Denis Legros

Coherence and Truth: A Cognitive Model of Propositional Truth Attribution

Abstract The truth value assigned to a proposition is treated by philosophers, logicians, and most psychologists as an abstract construct, a theoretical object outside the cognitive system. Breaking away from this consensus, we propose to carry out a psychological investigation to analyse the objective, verifiable properties of representations categorized as true by human individuals. We shall reject the conception whereby attributing a truth value to a proposition is the result of the activation of knowledge about the truth of that proposition. We shall also exclude the conception of truth as the result of the establishment of a correspondence with the world. We propose that truth be understood as the result of a decision about the values taken on by the conditions for fulfilment of the act of referencing in a mental model. Our cognitive model of propositional truth attribution is built on the assumption that the truth value of a proposition is determined by the ability of that proposition to fit into th...

The semantic effects of consulting a textual database on rewriting.

Abstract Children 8-10-years old were asked to write a story and then to improve it using resource texts. A first group accessed the resource texts through a computer database, a second group read the texts on paper and no texts were given to a third group. Regardless of their reading abilities, the children using computers produced more propositions during rewriting and, in particular, more macrostructural propositions. Inventions inspired by the resource texts outnumbered borrowings, particularly in the computer-assisted group. Our interpretation stresses the effect of the database learning environment on the reprocessing of mental models involved in generating first drafts.

Random indexing and the episodic memory metaphor. Application to text categorization

A model of episodic memory is derived to propose algorithms of text categorization with semantic space models. Performances of two algorithms are contrasted using textual material of the text-mining context ‘DEFT09’. Results confirm that the episodic memory metaphor provides a convenient framework to propose efficient algorithm for text categorization. One algorithm has already been tested with LSA. The present paper extends these algorithms to another model of Word Vector named Random Indexing.

How Context Contributes to Metaphor Understanding

The objective of this study is to show how the contextual knowledge about the topic allows choosing one out of the many interpretations possible for attributive metaphors of the form [A (the topic) is B (the vehicle)]. We propose an experiment that shows (i) that the properties of the vehicle that are actually attributed to the topic are the ones that are specified by the context in which the topic appears; and (ii) that conventional interpretations of a metaphor do not predominate unless this context is neutral, i.e. when the attributes of the topic furnished by the context do not belong to the vehicles category. We interpret these results on the importance of the context in understanding metaphor both in terms of property matching [1, 4, 7] and in terms of categorization [2, 8]. In our approach, understanding a metaphor consists of including the topic in a category specified by the vehicle such that the topic inherits certain of the traits of this category, within a situational object category network.