Characterizing prior knowledge-attention relationship by a computational model of cognitive reading

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.

[1]  M. Dolores del Castillo,et al.  Text Representation by a Computational Model of Reading , 2006, ICONIP.

[2]  Patricia A. Alexander,et al.  Running head: READING, INTEREST, AND THE MDL READING, INTEREST, AND THE MODEL OF DOMAIN LEARNING: A DEVELOPMENTAL MODEL OF INTEREST, KNOWLEDGE, AND STRATEGY USE IN TEXT COMPREHENSION , 2004 .

[3]  Walter Kintsch,et al.  Comprehension: A Paradigm for Cognition , 1998 .

[4]  S. Tobias.  Interest, Prior Knowledge, and Learning , 1994 .

[5]  Patricia A. Alexander,et al.  Interest and Learning From Text , 1991 .

[6]  Gregory Schraw,et al.  Situational Interest: A Review of the Literature and Directions for Future Research , 2001 .

[7]  Charles A. Perfetti,et al.  Comprehending written language: a blueprint of the reader , 2000 .

[8]  Ashwin Ram,et al.  Understanding language understanding: computational models of reading , 1999 .

[9]  W. Kintsch.  The role of knowledge in discourse comprehension: a construction-integration model. , 1988, Psychological review.

[10]  R. Azevedo,et al.  Testing and refining the direct and inferential mediation model of reading comprehension , 2007 .

[11]  John R. Anderson.  A spreading activation theory of memory. , 1983 .

[12]  Beverley L. Zakaluk.  A Theoretical Overview of the Reading Process: Factors Which Influence Performance and Implications for Instruction , 1996 .

[13]  R. Gelman,et al.  Learning as a Predictor of Situational Interest. , 1998 .

[14]  M. Dolores del Castillo,et al.  Modeling Human Reading in Conceptual Networks for Text Representation and Comparison , 2007, 2007 International Joint Conference on Neural Networks.

[15]  John Hallam,et al.  IEEE International Joint Conference on Neural Networks , 2005 .

[16]  S. Hidi.  Interest, Reading, and Learning: Theoretical and Practical Considerations , 2001 .

[17]  Ulrich Schiefele,et al.  Topic interest and levels of text comprehension. , 1992 .

[18]  Ashwin Ram,et al.  A Theory of Questions and Question Asking , 1991 .

[19]  Ashwin Ram,et al.  On the intersection of story understanding and learning , 1999 .

[20]  M. Dolores del Castillo,et al.  Characterizing Individual Interest by a Computational Model of Reading , 2008 .

[21]  S. L. Benton,et al.  Knowledge, interest, and narrative writing. , 1995 .