Your browser doesn't support javascript.
loading
Attribution of undesirable character traits, rather than trait-based dehumanization, predicts punishment decisions.
Brennan, Robert A; Enock, Florence E; Over, Harriet.
Afiliação
  • Brennan RA; Department of Psychology, University of York, York YO10 5DD, UK.
  • Enock FE; Department of Psychology, University of York, York YO10 5DD, UK.
  • Over H; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK.
R Soc Open Sci ; 11(7): 240087, 2024 Jul.
Article em En | MEDLINE | ID: mdl-39021773
ABSTRACT
Previous work has reported that the extent to which participants dehumanized criminals by denying them uniquely human character traits such as refinement, rationality and morality predicted the severity of the punishment endorsed for them. We revisit this influential finding across six highly powered and pre-registered studies. First, we conceptually replicate the effect reported in previous work, demonstrating that our method is sensitive to detecting relationships between trait-based dehumanization and punishment should they occur. We then investigate whether the apparent relationship between trait-based dehumanization and punishment is driven by the desirability of the traits incorporated into the stimulus set, their perceived humanness, or both. To do this, we asked participants to rate the extent to which criminals possessed uniquely human traits that were either socially desirable (e.g. cultured and civilized) or socially undesirable (e.g. arrogant and bitter). Correlational and experimental evidence converge on the conclusion that apparent evidence for the relationship between trait-based dehumanization and punishment is better explained by the extent to which participants attribute socially desirable attributes to criminals rather than the extent to which they attribute uniquely human attributes. These studies cast doubt on the hypothesized causal relationship between trait-based dehumanization and harm, at least in this context.
Palavras-chave

Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: R Soc Open Sci Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: R Soc Open Sci Ano de publicação: 2024 Tipo de documento: Article