The neural basis of human emotions is difficult to study, because emotions are primarily subjective and nondeterministic. To find basic principles of emotions and their underlying mechanisms, neuroscientists typically study specific emotions, using specific tasks. They use a combination of animal and human preparations, yielding various types of data, from single neuron firing patterns, to activation levels of a whole brain area. The approach, while rigorous, is slow and yields an increasingly complex body of often conflicting data. An integrative approach is needed. As described in this article, computational models of emotion have emerged as a promising tool for integration. Because these models require that all assumptions be made explicit, they offer a new language in which to express and test hypotheses and to explain and predict neural mechanisms.
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