P. N. Johnson-Laird, Ruth M. J. Byrne, Sangeet S. Khemlani
{"title":"Models of Possibilities Instead of Logic as the Basis of Human Reasoning","authors":"P. N. Johnson-Laird, Ruth M. J. Byrne, Sangeet S. Khemlani","doi":"10.1007/s11023-024-09662-4","DOIUrl":null,"url":null,"abstract":"<p>The theory of mental models and its computer implementations have led to crucial experiments showing that no standard logic—the sentential calculus and all logics that include it—can underlie human reasoning. The theory replaces the logical concept of validity (the conclusion is true in all cases in which the premises are true) with necessity (conclusions describe no more than possibilities to which the premises refer). Many inferences are both necessary and valid. But experiments show that individuals make necessary inferences that are invalid, e.g., <i>Few people ate steak or sole</i>; therefore, <i>few people ate steak</i>. Other crucial experiments show that individuals reject inferences that are not necessary but valid, e.g., <i>He had the anesthetic or felt pain, but not both</i>; therefore, <i>he had the anesthetic or felt pain, or both</i>. Nothing in logic can justify the rejection of a valid inference: a denial of its conclusion is inconsistent with its premises, and inconsistencies yield valid inferences of any conclusions whatsoever including the one denied. So inconsistencies are catastrophic in logic. In contrast, the model theory treats all inferences as defeasible (nonmonotonic), and inconsistencies have the null model, which yields only the null model in conjunction with any other premises. So inconsistences are local. Which allows truth values in natural languages to be much richer than those that occur in the semantics of standard logics; and individuals verify assertions on the basis of both facts and possibilities that did not occur.</p>","PeriodicalId":51133,"journal":{"name":"Minds and Machines","volume":"48 1","pages":""},"PeriodicalIF":4.2000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Minds and Machines","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11023-024-09662-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The theory of mental models and its computer implementations have led to crucial experiments showing that no standard logic—the sentential calculus and all logics that include it—can underlie human reasoning. The theory replaces the logical concept of validity (the conclusion is true in all cases in which the premises are true) with necessity (conclusions describe no more than possibilities to which the premises refer). Many inferences are both necessary and valid. But experiments show that individuals make necessary inferences that are invalid, e.g., Few people ate steak or sole; therefore, few people ate steak. Other crucial experiments show that individuals reject inferences that are not necessary but valid, e.g., He had the anesthetic or felt pain, but not both; therefore, he had the anesthetic or felt pain, or both. Nothing in logic can justify the rejection of a valid inference: a denial of its conclusion is inconsistent with its premises, and inconsistencies yield valid inferences of any conclusions whatsoever including the one denied. So inconsistencies are catastrophic in logic. In contrast, the model theory treats all inferences as defeasible (nonmonotonic), and inconsistencies have the null model, which yields only the null model in conjunction with any other premises. So inconsistences are local. Which allows truth values in natural languages to be much richer than those that occur in the semantics of standard logics; and individuals verify assertions on the basis of both facts and possibilities that did not occur.
期刊介绍:
Minds and Machines, affiliated with the Society for Machines and Mentality, serves as a platform for fostering critical dialogue between the AI and philosophical communities. With a focus on problems of shared interest, the journal actively encourages discussions on the philosophical aspects of computer science.
Offering a global forum, Minds and Machines provides a space to debate and explore important and contentious issues within its editorial focus. The journal presents special editions dedicated to specific topics, invites critical responses to previously published works, and features review essays addressing current problem scenarios.
By facilitating a diverse range of perspectives, Minds and Machines encourages a reevaluation of the status quo and the development of new insights. Through this collaborative approach, the journal aims to bridge the gap between AI and philosophy, fostering a tradition of critique and ensuring these fields remain connected and relevant.