Body and expression perception may be sustained by midlevel feature computations rather than by body categoryselective processes.

Humans are experts at recognizing intent and emotion from other people’s body movements; however, the underlying mechanisms are poorly understood. Here, we computed quantitative features of body posture and kinematics and acquired behavioural ratings of these feature descriptors to investigate their role in affective whole-body movement perception. Representational similarity analyses and classification regression trees were used to investigate the relation of emotional categories to both the computed features and behavioural ratings. Overall, postural rather than kinematic features discriminated better between emotional movements for the computed as well as for the behavioural features. In particular, limb angles and symmetry appeared to be the most relevant ones. This was observed independently of whether or not the time-related information was preserved in the computed features. Interestingly, the behavioural ratings showed a clearer distinction between affective movements than the computed counterparts. Finally, the perceived directionality of the movement (i.e. towards or away from the observer) was found to be critical for the recognition of fear and anger.

In the natural world, faces are not isolated objects but are rather encountered in the context of the whole body. Previous work has studied the perception of combined faces and bodies using behavioural and electrophysiological measurements, but the neural correlates of emotional face–body perception still remain unexplored. Here, we combined happy and fearful faces and bodies to investigate the influence of body expressions on the neural processing of the face, the effect of emotional ambiguity between the two and the role of the amygdala in this process. Our functional magnetic resonance imaging analyses showed that the activity in motor, prefrontal and visual areas increases when facial expressions are presented together with bodies rather than in isolation, consistent with the notion that seeing body expressions triggers both emotional and action-related processes. In contrast, psychophysiological interaction analyses revealed that amygdala modulatory activity increases after the presentation of isolated faces when compared to combined faces and bodies. Furthermore, a facial expression combined with a congruent body enhanced both cortical activity and amygdala functional connectivity when compared to an incongruent face–body compound. Finally, the results showed that emotional body postures influence the processing of facial expressions, especially when the emotion conveyed by the body implies danger.

Humans and other primate species are experts at recognizing body expressions. To understand the underlying perceptual mechanisms, we computed postural and kinematic features from affective whole-body movement videos and related them to brain processes. Using representational similarity and multivoxel pattern analyses, we showed systematic relations between computation-based body features and brain activity. Our results revealed that postural rather than kinematic features reflect the affective category of the body movements. The feature limb contraction showed a central contribution in fearful body expression perception, differentially represented in action observation, motor preparation, and affect coding regions, including the amygdala. The posterior superior temporal sulcus differentiated fearful from other affective categories using limb contraction rather than kinematics. The extrastriate body area and fusiform body area also showed greater tuning to postural features. The discovery of midlevel body feature encoding in the brain moves affective neuroscience beyond research on high-level emotion representations and provides insights in the perceptual features that possibly drive automatic emotion perception.

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