A computational model explaining language phenomena on Korean visual word recognition

Kinam Park, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


How do we understand the meaning of written word? Until recently, the basic mechanisms underlying visual word recognition have remained controversial. To explore the basic mechanisms underlying the human recognition processes, we propose the computational model of visual word recognition which reflects the theoretically presented linguistic processing mechanisms of Korean's visual word processing. In applying the computational model, sets of orthographic units, hidden units, context units and semantic units, were constructed. During the training phase, the weight on the connections between the units were modified using the back-propagation learning algorithm. The model using a lexical decision response time was substituted by a cross entropy values. To evaluate the model, we also conducted behavioral experiments to compare the results of the model performance with human performances. The results show that the proposed model closely simulates Korean visual word processing characteristics such as the word frequency effect, the lexical status effect, the word similarity effect and the semantic priming effect. The model suggests that the computational models should be a valuable tool that can explain the lexical information processing characteristics of the Korean visual word recognition.

Original languageEnglish
Pages (from-to)11-24
Number of pages14
JournalCognitive Systems Research
Publication statusPublished - 2014 Mar

Bibliographical note

Funding Information:
This research was supported by the Original Technology Research Program for Brain Science through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012-0006584) and was partly supported by This work was supported by the Technology Innovation Program funded by the Ministry of Knowledge Economy (MKE, Korea) [1004850, Development of Structured Plug-in Software for Smart Education].


  • Connectionist model
  • Lexical decision task
  • Visual word recognition

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence


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