Human visual perception is efficient, flexible and context-sensitive. The Bayesian brain view explains this with probabilistic perceptual inference integrating prior experience and knowledge through top-down influences. Advances in machine learning, such as Artificial Neural Networks (ANNs), have enabled considerable progress in computer vision. Unlike humans, these networks do not yet adaptively draw on meaningful and task-relevant contextual cues and prior knowledge. We propose ideas to better align human and computer vision, applied to facial expression recognition. We review evidence of knowledge-augmented and context-sensitive face perception in humans and approaches trying to leverage such sources of information in computer vision. We discuss how both fields can establish an epistemic loop: Redesigning synthetic systems with inspiration from the Bayesian brain-framework could make networks more flexible and useful for human-machine interaction. In turn, employing ANNs as scientific tools will widen the scope of empirical research into human knowledge-augmented perception.
Keywords: Bayesian brain; Computer vision; Deep learning; Facial expression perception; Top-down modulation.
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