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Semantic Activation Mechanism Of Chinese Lexical Neighborhood Size Effect

Posted on:2023-06-03Degree:MasterType:Thesis
Country:ChinaCandidate:Y J ZhangFull Text:PDF
GTID:2545306776957369Subject:Psychology
Abstract/Summary:PDF Full Text Request
In the process of processing lexical information,readers will use two aspects of information: one is the bottom-up input information from the target word(such as neighborhood size),and the other is the top-down high-level information from long-term memory(such as contextual predictability).Both aspects of information are very necessary for individuals to complete lexical access.The research on the role of lexical neighborhood size in Chinese lexical recognition has reached a relatively consistent conclusion,which is manifested in the neighborhood size promotion effect,but the promotion mechanism of the neighborhood size effect is still unclear.Some researchers believe that Chinese,as an ideograph,has a strong semantic association between neighbors.The neighborhood size effect in Chinese is mainly caused by the promotion of semantic activation associated with neighbors.A semantic activation model for Chinese word recognition is proposed,but there is little empirical evidence to support this model.In the present two studies,target words with different neighborhood sizes are embedded in sentences,and eye movement technology and ERPs technology are used to investigate the neighborhood size effect.By observing the performance of neighborhood size on different eye movement indicators,the stage of neighborhood size effect in lexical recognition is determined from the time course,The contextual predictive variable is included to investigate whether neighborhood size and the variable interact to affect lexical recognition and the stage of interaction,to determine the promotion mechanism of neighborhood size effect.In Study 1,the eye-tracking technique was used to investigate the action stage of the Chinese lexical neighborhood size effect and its promoting mechanism.The experimental design was 2(lexical neighborhood size: large and small)Ă— 2(context predictability: high predictability and low predictability).The neighborhood size and context predictability were incorporated into the Linear Mixed Model for analysis to verify their role in the process of lexical recognition.The results showed that:(1)lexical neighborhood size and contextual predictability significantly affect lexical skipping rate;(2)There are significant contextual predictive main effects on all eye movement indicators;(3)The main effects of word neighborhood size and context predictability on the first round fixation time and skipping rate are significant,but there is no interaction.The main effects on the total fixation time are significant,and there is significant interaction.In Study 2,ERPs technology was used to further explore the neurophysiological mechanism of the Chinese lexical neighborhood size effect.The experimental design was the same as study 1 to observe whether there were significant differences in N400 components induced by different neighborhood sizes,especially under low context predictive conditions,to determine the neural mechanism of the neighborhood size effect.The results showed that:(1)the main effect of neighborhood size was not significant,and there was no significant difference in N400 amplitude induced by neighborhood size;(2)Neighborhood size and context predictability interact significantly on the N400 component.The interaction between them occurs in the semantic extraction and integration stage in the late stage of lexical recognition.Conclusion:(1)The promoting effect mechanism of neighborhood size is the promotion of semantic activation between neighbors;(2)Orthographic and phonetic information processing will inhibit the recognition of target words,but this inhibition is less than the promotion of semantic activation;(3)Neighborhood size and context predictability independently affect the early stage of lexical recognition and interactively affect the late stage of lexical.
Keywords/Search Tags:neighborhood size, Context predictability, E-Z reader model, Eye tracking, N400
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