  This paper addresses the challenge of modeling human reasoning, within a new
framework called Cognitive Argumentation. This framework rests on the
assumption that human logical reasoning is inherently a process of dialectic
argumentation and aims to develop a cognitive model for human reasoning that is
computational and implementable. To give logical reasoning a human cognitive
form the framework relies on cognitive principles, based on empirical and
theoretical work in Cognitive Science, to suitably adapt a general and abstract
framework of computational argumentation from AI. The approach of Cognitive
Argumentation is evaluated with respect to Byrne's suppression task, where the
aim is not only to capture the suppression effect between different groups of
people but also to account for the variation of reasoning within each group.
Two main cognitive principles are particularly important to capture human
conditional reasoning that explain the participants' responses: (i) the
interpretation of a condition within a conditional as sufficient and/or
necessary and (ii) the mode of reasoning either as predictive or explanatory.
We argue that Cognitive Argumentation provides a coherent and cognitively
adequate model for human conditional reasoning that allows a natural
distinction between definite and plausible conclusions, exhibiting the
important characteristics of context-sensitive and defeasible reasoning.
