人工语法学习中习得知识的分离:基于信号检测论和结构知识的视角

doi:10.16187/j.cnki.issn1001-4918.2022.01.02

摘要/Abstract

摘要： 习得知识的意识觉知问题一直以来是内隐学习领域的研究热点和难点。以加工分离程序来分解击中率和虚报率、以结构知识中意识和无意识测量为视角，采用2（测验方式：SDTT，SKT）×2（学习程度：30试次，60试次）混合设计，试图去分离人工语法学习中习得的意识知识和无意识知识。结果发现：（1）学习程度影响到习得的无意识知识，但还未影响到习得的意识知识；（2）在分离人工语法学习的习得知识上，SDTT的敏感性高于SKT；（3）SKT夸大了元认知中的意识成分。

关键词: 人工语法学习, 内隐学习, 信号检测论, 结构知识, 加工分离程序

Abstract: The logic process of SDT is identical with the PDP (Jacoby,1991), which helps to separate conscious and unconscious knowledge to some extent. The hit means that participants consciously or unconsciously identify the grammatical letter strings as grammatical, which is approximate to the inclusion test, p_(hit)=C+UC(1-C).The false alarm means that participants unconsciously regard the ungrammatical letter strings as grammatical, which is approximate to the exclusion test, p_{(false alarm)}=UC(1-C).This procedure can avoid three drawbacks of structural knowledge measures(Ivanchei & Moroshkina, 2018), which conceals the dissociation between conscious and unconscious knowledge. This paper investigates the dissociation between conscious and unconscious knowledge, employing structural knowledge and signal detection theory.With 2 (measure methods:SDTT vs. SKT)×2(degree of learning:30 trials vs. 60 trials) mixed design and the same experimental materials to Ivanchei and Moroshkina (2018)' experiment 2, the results showed that the degree of learning affects the unconscious knowledge acquired, but does not affect the conscious knowledge acquired. Further, in separating the knowledge acquired from artificial grammar learning, the sensitivity of the SDTT is higher than that of the SKT. Additionally, the SKT exaggerated the consciousness components in metacognition.

Key words: artificial grammar learning, implicit learning, signal detection theory, structural knowledge, process dissociation procedure

中图分类号:

B844

杨海波, 董良, 周婉茹. 人工语法学习中习得知识的分离:基于信号检测论和结构知识的视角[J]. 心理发展与教育, 2022, 38(1): 10-16.

YANG Haibo, DONG Liang, ZHOU Wanru. Employing Signal Detection Theory and Structural Knowledge to Separate Knowledge Acquired from Artificial Grammar Learning[J]. Psychological Development and Education, 2022, 38(1): 10-16.

参考文献

Abrams, M., & Reber, A. S. (1988). Implicit learning:Robustness in the face of psychiatric disorders.Journal of Psycholinguistic Research, 17(5), 425-439.
Cleeremans, A., & Jimenez, L. (2002). Implicit learning and consciousness:A graded, dynamic perspective. 1-46.https://journalpsyche.org/articles/Oxc03a.pdf
Destrebecqz, A., & Cleeremans, A. (2001). Can sequence learning be implicit? New evidence with the process dissociation procedure.Psychonomic Bulletin & Review, 8(2), 343-350.
Dienes, Z., & Scott, R. (2005). Measuring unconscious knowledge:Distinguishing structural knowledge and judgment knowledge.Psychological Research, 69(5-6), 338-351.
Guo, X., Zheng, L., Zhu, L., Yang, Z., Chen, C., Zhang, L., … Dienes, Z. (2011). Acquisition of conscious and unconscious knowledge of semantic prosody. Consciousness and Cognition, 20(2), 417-425.
Haider, H., Eichler, A., & Lange, T. (2011). An old problem:How can we distinguish between conscious and unconscious knowledge acquired in an implicit learning task? Consciousness and Cognition, 20(3), 658-672.
Ivanchei, I. I., & Moroshkina, N. V. (2018). The effect of subjective awareness measures on performance in artificial grammar learning task.Consciousness and Cognition, 57, 116-133.
Jacoby, L. L. (1991). A process dissociation framework:Separating automatic from intentional uses of memory.Journal of Memory and Language, 30(5), 513-541.
Jurchis, R., Costea, A., Dienes, Z., Miclea, M., & Opre, A. (2020). Evaluative conditioning of artificial grammars:Evidence that subjectively-unconscious structures bias affective evaluations of novel stimuli. Journal of Experimental Psychology:General, 149(9), 1800-1809.
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(1), 169-181.
Mealor, A. D., & Dienes, Z. (2012). Conscious and unconscious thought in artificial grammar learning. Consciousness and Cognition, 21(2), 865-874.
Mong, H. M., McCabe, D. P., & Clegg, B. A. (2012). Evidence of automatic processing in sequence learning using process-dissociation.Advances in Cognitive Psychology, 8(2), 98-108.
Norman, E., Price, M. C., Duff, S. C., & Mentzoni, R. A. (2007). Gradations of awareness in a modified sequence learning task.Consciousness and Cognition, 16(4), 809-837.
Norman, E., Scott, R. B., Price, M. C., Jones, E., & Dienes, Z. (2019). Can unconscious structural knowledge be strategically controlled? Implicit Learning, 159-173.https://doi.org/10.4324/9781315628905-7
Nowaczyk, R. H., Shaughnessy, J. J., & Zimmerman, J. (1974). Proactive interference in short-term retention and the measurement of degree of learning:A new technique. Journal of Experimental Psychology, 103(1), 45-53.
Opitz, B., & Hofmann, J. (2015). Concurrence of rule- and similarity-based mechanisms in artificial grammar learning.Cognitive Psychology, 77, 77-99.
Reber, A. S. (1967). Implicit learning of artificial grammars.Journal of Verbal Learning and Verbal Behavior, 6(6), 855-863.
Timmermans, B., & Cleeremans, A. (2015). How can we measure awareness? An overview of current methods.In M. Overgaard(Ed.),Behavioural Methods in Consciousness Research(pp.21-46). Oxford University Press.
Underwood, B. J. (1964). Degree of learning and the measurement of retention.Journal of Verbal Learning and Verbal Behavior, 3(2), 112-129.
Yonelinas, A. P. (2001). Components of episodic memory:The contribution of recollection and familiarity.Philosophical Transactions of the Royal Society of London. Series B:Biological Sciences, 356(1413), 1363-1374.
Yonelinas, A. P., & Jacoby, L. L. (2012). The process-dissociation approach two decades later:Convergence, boundary conditions, and new directions.Memory & Cognition, 40(5), 663-680.
郭秀艳, 姜珊, 凌晓丽, 朱磊, 唐菁华. (2011). 直觉对内隐学习优势效应的特异性贡献. 心理学报, 43(9), 977-982.
郭秀艳, 朱磊, 邹庆宇. (2005). 内隐学习的主观测量标准. 心理科学, 28(5), 1192-1195.
杨海波, 刘电芝. (2016). 片段再认任务在内隐序列学习研究中的有效性检验. 心理学报, 48(3), 230-237.
杨和雄, 王良元. (1998). 敏感性分析及其模糊方法. 南京邮电学院学报, 18(1), 99-101+108.

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