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Emotion has been exposed as a crucial component of intelligent behaviour. Considerations in both neuroscience and psychology have identified emotion as playing a central role in various critical cognitive processes, such as attaining salience from the environment in order to support decision making, exploration-exploitation and broader adaptation. This paper provides an overview of some of the corroborative material in these fields, to then consider how emotion has been translated into machine learning. We identify emotion as being a promising endeavour for machine learning and expose Emotion-augmented Machine Learning (EML) as a frontier field in Artificial Intelligence and Affective Computing.
Strömfelt et al. (Sun,) studied this question.