Manipulation of the Relative Positions of Stimuli A and C in a Transitive Inference Array

1999 ◽  
Author(s):  
Janice N. Steirn ◽  
Kelli Taylor
Keyword(s):  
1994 ◽  
Author(s):  
Debra N. Vantilburg ◽  
Heidi M. Carman ◽  
F. Robert Treichler

2010 ◽  
Author(s):  
Diana B. Klimas ◽  
Crosby Wilson ◽  
Thomas J. Budroe ◽  
Matthew J. Anderson

2017 ◽  
Vol 31 (2) ◽  
pp. 200-208 ◽  
Author(s):  
Emiliano Brunamonti ◽  
Floriana Costanzo ◽  
Anna Mammì ◽  
Cristina Rufini ◽  
Diletta Veneziani ◽  
...  

2021 ◽  
Author(s):  
Hosein Aghayan Golkashani ◽  
Ruth L. F. Leong ◽  
Kian F. Wong ◽  
Michael W. L. Chee

Author(s):  
Andrew Potter

Abstract Rhetorical structure theory (RST) and relational propositions have been shown useful in analyzing texts as expressions in propositional logic. Because these expressions are systematically derived, they may be expected to model discursive reasoning as articulated in the text. If this is the case, it would follow that logical operations performed on the expressions would be reflected in the texts. In this paper the logic of relational propositions is used to demonstrate the applicability of transitive inference to discourse. Starting with a selection of RST analyses from the research literature, analyses of the logic of relational propositions are performed to identify their corresponding logical expressions and within each expression to identify the inference path implicit within the text. By eliminating intermediary relational propositions, transitivity is then used to progressively compress the expression. The resulting compressions are applied to the corresponding texts and their compressed RST analyses. The application of transitive inference to logical expressions results in abridged texts that are intuitively coherent and logically compatible with their originals. This indicates an underlying isomorphism between the inferential structure of logical expressions and discursive coherence, and it confirms that these expressions function as logical models of the text. Potential areas for application include knowledge representation, logic and argumentation, and RST validation.


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