Degree estimates as a measure of inference calculation


  • Eszter Ronai Northwestern University
  • Ming Xiang The University of Chicago



scalar inference, scalar diversity, inference task, Question Under Discussion, focus semantics


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.




How to Cite

Ronai, Eszter, and Ming Xiang. 2023. “Degree Estimates As a Measure of Inference Calculation”. Proceedings of the Linguistic Society of America 8 (1): 5537.