Abstract

BARREYRO, Juan Pablo; INJOQUE-RICLE, Irene  and  MOLINARI MAROTTO, Carlos. Comprehension of narratives simulation through the Landscape model and the role of emotional inferences. Interdisciplinaria [online]. 2014, vol.31, n.1, pp.93-106. ISSN 1668-7027.

Text comprehension requires the construction of a coherent mental representation, integrating text information with previous knowledge. Several studies have suggested that the emotional states of characters need not be stated explicitly: readers can infer them as a consequence of the narrative situation, characters' goals, actions, and relations to other characters. This study investigated emotional inferences generation during reading using the simulation of a narrative text, carried out with the Landscape Program and assessing text sentence recognition and the relevance of the sentence to the story. This program is a connectionist model that represents comprehension as a changing landscape of activations of propositions along several reading cycles. This model proposes that, as the reader procedes through a text, propositions fluctuate in activation. That is, with each reading cycle, new propositions are activated, and activation values of current propositions change. In addition, the coactivation of propositions leads to the establishment of connections between them. Through these fluctuating activations, a memory representation of the text gradually and dynamically emerges. The peaks and valleys of this landscape represent the relative contribution of each proposition at any given point in the story, and are the base for the construction of a mental representation of the story. The aim of this work is to determine if comprehension of a narrative with emotional inferences is a better predictor of textual proposition recognition and its relevance to the story than comprehension of a narrative without emotional inference, using a program that simulates the comprehension process. In order to simulate the generation of emotional inferences, we used the Landscape Computational Model. For this purpose 30 participants, undergraduates (9 males -30%- and 21 women, with a mean age of 20.67 years, SD = 2.85), read a story and subsequently completed a proposition recognition and proposition relevance for the story protocol. Pearson correlation analysis and linear regression analysis were conducted. Two linear regression models were tested, both including propositional values of simulation with emotional inference and without emotional inferences as independent variables, one including proposition recognition as dependent variable, and the other including proposition relevance for the story as dependent variable. The Pearson correlation coefficients showed that the simulation of the story with emotional interference and the simulation of the story without emotional interference are related to proposition recognition and story relevance, although the relation between this values and the simulation based on emotional inference had better coefficients (r = .35, p < .01, and r = .38, p < .01 respectively) than the causal-referential simulation (r = .27, p < .05, and r = .28, p < .05 respectively). The linear regression analysis detected that only the simulation with emotional inference explained the variance of recognition data ( = .45, p < .01) and the variance of the relevance to the story values ( = .53, p < .01). These findings suggest that emotional inferences play an important role in the understanding of narratives texts; because they focus the reader's attention to certain important points of the story. That is, the realization of emotional inferences seems to intensify the attention that the reader devotes to the entire cycle (which includes the proposition that prompts the inference, and also those that are causally connected to it), facilitating its later recognition and relevance to the story. This intensified processing can be related to the role that characters' emotional reactions play in a narrative.

Keywords : Simulation; Landscape model; Narrative comprehension; Emotional inferences; Adults.

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