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Investigating the Uncanny Valley for prosthetic hands

Investigating the Uncanny Valley for prosthetic hands

Poliakoff, Ellen, O’Kane, Sophie, Carefoot, Olivia, Kyberd, J ORCID: 0000-0001-9022-6748 and Gowen, Emma (2018) Investigating the Uncanny Valley for prosthetic hands. Prosthetics and Orthotics International, 42 (1). pp. 21-27. ISSN 0309-3646 (doi:https://doi.org/10.1177/039364617744083)

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Abstract

Background: In 1970, Mori hypothesised the existence of an ‘uncanny valley’, whereby stimuli falling short of being fully human are found to be creepy or eerie.

Objectives: To investigate how eerie people find different prosthetic hands and whether perceptions of eeriness can be accounted for by categorical ambiguity.

Study Design: Students participated in computerised experiments during which photographic images of hands were presented.

Methods: We compared photographs of prosthetic hands pre-selected as more (H+) or less human-like (H-), as well as mechanical and real hands. Participants rated the hands for eeriness and human-likeness, as well as performing a speeded classification (human/non-human) and location judgment (control) task.

Results: The H- prosthetic hands were rated as more eerie than the H+ prosthetic, mechanical and real hands, and this was unaffected by hand orientation. Participants were significantly slower to categorise the H+ prosthetic hands compared to the H- prosthetic and real hands, which was not due to generally slower responses to the H+ prosthetic hands (control task).

Conclusions: People find prosthetic hands to be eerie, most consistently for less human-like prosthetic hands. This effect is not driven by ambiguity about whether to categorise the prosthetic hand as human or artificial.

Item Type: Article
Uncontrolled Keywords: Prosthetic design, Prosthetics, Uncanny valley, Perception, Affect
Subjects: T Technology > TJ Mechanical engineering and machinery
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 01 Feb 2019 01:38
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT a
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/18106

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