


Michael Tessler Stanford University, Stanford, California, USA Noah Goodman Stanford University
Syllogistic reasoning lies at the intriguing intersection of natural and formal reasoning, of language and logic. Syllogisms comprise a formal system of reasoning yet use natural language quantifiers, and invite natural language conclusions. How can we make sense of the interplay between logic and language? We develop a computationallevel theory that considers reasoning over concrete situations, constructed probabilistically by sampling. The base model can be enriched to consider the pragmatics of natural language arguments. The model predictions are compared with behavioral data from a recent metaanalysis. The flexibility of the model is then explored in a data set of syllogisms using the generalized quantifiers most and few. We conclude by relating our model to two extant theories of syllogistic reasoning – Mental Models and Probability Heuristics.
Some arguments are probably valid: Syllogistic reasoning as communication (1.1 MB)