Interrelation of Character-Generated Implicature and Inter-Character Sentimentality: A Comparison of of Stephenie Meyer’s ‘Twilight’ and Veronica Roth’s ‘Divergent’

Hammed Mohammadpanah, Samira Hamzehei


Inspired by the cognitive approach to characterisation and in view of relevance theory, this research attempted to outline a relevance-theoretic account of how affective attachment between fictional characters influences writers’ use of implicata through characters as part of inter-character discourse by defining cognitive processes into fictional characters as a pivotal element of implicit characterisation. Our attempt addressed the veracity of such an influence and the question whether awareness of the intensity degree of such sentimentality influences readers’ non-spontaneous interpretation of character-generated implicata and characters’ intention to actually execute relevant implicating. By adherence to defining cognitive processes into character discourse, we conducted an analysis on six samples of implicata exchanges within inter-character verbal discourse between the mutual parties of the primary affective attachment of the narrative, between the protagonist and another round character out of Stephenie Meyer’s ‘Twilight’ and Veronica Roth’s ‘Divergent’. In every instance of character-generated implication, we found decisive facilitatory influence for awareness of three levels of calculable implicated conclusions, inter-character sentiment intensity, and characters’ communicative intents on readers' achieving what Furlong terms ‘maximal relevance’ through non-spontaneous interpretation of literary texts. Additionally, tracking the progression of inter-character sentiment intensity throughout the two narratives yielded strategic drops during the gradual formation of inter-character bonds employed mainly to demonstrate a mutual sense of fastidiousness in characters’ choice of a companion in romance and also strengthening the said bonds.


Implicature, Characterisation, Relevance Theory, Non-spontaneous Interpretation, Maximal Relevance

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