Cognition & Communication Research Centre, Division of Psychology, Northumbria UniversitySchool of Computer Science and Informatics, University College Dublin.
Cogn Sci. 2006 Jan 2;30(1):95-120. doi: 10.1207/s15516709cog0000_53.
Plausibility has been implicated as playing a critical role in many cognitive phenomena from comprehension to problem solving. Yet, across cognitive science, plausibility is usually treated as an operationalized variable or metric rather than being explained or studied in itself. This article describes a new cognitive model of plausibility, the Plausibility Analysis Model (PAM), which is aimed at modeling human plausibility judgment. This model uses commonsense knowledge of concept-coherence to determine the degree of plausibility of a target scenario. In essence, a highly plausible scenario is one that fits prior knowledge well: with many different sources of corroboration, without complexity of explanation, and with minimal conjecture. A detailed simulation of empirical plausibility findings is reported, which shows a close correspondence between the model and human judgments. In addition, a sensitivity analysis demonstrates that PAM is robust in its operations.
从理解到解决问题，许多认知现象都涉及到可能性的作用。然而，在整个认知科学领域，可能性通常被视为一种可操作的变量或度量，而不是本身被解释或研究。本文描述了一种新的可能性认知模型，即可能性分析模型（PAM），旨在对人类可能性判断进行建模。该模型使用常识性的概念一致性知识来确定目标场景的可能性程度。从本质上讲，一个高度可能的场景是一个非常符合先验知识的场景：有许多不同的证实来源，没有复杂的解释，并且很少有猜测。报告了对经验可能性发现的详细模拟，结果表明模型与人类判断之间存在密切对应。此外，敏感性分析表明，PAM 在其操作中具有鲁棒性。
