching related scenes, and then use the artificial words to generate sentences according to the loop rules of finite-state grammar. Using some word order (SVO, SOV, etc.) and morphological change (internal inflectional form and suffix) in natural languages, rules similar to natural language are formulated. To study language learning by shaping natural language into artificial or semi-artificial micro language to construct a subset of natural language.
    Goals It is possible to study the pure grammar by eliminating the semantic factors in a language. To capture the process of new grammar acquisition from other aspects of language learning and controlling.
    Process The participants learn a series of strings, and then, based on the rules they learned, determine whether the rules involved in the new string conform to the rules they previously learned implicitly. The participants were asked to identify some artificial words and combinations through the picture learning tasks, and then extract the abstract grammar rules learned in the picture word tasks. The participants learn new sentences made up of artificial words in a meaningful context, and then they were asked to make grammatical judgments after reached a certain level of proficiency. The participants were asked to learn a small number of words or sentences in a natural language and to observe how they responded to the rules of grammar after a short period of training.
    Merits Simple sequences are easy to learn and are suitable for studying the representation and degree of consciousness processing of acquired knowledge. Withouting the interference of semantics, phonetics or pragmatics, the amount of previous language contact difference is excluded. The introduction of word meaning promotes the learning of long-distance dependence between words on grammatical string. The recursion performance based on the rules of natural language can make the newly learned rules produce the infinite sentences.
    Demerits There is no uniform stipulation on each characteristic index, the selection of symbol sequence is also random, and the nature of knowledge acquired in learning is controversial. It can only be described as the transfer probability of adjacent elements between sequences. The words cannot have meaning or be used for generating coherent discourse. The degree of dependence between artificial words is far less than that of natural language, their lexical meaning is obvious, but syntactic meaning is imperfect. The amount of target language contact used in the experiment cannot be completely guaranteed, which affects the experimental results to some extent.

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1