Generics and Quantified Generalizations: Asymmetry Effects and Strategic Communicators.

Cognition

University of Massachusetts Amherst, Department of Philosophy, 150 Hicks Way, Amherst, MA 01003, United States of America. Electronic address:

Published: December 2024

Generic statements ('Tigers have stripes') are pervasive and developmentally early-emerging modes of generalization with a distinctive linguistic profile. Previous experimental work suggests that generics display a unique asymmetry between the prevalence levels required to accept them and the prevalence levels typically implied by their use. This asymmetry effect is thought to have serious social consequences: if speakers use socially problematic generics based on prevalence levels that are systematically lower than what is typically inferred by their recipients, then using generics will likely exacerbate social stereotypes and biases. This paper presents evidence against the popular hypothesis that this asymmetry effect is unique to generics. Correcting for various shortcomings of previous studies, we found a generalized asymmetry effect across generics and various kinds of explicitly quantified statements ('most', 'some', 'typically', 'usually'). In addition, to better understand the conditions under which generalized asymmetry effects may exacerbate biases, we examine whether speakers choose generalizing sentences based simply on their acceptance conditions, or are systematically sensitive to the implications likely drawn by their typical recipients. In support of the latter view, we found that, in neutral or cooperative scenarios, speakers reliably choose generalizing sentences whose implied prevalence levels closely match the actual ones. In non-cooperative scenarios, many speakers exploit asymmetry effects to further their own goals by choosing generalizing sentences that are strictly true but likely to mislead their recipients. These results refine our understanding of the source of asymmetry effects and the conditions under which they may introduce biased beliefs into social networks.

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http://dx.doi.org/10.1016/j.cognition.2024.106004DOI Listing

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