Degree estimates as a measure of inference calculation

Bibliographic Data

ID22159529
AuthorsEszter Ronai (0000-0003-1578-0938), Ming Xiang (0000-0001-8453-2124, University of Chicago)
Year2023
Volume8
Issue1
Pages5537
Publication date2023-04-27
Open AccessYes
TypeARTICLE
VenueProceedings of the Linguistic Society of America (JOURNAL)
Journal identifiersISSN: 2473-8689 • E-ISSN: 2473-8689
PublisherLinguistic Society of America (PUBLISHER • US)
DOI10.3765/plsa.v8i1.5537
OpenAlexW4367185752
LanguageEN
References cited15

Scalar inference (SI), e.g., utterances containing the quantifier some being enriched to mean some but not all, is a central topic in semantics-pragmatics. Of recent interest in the experimental literature is the phenomenon of scalar diversity: that different lexical scales exhibit variation is how likely they are to lead to SI. However, studies of scalar diversity have almost exclusively relied on a particular experimental task: the inference task. In this paper, we argue that the inference task suffers from a number of shortcomings: namely, that it biases by providing participants with the stronger alternative and that it obscures pragmatic inferences other than SI. Instead we offer as an alternative a degree estimate task to investigate utterances containing scalar terms. We use the degree estimate task to reassess previous inference task-based findings from the literature on how two manipulations (discourse context and only) affect the likelihood of inference calculation. Our results show that the two tasks produce results that differ from each other in subtle but important ways

Cognitive psychology · Epistemology · Inference · Linguistics · Natural language processing · Phenomenon · Pragmatics · Computer Science · Language and cultural evolution · Mathematics · Neurobiology of Language and Bilingualism · Philosophy · Psychology · Syntax, Semantics, Linguistic Variation · Artificial Intelligence

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