How children tell a (prosocial) lie from an (ironic) joke: The role of shared knowledge.

  • Published In: Social Development, 2023, v. 32, n. 3. P. 944 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Aguert, Marc 3 of 3

Abstract

Understanding counterfactual utterances is a major challenge for children, because of the many ways in which they can be interpreted (pretence, errors, figures of speech, lies). In the present study, 7‐year‐olds and adults determined whether counterfactual utterances were prosocial lies or irony, depending on whether the counterfactuality was known only to the speaker (unshared knowledge) or to both interlocutors (shared knowledge). When the counterfactuality was shared by the interlocutors, both the 7‐year‐olds and the adults were less likely to interpret the speaker's counterfactual utterance as an attempted lie, and more likely to conclude that the speaker was being ironic. Adults were better than children at distinguishing irony from lies, but both age groups exhibited the same response pattern, namely a bias toward lying. This bias did not prevent the adults from deciding that the speaker was being ironic when the counterfactuality was shared, whereas children responded at chance level. In children, the association between task performance and theory‐of‐mind skills was nonsignificant, with a very small effect size. We discuss the possibility that, contrary to widespread belief, distinguishing irony from lies does not necessarily involve theory of mind (ToM). [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Development. 2023/08, Vol. 32, Issue 3, p944
  • Document Type:Article
  • Subject Area:Literature and Writing
  • Publication Date:2023
  • ISSN:0961-205X
  • DOI:10.1111/sode.12659
  • Accession Number:168591555
  • Copyright Statement:Copyright of Social Development is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.