The Neuroscientist Comments

emotions and their facial expression play a fundamental role in our daily lives. Humans spend a great amount of time witnessing the emotions of their counterparts, interpreting the meaning of these signals, and determining how to respond to and deal with the complex emotional experiences. There has been a great deal of research on emotional expression, particularly on those expressions that are most immediate and automatic and, as such, often unknown to the subject who displays them. In his 1872 book The Expression of the Emotions in Man and Animals, Charles Darwin observed the striking similarity between the emotional expressions of many mammals and humans; he thus postulated both an evolutionary explanation of the similarity and an anthropological thesis that facial expressions of emotion, such as those of anger, surprise, and fear, are universal in human beings. In the 1960s the American psychologist Paul ekman set out to disprove Darwin’s anthropological thesis but found, to his initial consternation, that it was confirmed by mounting cross-cultural evidence (ekman and Friesen, 1971). ekman later developed the Facial Action Coding System (FACS, 1978), a system to taxonomize human facial movements by their appearance on the face, based on a system originally developed by a Swedish anatomist named Carl-Herman Hjortsjö. In his seminal works, ekman also studied the processing of both posed and spontaneous facial expressions in left and right hemisphere brain damaged patients (Mammucari and others., 1988; Caltagirone and others, 1989). Moreover, there is considerable evidence that in humans monkeys, and chimpanzees, emotions are expressed more intensely in the left hemiface. Because most facial muscles, particularly in the lower part, are innervated by the contralateral hemisphere, this finding has been interpreted as support for the hypothesis of right hemispheric dominance for emotion processing. Along such line of reasoning, Indersmitten and Gur (Indersmitten and Gur, 2003), using the experimental paradigm of chimeric faces, found that emotions are expressed more intensely in the left hemiface for all emotions and conditions, with the exception of evoked anger, which was expressed more intensely in the right hemiface. Beyond all, the universality of facial expressions of emotion has remained one of the longest standing debates in the biological and social sciences. Briefly stated, the universality hypothesis claims that all humans communicate six basic internal emotional states (happiness, surprise, fear, disgust, anger, and sadness) using the same facial movements by virtue of their biological and evolutionary origins. However, divergent data exist. For instance, using a unique computer graphics platform that combines generative grammars with visual perception, Jack and coworkers accessed the mind’s eye of 30 Western and eastern culture individuals and reconstructed their mental representations of the six basic facial expressions of emotion (Jack and others, 2012). They found that whereas Westerners represent each of the six basic emotions with a distinct set of facial movements common to the group, easterners do not, and that easterners represent emotional intensity with distinctive dynamic eye activity. Such divergence in findings across studies might be related to the fact that experiments are usually carried on using small samples of participants who typically label photographs of posed expressions with 5–6 words and neglecting the social context in which facial expressions arise. Very recently, a group of researchers from the University of Berkeley and the Google research department, carried on a massive data research aimed at examining a multitude of context–expression associations across a comprehensive array of cultures (Cowen and others, 2021). To this aim, they used deep neural networks (DNNs) in two experiments to examine the extent to which 16 types of facial expression occurred systematically in thousands of contexts in 6 million videos from 144 countries, across 12 world regions. DNNs apply multiple layers of transformation to inputs (in this case, videos) to predict outputs (for example, perceived facial expression). In experiment 1, authors integrated DNNs that process video content (pixel values) and metadata (titles or descriptions) to classify 3 million videos in terms of 653 contexts, including many with theoretical relevance to emotion (for example, wedding, practical joke or protest). In experiment 2, to rule out confounding visual features, a DNN that relied exclusively on user-generated titles and descriptions was used to classify another 3 million videos in terms of 1,953 contexts. Results revealed that 16 facial expressions including basic and blended expressions referring to internal emotional states (namely, amusement, anger, awe, concentration, confusion, contempt, contentment, desire, disappointment, doubt, elation, interest, pain, sadness, surprise, triumph) are associated with similar contexts in all 12 world regions. Nuanced expressions such as awe occur in similar circumstances (fireworks, toys and dance) in every region, pointing to universals in expressive behaviour across the modern world. Overall, 70% of the variation in facial expression across contexts was universal to every world studied region. Authors mention that, since these findings are based on online videos, they may have been influenced by cultural globalization, particularly through digital and social media (e.g. people across the world may have adopted facial expressions from Western media). However, no suggestions of a bias towards similarity with the West have been observed. Rather, authors document greater similarity between neighbouring regions, which is expected if the videos are representative of local cultures as opposed to Western culture. For instance, the Middle east and Indian subcontinent shared 85.0% (s.e., 0.8%) of their variance in