It is long hypothesized that there is a reliable, specific mapping between certain emotional states and the facial movements that express those states. This hypothesis is often tested by asking untrained participants to pose the facial movements they believe they use to express emotions during generic scenarios. Here, we test this hypothesis using, as stimuli, photographs of facial configurations posed by professional actors in response to contextually-rich scenarios. The scenarios portrayed in the photographs were rated by a convenience sample of participants for the extent to which they evoked an instance of 13 emotion categories, and actors’ facial poses were coded for their specific movements. Both unsupervised and supervised machine learning find that in these photographs, the actors portrayed emotional states with variable facial configurations; instances of only three emotion categories (fear, happiness, and surprise) were portrayed with moderate reliability and specificity. The photographs were separately rated by another sample of participants for the extent to which they portrayed an instance of the 13 emotion categories; they were rated when presented alone and when presented with their associated scenarios, revealing that emotion inferences by participants also vary in a context-sensitive manner. Together, these findings suggest that facial movements and perceptions of emotion vary by situation and transcend stereotypes of emotional expressions. Future research may build on these findings by incorporating dynamic stimuli rather than photographs and studying a broader range of cultural contexts.

In this article, we suggest that motivation serves to anticipate the energy of the body and meet those needs before they arise, called allostasis. We describe motivation as the output of energy computations that include estimates about future energy/metabolic needs and the value of effort required for potential behaviors (i.e., whether the cost of effort is worthwhile). We bring neuroscience evidence to bear to support this hypothesis. We outline a system of brain networks that have been shown to be important for motivation, and focus in on one hub in this network, the anterior mid-cingulate cortex (aMCC), and discuss its importance for establishing motivation in the service of allostasis. We present evidence that the aMCC, positioned at the intersection of multiple brain networks, is wired to integrate signals relating to allostasis with its sensory consequences, termed interoception, as well as with cognitive control processes, sensory and motor functions. This integration guides the nervous system towards the optimal effort required to achieve a desired goal. Across a variety of task domains, we discuss the role of aMCC in motivation, including a) processing of the value of prior and expected rewards, b) assessment of energetic costs in the brain and the body, c) selectively learning and encoding prediction errors (unexpected changes) that are relevant for allostasis, d) computations for monitoring of internal states of the body and e) modulating the internal state of the body to prepare for action. Finally, we discuss the link between individual differences in aMCC processing and variation in two extreme ends of the range of motivational states, tenacity and apathy.

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