Social species perceive emotion via extracting diagnostic features of body movements. Although extensive studies have contributed to knowledge on how the entire body is used as context for decoding bodily expression, we know little about whether specific body parts (e.g., arms and legs) transmit enough information for body understanding. In this study, we performed behavioral experiments using the Bubbles paradigm on static body images to directly explore diagnostic body parts for categorizing angry, fearful and neutral expressions. Results showed that subjects recognized emotional bodies through diagnostic features from the torso with arms. We then conducted a follow-up functional magnetic resonance imaging (fMRI) experiment on body part images to examine whether diagnostic parts modulated body-related brain activity and corresponding neural representations. We found greater activations of the extra-striate body area (EBA) in response to both anger and fear than neutral for the torso and arms. Representational similarity analysis showed that neural patterns of the EBA distinguished different bodily expressions. Furthermore, the torso with arms and whole body had higher similarities in EBA representations relative to the legs and whole body, and to the head and whole body. Taken together, these results indicate that diagnostic body parts (i.e., torso with arms) can communicate bodily expression in a detectable manner.

Recognizing the emotions of faces in a crowd is crucial for understanding overall behavior and intention as well as for smooth and friendly social interactions. However, it is unclear whether the spatial frequency of faces affects the discrimination of crowd emotion. Although high- and low-spatial-frequency information for individual faces is processed by distinct neural channels, there is a lack of evidence on how this applies to crowd faces. Here, we used functional magnetic resonance imaging (fMRI) to investigate neural representations of crowd faces at different spatial frequencies. Thirty-three participants were asked to compare whether a test face was happy or more fearful than a crowd face that varied in high, low, and broad spatial frequencies. Our findings revealed that fearful faces with low spatial frequencies were easier to recognize in terms of accuracy (78.9%) and response time (927 ms). Brain regions, such as the fusiform gyrus, located in the ventral visual stream, were preferentially activated in high spatial frequency crowds, which, however, were the most difficult to recognize behaviorally (68.9%). Finally, the right inferior frontal gyrus was found to be better activated in the broad spatial frequency crowds. Our study suggests that people are more sensitive to fearful crowd faces with low spatial frequency and that high spatial frequency does not promote crowd face recognition.

Although emotional expressions conveyed by the eye regions are processed efficiently, little is known regarding the relationship between emotional processing of isolated eye regions and temporal attention. In this study, we conducted three rapid serial visual presentation (RSVP) experiments with varying task demands (emotion discrimination, eye detection, eyes ignored) related to the first target (T1) to investigate how the perception of emotional valence in the eye region (T1: happy, neutral, fearful) impacts the identification of a second target (T2: neutral houses). Event-related potential (ERP) findings indicated that fearful stimuli reliably increased N170 amplitude regardless of the emotional relevance of task demands. The P3 component exhibited enhanced responses to happy and fearful stimuli in the emotion discrimination task and to happy eye regions in the eye detection task. Analysis of T2-related ERPs within the attentional blink period revealed that T2 houses preceded by fearful and happy stimuli elicited larger N2 and P3 amplitudes than those preceded by neutral stimuli only in the emotion discrimination task. Together, these findings indicate that attention to affective content conveyed by the eyes can not only amplify the perceptual analysis of emotional eye regions but also facilitate the processing of a subsequent target.

Face perception is a complex process that involves highly specialized procedures and mechanisms. Investigations into face perception can help us better understand how the brain processes fine-grained, multidimensional information. This research aimed to delve deeply into how different dimensions of facial information are represented in specific brain regions or through inter-regional connections via an implicit face recognition task. To capture the representation of various facial information in the brain, we employed support vector machine decoding, functional connectivity, and model-based representational similarity analysis on fMRI data, resulting in the identification of three crucial findings. Firstly, despite the implicit nature of the task, emotions were still represented in the brain, contrasting with all other facial information. Secondly, the connections between the medial amygdala and the parahippocampal gyrus were found to be essential for the representation of facial emotion in implicit tasks. Thirdly, in implicit tasks, arousal representation occurred in the parahippocampal gyrus, while valence depended on connections between the primary visual cortex and the parahippocampal gyrus. In conclusion, these findings dissociate the neural mechanisms of emotional valence and arousal, revealing the precise spatial patterns of multidimensional information processing in faces.