Edited by Barbara H. Partee, University of Massachusetts, Amherst, MA, and approved March 18, 2019 (received for review December 10, 2018)
Linguistic meaning encompasses a rich typology of inferences, characterized by distinct patterns of interaction with logical expressions. For example, “Robin has continued to smoke” triggers the presuppositional inference that Robin smoked before, characterized by the preservation of the inference under negation in “Robin hasn’t continued to smoke.” We show experimentally that four main inference types can be robustly replicated with iconic gestures and visual animations. These nonlinguistic objects thus display the same type of logical behavior as spoken words. Because the gestures and animations were novel to the participants, the results suggest that people may productively divide new informational content among the components of the inferential typology using general algorithms that apply to linguistic and nonlinguistic objects alike.
Contemporary semantics has uncovered a sophisticated typology of linguistic inferences, characterized by their conversational status and their behavior in complex sentences. This typology is usually thought to be specific to language and in part lexically encoded in the meanings of words. We argue that it is neither. Using a method involving “composite” utterances that include normal words alongside novel nonlinguistic iconic representations (gestures and animations), we observe successful “one-shot learning” of linguistic meanings, with four of the main inference types (implicatures, presuppositions, supplements, homogeneity) replicated with gestures and animations. The results suggest a deeper cognitive source for the inferential typology than usually thought: Domain-general cognitive algorithms productively divide both linguistic and nonlinguistic information along familiar parts of the linguistic typology.
↵2P.S. and E.C. contributed equally to this work.
Author contributions: L.T., P.S., and E.C. designed research; L.T. performed research; L.T. analyzed data; and L.T., P.S., and E.C. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: The experimental materials, instructions to participants, data, and R scripts for the analyses are available at https://osf.io/q9zyf.
Published under the PNAS license.
