Humans can easily recognize others' facial expressions. Among the brain substrates that enable this ability, considerable attention has been paid to face-selective areas; in contrast, whether motion-sensitive areas, which clearly exhibit sensitivity to facial movements, are involved in facial expression recognition remained unclear. The present functional magnetic resonance imaging (fMRI) study used multi-voxel pattern analysis (MVPA) to explore facial expression decoding in both face-selective and motion-sensitive areas. In a block design experiment, participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) in images, videos, and eyes-obscured videos. Due to the use of multiple stimulus types, the impacts of facial motion and eye-related information on facial expression decoding were also examined. It was found that motion-sensitive areas showed significant responses to emotional expressions and that dynamic expressions could be successfully decoded in both face-selective and motion-sensitive areas. Compared with static stimuli, dynamic expressions elicited consistently higher neural responses and decoding performance in all regions. A significant decrease in both activation and decoding accuracy due to the absence of eye-related information was also observed. Overall, the findings showed that emotional expressions are represented in motion-sensitive areas in addition to conventional face-selective areas, suggesting that motion-sensitive regions may also effectively contribute to facial expression recognition. The results also suggested that facial motion and eye-related information played important roles by carrying considerable expression information that could facilitate facial expression recognition. Hum Brain Mapp 38:3113-3125, 2017. © 2017 Wiley Periodicals, Inc.

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

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The term 'platform capitalism' captures a dynamic set of new work modalities that are mediated by platforms and have been brought about through advances in Information and Communication Technologies, adjustments in consumption modes and preferences, and changes in how work is conceived. Beyond work-related changes, the ascent of platform capitalism reflects wider societal and political as well as economic changes. While research on platform capitalism and its manifold manifestations abounds, there is a lack of consensus in the literature regarding its key features and characteristics. Seeking to provide conceptual clarity and to contribute to efforts of theorization, we here analyse four main facets of platform capitalism, namely, crowdsourcing, sharing economy, gig economy and platform economy. We review key definitions of each term and provide an overview of their distinctive features. This allows us to identify both similarities and differences in the framing of these four terms. We also delve into the ideologies underlying these four terms, thus providing a critique of the neophilia characterizing the discourse framing platform capitalism.

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Emotions can be perceived from both facial and bodily expressions. Our previous study has found the successful decoding of facial expressions based on the functional connectivity (FC) patterns. However, the role of the FC patterns in the recognition of bodily expressions remained unclear, and no neuroimaging studies have adequately addressed the question of whether emotions perceiving from facial and bodily expressions are processed rely upon common or different neural networks. To address this, the present study collected functional magnetic resonance imaging (fMRI) data from a block design experiment with facial and bodily expression videos as stimuli (three emotions: anger, fear, and joy), and conducted multivariate pattern classification analysis based on the estimated FC patterns. We found that in addition to the facial expressions, bodily expressions could also be successfully decoded based on the large-scale FC patterns. The emotion classification accuracies for the facial expressions were higher than that for the bodily expressions. Further contributive FC analysis showed that emotion-discriminative networks were widely distributed in both hemispheres, containing regions that ranged from primary visual areas to higher-level cognitive areas. Moreover, for a particular emotion, discriminative FCs for facial and bodily expressions were distinct. Together, our findings highlight the key role of the FC patterns in the emotion processing, indicating how large-scale FC patterns reconfigure in processing of facial and bodily expressions, and suggest the distributed neural representation for the emotion recognition. Furthermore, our results also suggest that the human brain employs separate network representations for facial and bodily expressions of the same emotions. This study provides new evidence for the network representations for emotion perception and may further our understanding of the potential mechanisms underlying body language emotion recognition.