Research on social perception in monkeys may benefit from standardized, controllable, and ethologically valid renditions of conspecifics offered by monkey avatars. However, previous work has cautioned that monkeys, like humans, show an adverse reaction toward realistic synthetic stimuli, known as the "uncanny valley" effect. We developed an improved naturalistic rhesus monkey face avatar capable of producing facial expressions (fear grin, lip smack and threat), animated by motion capture data of real monkeys. For validation, we additionally created decreasingly naturalistic avatar variants. Eight rhesus macaques were tested on the various videos and avoided looking at less naturalistic avatar variants, but not at the most naturalistic or the most unnaturalistic avatar, indicating an uncanny valley effect for the less naturalistic avatar versions. The avoidance was deepened by motion and accompanied by physiological arousal. Only the most naturalistic avatar evoked facial expressions comparable to those toward the real monkey videos. Hence, our findings demonstrate that the uncanny valley reaction in monkeys can be overcome by a highly naturalistic avatar.
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http://dx.doi.org/10.1523/ENEURO.0524-19.2020 | DOI Listing |
Front Psychiatry
November 2024
Université Paris Cité, Laboratoire Mémoire, Cerveau et Cognition, Boulogne-Billancourt, France.
Background: Self-disorders constitute a core feature of the schizophrenia spectrum, including early stages such as first-episode psychosis (FEP). These disorders impact the minimal Self, or bodily self-consciousness, which refers to the basic, pre-reflective sense of embodied experience. The minimal Self is intrinsically linked to episodic memory, which captures specific past experiences of the Self.
View Article and Find Full Text PDFHear Res
September 2024
Sensory Physiology & Behaviour Group, Department for Neuroscience, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Carl von Ossietzky Str. 9-11, 26111 Oldenburg, Germany; Cluster of Excellence Hearing4all, Carl von Ossietzky University Oldenburg, Carl von Ossietzky Str. 9-11, 26111 Oldenburg, Germany; Ear Institute, University College London, 332 Gray's Inn Road, London, WC1 × 8EE, UK. Electronic address:
Oxf Open Neurosci
December 2023
Centre for Brain and Cognitive Development, Birkbeck, University of London, Malet St, London, WC1E 7HX, UK.
A child's social world is complex and rich, but has traditionally been assessed with conventional experiments where children are presented with repeated stimuli on a screen. These assessments are impoverished relative to the dynamics of social interactions in real life, and can be challenging to implement with preschoolers, who struggle to comply with strict lab rules. The current work meets the need to develop new platforms to assess preschoolers' social development, by presenting a unique virtual-reality set-up combined with wearable functional near-infrared spectroscopy (fNIRS).
View Article and Find Full Text PDFSci Rep
December 2023
Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands.
In conversation, recognizing social actions (similar to 'speech acts') early is important to quickly understand the speaker's intended message and to provide a fast response. Fast turns are typical for fundamental social actions like questions, since a long gap can indicate a dispreferred response. In multimodal face-to-face interaction, visual signals may contribute to this fast dynamic.
View Article and Find Full Text PDFObjective: The objective was to demonstrate anthropomorphism needs to communicate contextually useful information to increase user confidence and accurately calibrate human trust in automation.
Background: Anthropomorphism is believed to improve human-automation trust but supporting evidence remains equivocal. We test the Human-Automation Trust Expectation Model (HATEM) that predicts improvements to trust calibration and confidence in accepted advice arising from anthropomorphism will be weak unless it aids naturalistic communication of contextually useful information to facilitate prediction of automation failures.
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