DocumentCode :
2955799
Title :
Characterizing prior knowledge-attention relationship by a computational model of cognitive reading
Author :
Serrano, J. Ignacio ; Castillo, M. Dolores del ; Iglesias, Ángel ; Oliva, Jesús
Author_Institution :
Dept. of Inf., Inst. de Autom. Ind.-Consejo Super. de Investig. Cientificas(CSIC), Arganda del Rey
fYear :
2008
fDate :
1-8 June 2008
Firstpage :
881
Lastpage :
886
Abstract :
Interest and prior knowledge are supposed to influence reading comprehension and learning from natural language texts. The effects of interest have been well studied in the literature, but little effort has been made on empirically establishing the influences of prior knowledge in reading attention and engagement, and therefore in comprehension and learning. A quantitative characterization of this relationship is proposed in this paper by means of a connectionist and computational method, a model of cognitive reading which allows to configure and isolate inferential depth and memory issues, which are well-known to be strongly related to attention and engagement. Results have pointed out a clear and straight relationship between prior knowledge and the latter issues and they have shown the computational model to be suitable as experimental framework for the validation of further hypothesis related to human language processing.
Keywords :
cognitive systems; natural language processing; text analysis; cognitive reading; computational model; human language processing; knowledge-attention relationship; natural language texts; Computational modeling; Humans; Indexing; Mediation; Natural languages; Neural networks; Psychology; Vocabulary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2008.4633902
Filename :
4633902
Link To Document :
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