Shape-invariant encoding of dynamic primate facial expressions in human perception

Year:
2021
Type of Publication:
Article
Authors:
Taubert, Nick
Stettler, Michael
Siebert, R.
Spadacenta, S.
Sting, L.
Dicke, P.
Thier, P.
Giese, Martin A.
Journal:
eLife
Month:
June
BibTex:
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 models 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 these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. 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 largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.