Psychological Development and Education ›› 2022, Vol. 38 ›› Issue (1): 10-16.doi: 10.16187/j.cnki.issn1001-4918.2022.01.02

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Employing Signal Detection Theory and Structural Knowledge to Separate Knowledge Acquired from Artificial Grammar Learning

YANG Haibo1,2,3, DONG Liang1, ZHOU Wanru1   

  1. 1. Department of Psychology, Minnan Normal University, Zhangzhou 363000;
    2. Institute of Applied Psychology, Minnan Normal University, Zhangzhou 363000;
    3. Fujian Key Laboratory of Applied Cognition & Personality, Minnan Normal University, Zhanzhou 363000
  • Published:2022-02-17

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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

CLC Number: 

  • B844
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