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Emotion Algebra reveals that sequences of facial expressions have meaning beyond their discrete constituents

Studies of emotional facial expressions reveal agreement among observes about the meaning of six to fifteen basic static expressions. Other studies focused on the temporal evolvement, within single facial expressions. Here, we argue that people infer a larger set of emotion states than previously assumed, by taking into account sequences of different facial expressions, rather than single facial expressions. Perceivers interpreted sequences of two images, derived from eight facial expressions. We employed vector representation of emotion states, adopted from the field of Natural Language Processing and performed algebraic vector computations. We found that the interpretation participants ascribed to the sequences of facial expressions could be expressed as a weighted average of the single expressions comprising them, resulting in 32 new emotion states. Additionally, observes’ agreement about the interpretations, was expressed as a multiplication of respective expressions. Our study sheds light on the significance of facial expression sequences to perception. It offers an alternative account as to how a variety of emotion states are being perceived and a computational method to predict these states.