认知论证与抑制任务
Cognitive Argumentation and the Suppression Task
摘要：
本文提出了一个新的框架，称为认知论证建模人类推理的挑战。这个框架基于这样一个假设，即人类逻辑推理本质上是一个辩证论证的过程，旨在为人类推理开发一个可计算和可实现的认知模型。为了使逻辑推理具有人类认知形式，该框架依赖于认知原则，基于认知科学中的经验和理论工作，以适当地适应AI计算论证的一般和抽象框架。认知论证的方法进行评估与伯恩的抑制任务，其目的是不仅要捕捉不同群体的人之间的抑制效果，但也要考虑到每个群体内的推理的变化。两个主要的认知原则是特别重要的捕捉人类条件推理，解释参与者的反应：（i）解释的条件内的条件作为充分和/或必要的和（ii）的推理模式，无论是预测或解释。我们认为，认知论证提供了一个连贯的和认知充分的模型，人类的条件推理，允许一个自然的明确和似是而非的结论之间的区别，表现出的重要特征，上下文敏感和可撤销的推理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
