Logic, Probability, and Pragmatics in Syllogistic Reasoning. (10th January 2022)

• Record Type: Journal Article
• Authors: Tessler, Michael Henry; Tenenbaum, Joshua B.; Goodman, Noah D.
• Is Part Of: Topics in cognitive science. Volume 14:Number 3(2022)
• Page Start: 574
• Page End: 601
• Publication Date: 2022-01-10
• Subjects: Pragmatics -- Reasoning -- Rational Speech Act -- Syllogisms -- Semantics; Cognitive science -- Periodicals; Cognitive Science -- Periodicals; 153.05
• DOI: 10.1111/tops.12593
• Languages: English
• ISSNs: 1756-8757

Abstract

Abstract: Syllogistic reasoning lies at the intriguing intersection of natural and formal reasoning of language and logic. Syllogisms comprise a formal system of reasoning yet make use of natural language quantifiers (e.g., all, some ) and invite natural language conclusions. The conclusions people tend to draw from syllogisms, however, deviate substantially from the purely logical system. Are principles of natural language understanding to blame? We introduce a probabilistic pragmatic perspective on syllogistic reasoning: We decompose reasoning with natural language arguments into two subproblems: language comprehension and language production. We formalize models of these processes within the Rational Speech Act framework and explore the pressures that pragmatic reasoning places on the production of conclusions. We test our models on a recent, large data set of syllogistic reasoning and find that the selection process of conclusions from syllogisms are best modeled as a pragmatic speaker who has the goal of aligning the beliefs of a naive listener with those of their own. We compare our model to previously published models that implement two alternative theories—Mental Models and Probability Heuristics—finding that our model quantitatively predicts the full distributions of responses as well as or better than previous accounts, but with far fewer parameters. Our results suggest that human syllogistic reasoning may be best understood not as a poor approximation to ideal logical reasoning, but rather as rational probabilistic inference in support of natural communication. Abstract : People often face reasoning problems with assumptions and conclusions stated in natural language, suggesting that pragmatic factors of human language use will affect reasoning. We develop a probabilistic pragmatics model for the comprehension of argument premises and production of conclusions. This model explains data from syllogistic reasoning experiments, suggesting that some features of "illogical reasoning" are in fact rational probabilistic inferences in support of natural communication.