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Beyond human-likeness: Socialness is more influential when attributing mental states to robots.
Jastrzab, Laura E; Chaudhury, Bishakha; Ashley, Sarah A; Koldewyn, Kami; Cross, Emily S.
Afiliación
  • Jastrzab LE; Institute for Cognitive Neuroscience, School of Human and Behavioural Science, Bangor University, Wales, UK.
  • Chaudhury B; Institute for Neuroscience and Psychology, School of Psychology, University of Glasgow, Glasgow, UK.
  • Ashley SA; Institute for Neuroscience and Psychology, School of Psychology, University of Glasgow, Glasgow, UK.
  • Koldewyn K; Institute for Cognitive Neuroscience, School of Human and Behavioural Science, Bangor University, Wales, UK.
  • Cross ES; Division of Psychiatry, Institute of Mental Health, University College London, London, UK.
iScience ; 27(6): 110070, 2024 Jun 21.
Article en En | MEDLINE | ID: mdl-38947497
ABSTRACT
We sought to replicate and expand previous work showing that the more human-like a robot appears, the more willing people are to attribute mind-like capabilities and socially engage with it. Forty-two participants played games against a human, a humanoid robot, a mechanoid robot, and a computer algorithm while undergoing functional neuroimaging. We confirmed that the more human-like the agent, the more participants attributed a mind to them. However, exploratory analyses revealed that the perceived socialness of an agent appeared to be as, if not more, important for mind attribution. Our findings suggest top-down knowledge cues may be equally or possibly more influential than bottom-up stimulus cues when exploring mind attribution in non-human agents. While further work is now required to test this hypothesis directly, these preliminary findings hold important implications for robotic design and to understand and test the flexibility of human social cognition when people engage with artificial agents.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IScience Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IScience Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos