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bioRxiv · 10.1101/2020.07.10.195941

Shape-invariant perceptual encoding of dynamic facial expressions across species

Nick Taubert·Michael Stettler·Ramona Siebert·Silvia Spadacenta·Louisa Sting·Peter Dicke·Peter Thier·Martin Giese
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Abstract

Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural-network theories predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate this hypothesis, we developed photo-realistic human and monkey heads that were animated with motion-capture data from monkeys and human. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented independently of facial shape. This result supports the co-evolution of the visual processing and motor-control of facial expressions, while it challenges popular neural-network theories of dynamic expression-recognition.

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Nick Taubert, Michael Stettler, Ramona Siebert, Silvia Spadacenta, Louisa Sting, Peter Dicke, Peter Thier, Martin Giese. 2020-07-10. Shape-invariant perceptual encoding of dynamic facial expressions across species. https://doi.org/10.1101/2020.07.10.195941

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