Perceptual and Neural Recognition of Realistic, Dynamic, Spontaneous Facial Emotional Expressions: The 4D Space-Time Perspective

This thesis examined how progress in understanding emotional expression recognition could be improved through perceptually-realistic, dynamic, spontaneous facial expressions modelled as 4D computer human stimuli. The methodology of this thesis explored how complex facial expressions are recognised by the human visual system. The findings support emotion-specific preferences in facial recognition, while providing clarification on how the visual components of a 3D expression model are decoded through dynamic, localised facial musculature or through facial configuration. Implementing machine-learning with EEG-based neural activations, has explored how emotional expressions are decoded in higher cortical systems as perceptually-realistic expressions naturally unfold over space and time.