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Applying the Landscape Model to Comprehending Discourse From TV News Stories

Applying the Landscape Model to Comprehending Discourse From TV News Stories The Landscape Model of text comprehension was extended to the comprehension of audiovisual discourse from text and video TV news stories. Concepts from the story were coded for activation after each sequence, creating a matrix of activations that was reduced to a vector of the degree of total activation for each concept. In Study 1, the degree vector correlated well with participants' ratings of how much the sequence made them think of each concept. In Study 2, the degree vector, vectors based on the number of activations, and the degree of co-activation were used to predict participants' recall. The model predicted recall for the text version well, but only moderately well for the video version. The Landscape Model was modified using Dual Code Theory by coding and analyzing audio and visual information as separate components. It predicted students' recall well, indicating its robustness as a model of discourse processing. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Discourse Processes Informa Healthcare

Applying the Landscape Model to Comprehending Discourse From TV News Stories

Abstract

The Landscape Model of text comprehension was extended to the comprehension of audiovisual discourse from text and video TV news stories. Concepts from the story were coded for activation after each sequence, creating a matrix of activations that was reduced to a vector of the degree of total activation for each concept. In Study 1, the degree vector correlated well with participants' ratings of how much the sequence made them think of each concept. In Study 2, the degree vector, vectors based on the number of activations, and the degree of co-activation were used to predict participants' recall. The model predicted recall for the text version well, but only moderately well for the video version. The Landscape Model was modified using Dual Code Theory by coding and analyzing audio and visual information as separate components. It predicted students' recall well, indicating its robustness as a model of discourse processing.
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