TAILIEUCHUNG - Báo cáo khoa học: "A Cognitive Model of Coherence-Driven Story Comprehension"

Current models of story comprehension have three major deficiencies: (1) lack of experimental support for the inference processes they involve (. reliance on prediction); (2) indifference to 'kinds' of coherence (. local and global); and (3) inability to find interpretations at variable depths. I propose that comprehension is driven by the need to find a representation that reaches a 'coherence threshold'. Variable inference processes are a reflection of different thresholds, and the skepticism of an individual inference process determines how thresholds are reached. . | A Cognitive Model of Coherence-Driven story Comprehension Elliot Smith School of Computer Science University of Birmingham Edgbaston Birmingham B15 2TT. United Kingdom. email Abstract Current models of story comprehension have three major deficiencies 1 lack of experimental support for the inference processes they involve . reliance on prediction 2 indifference to kinds of coherence . local and global and 3 inability to find interpretations at variable depths. I propose that comprehension is driven by the need to find a representation that reaches a coherence threshold . Variable inference processes are a reflection of different thresholds and the skepticism of an individual inference process determines how thresholds are reached. 1 Introduction Recent research in psychology maintains that comprehension is explanation-driven Graesser et al. 1994 and guided by the need for coherence van den Broek et al. 1995 . The com-prehender s goal is construction of a more-or-less coherent representation which includes explanations for and relations between the story s eventualities. This representation is generated via inferences which enrich the representation until it reaches the threshold specified by the comprehender s coherence need van den Broek et al. 1995 . By contrast early models of comprehension emphasised its expectation-driven nature prediction of future eventualities followed by substantiation of these predictions DeJong 1979 . The inference processes described in these early models are still implemented in many contemporary systems. One problem with these models is their failure to account for experimental evidence about inferences predictive inferences are not generated at point X in the story unless strongly supported by the story up to point X Trabasso and Magliano 1996 in addition predictive inferences not immediately confirmed by the story after point X are not incorporated into the representation Murray et al. 1993 . While it is

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