Paper

Shape-invariant perceptual encoding of dynamic facial expressions across species

Spadacenta, S, Thier, P, Stettler, M, Dicke, P, Taubert, N, Siebert, R, Sting, L, Giese, Martin A

2020

Overview

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. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵† equal contributions * ↵‡ equal contributions

• Publisher: Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
• Subject: Bayesian analysis / Coevolution / Hypotheses / Information processing / Learning algorithms / Machine learning / Motor task performance / Neural networks / Neuromuscular system / Neuroscience / Sensorimotor integration / Species