Multidimensional similarity structure analysis
Multidimensional similarity structure analysis
An optimality principle for unsupervised learning
Advances in neural information processing systems 1
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
EMPATH: face, emotion, and gender recognition using holons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Human expression recognition from motion using a radial basis function network architecture
IEEE Transactions on Neural Networks
Categorical imperative NOT: facial affect is perceived continuously
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Classification of Faces in Man and Machine
Neural Computation
Impaired Judgments of Sadness But Not Happiness Following Bilateral Amygdala Damage
Journal of Cognitive Neuroscience
Real-time estimation of emotional experiences from facial expressions
Interacting with Computers
Recognition of facial expressions using Gabor wavelets and learning vector quantization
Engineering Applications of Artificial Intelligence
Convergence of the visual field split: Hemispheric modeling of face and object recognition
Journal of Cognitive Neuroscience
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
Recognizing facial expressions with PCA and ICA onto dimension of the emotion
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Grounding affective dimensions into posture features
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Face and facial expression recognition with an embedded system for human-robot interaction
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Multi-modal information retrieval using FINT
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
A feasibility study in using facial expressions analysis to evaluate player experiences
Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System
Disentangling factors of variation for facial expression recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Computational Intelligence and Neuroscience
Hemispheric asymmetry in perception: A differential encoding account
Journal of Cognitive Neuroscience
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There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain.