@article{52021,
author = "Nick Taubert and Michael  Stettler and Siebert, R. and Spadacenta, S. and Sting, L. and Dicke, P. and Thier, P. and Martin A. Giese",
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.",
doi = "10.7 554 /eLife.61197",
journal = "eLife",
month = "June",
title = "{S}hape-invariant encoding of dynamic primate facial expressions in human perception",
url = "https://elifesciences.org/articles/61197",
year = "2021",
files = "elife-61197-v1.pdf",
}