@article{TEXTUAL,
      recid = {14384},
      author = {Ronai, Eszter and Xiang, Ming},
      title = {Scalar inference calculation through the lens of degree  estimates},
      journal = {Language and Cognition},
      address = {2025-01-09},
      number = {TEXTUAL},
      abstract = {Scalar inference (SI), e.g., utterances containing some  being enriched to mean some but not all, is a central topic  in semantics and pragmatics. Of recent interest in the  experimental literature is scalar diversity: different  lexical scales differ in their likelihood of leading to SI.  Studies of scalar diversity have almost exclusively relied  on the so-called inference task. In this article, we  highlight two shortcomings of the inference task: it biases  participants by providing them with the stronger  alternative, and it obscures pragmatic inferences other  than SI. We offer as an alternative a degree estimate task  to investigate utterances containing scalar terms. We  validate the degree estimate task, i.a., by successfully  replicating a previous finding about scalar diversity: that  the distinctness of scalar terms (some versus all) is a  significant predictor of it. We then use degree estimates  to reassess previous inference task-based findings. Our  results show that biasing discourse contexts lead to lower  degree estimates (i.e., more strengthened meanings) than a  manipulation with only, which contrasts with prior  literature’s findings. The article concludes that the  inference and degree estimate tasks both have advantages:  the former offers a straightforward definition of SI  calculation, while the latter avoids explicitly mentioning  a negated stronger alternative.},
      url = {http://knowledge.uchicago.edu/record/14384},
}