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

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

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。

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;=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;=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.