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The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions
Using functional magnetic resonance imaging (fMRI) repetition suppression, we explored the selectivity of the human action perception system (APS), which consists of temporal, parietal and frontal areas, for the appearance and/or motion of the perceived agent. Participants watched body movements of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3324571/ https://www.ncbi.nlm.nih.gov/pubmed/21515639 http://dx.doi.org/10.1093/scan/nsr025 |
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author | Saygin, Ayse Pinar Chaminade, Thierry Ishiguro, Hiroshi Driver, Jon Frith, Chris |
author_facet | Saygin, Ayse Pinar Chaminade, Thierry Ishiguro, Hiroshi Driver, Jon Frith, Chris |
author_sort | Saygin, Ayse Pinar |
collection | PubMed |
description | Using functional magnetic resonance imaging (fMRI) repetition suppression, we explored the selectivity of the human action perception system (APS), which consists of temporal, parietal and frontal areas, for the appearance and/or motion of the perceived agent. Participants watched body movements of a human (biological appearance and movement), a robot (mechanical appearance and movement) or an android (biological appearance, mechanical movement). With the exception of extrastriate body area, which showed more suppression for human like appearance, the APS was not selective for appearance or motion per se. Instead, distinctive responses were found to the mismatch between appearance and motion: whereas suppression effects for the human and robot were similar to each other, they were stronger for the android, notably in bilateral anterior intraparietal sulcus, a key node in the APS. These results could reflect increased prediction error as the brain negotiates an agent that appears human, but does not move biologically, and help explain the ‘uncanny valley’ phenomenon. |
format | Online Article Text |
id | pubmed-3324571 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33245712012-04-12 The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions Saygin, Ayse Pinar Chaminade, Thierry Ishiguro, Hiroshi Driver, Jon Frith, Chris Soc Cogn Affect Neurosci Original Articles Using functional magnetic resonance imaging (fMRI) repetition suppression, we explored the selectivity of the human action perception system (APS), which consists of temporal, parietal and frontal areas, for the appearance and/or motion of the perceived agent. Participants watched body movements of a human (biological appearance and movement), a robot (mechanical appearance and movement) or an android (biological appearance, mechanical movement). With the exception of extrastriate body area, which showed more suppression for human like appearance, the APS was not selective for appearance or motion per se. Instead, distinctive responses were found to the mismatch between appearance and motion: whereas suppression effects for the human and robot were similar to each other, they were stronger for the android, notably in bilateral anterior intraparietal sulcus, a key node in the APS. These results could reflect increased prediction error as the brain negotiates an agent that appears human, but does not move biologically, and help explain the ‘uncanny valley’ phenomenon. Oxford University Press 2012-04 2011-04-22 /pmc/articles/PMC3324571/ /pubmed/21515639 http://dx.doi.org/10.1093/scan/nsr025 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Saygin, Ayse Pinar Chaminade, Thierry Ishiguro, Hiroshi Driver, Jon Frith, Chris The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions |
title | The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions |
title_full | The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions |
title_fullStr | The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions |
title_full_unstemmed | The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions |
title_short | The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions |
title_sort | thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3324571/ https://www.ncbi.nlm.nih.gov/pubmed/21515639 http://dx.doi.org/10.1093/scan/nsr025 |
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