The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions

By Ayse Pinar Saygin, Thierry Chaminade, Hiroshi Ishiguro, Jon Driver, ChrisD . Frith. 2011

450 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Social Psychology

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Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, neutral): “Watching a human-like figure make robot-like, mechanical movements elicits more activity than either a human-like figure making human-like movements or a robot making mechanical movements (Saygin et al., 2012).”
Measurement of Trust in Automation: A Narrative Review and Reference Guide (2021, background): “…et al., 2020), and understanding differences between humans and machines that have human-like properties (Krach et al., 2008; Kircher et al., 2009; Saygin et al., 2012; Wang and Quadflieg, 2015; Hoenen et al., 2016; Özdem et al., 2017; Rosenthal-von der Pütten et al., 2019; Moreau et al., 2020).”
The role of prediction in social neuroscience (2012, methodology): “A recent study used an fMRI repetition suppression paradigm to measure activity in the action observation network while watching a robot, an android, and a human move (Saygin et al., 2012).”
Anthropomorphism: Opportunities and Challenges in Human–Robot Interaction (2014, background): “It has been suggested that neurological changes are responsible for the uncanny valley phenomenon [124].”
Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry (2020, background): “Participants in the Opacity treatment, who were thus naïve about the origin of the poem, additionally engaged in an incentivized version of the Turing Test (Saygin et al., 2012; Turing, 1950).”