title: How many kinds of reasoning? Inference, probability, and natural language semantics.
abstract: The "new paradigm" unifying deductive and inductive reasoning in a Bayesian framework (Oaksford & Chater, 2007; Over, 2009) has been claimed to be falsified by results which show sharp differences between reasoning about necessity vs. plausibility (Heit & Rotello, 2010; Rips, 2001; Rotello & Heit, 2009). We provide a probabilistic model of reasoning with modal expressions such as "necessary" and "plausible" informed by recent work in formal semantics of natural language, and show that it predicts the possibility of non-linear response patterns which have been claimed to be problematic. Our model also makes a strong monotonicity prediction, while two-dimensional theories predict the possibility of reversals in argument strength depending on the modal word chosen. Predictions were tested using a novel experimental paradigm that replicates the previously-reported response patterns with a minimal manipulation, changing only one word of the stimulus between conditions. We found a spectrum of reasoning "modes" corresponding to different modal words, and strong support for our model's monotonicity prediction. This indicates that probabilistic approaches to reasoning can account in a clear and parsimonious way for data previously argued to falsify them, as well as new, more fine-grained, data. It also illustrates the importance of careful attention to the semantics of language employed in reasoning experiments.
authors.affiliation: Stanford University, Department of Linguistics, 450 Serra Mall, Building 460, Stanford, CA 94305, United States. Electronic address: danlassiter@stanford.edu.
authors.forename: Daniel
authors.initials: D
authors.lastname: Lassiter
authors.affiliation: Stanford University, Department of Psychology, 450 Serra Mall, Building 420, Stanford, CA 94305, United States. Electronic address: ngoodman@stanford.edu.
authors.forename: Noah D
authors.initials: ND
authors.lastname: Goodman
mesh_terms: D006801:Humans; D007802:Language; D008960:Models, Psychological; D011336:Probability; D011340:Problem Solving / Q000502:physiology*; D011582:Psychological Theory; D013850:Thinking / Q000502:physiology*
keywords: Deduction; Induction; Natural language semantics; Probabilistic model; Reasoning