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Synthetic images of facial expression were used to assess whether judges can correctly recognize emotions exclusively on the basis of configurations of facial muscle movements. A first study showed that static, synthetic images modeled after a series of photographs that are widely used in facial expression research yielded recognition rates and confusion patterns comparable to posed photos. In a second study, animated synthetic images were used to examine whether schematic facial expressions consisting entirely of theoretically postulated facial muscle configurations can be correctly recognized. Recognition rates for the synthetic expressions were far above chance, and the confusion patterns were comparable to those obtained with posed photos. In addition, the effect of static versus dynamic presentation of the expressions was studied. Dynamic presentation increased overall recognition accuracy and reduced confusions between unrelated emotions.

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The mechanisms through which people perceive different types of smiles and judge their authenticity remain unclear. Here, 19 different types of smiles were created based on the Facial Action Coding System (FACS), using highly controlled, dynamic avatar faces. Participants observed short videos of smiles while their facial mimicry was measured with electromyography (EMG) over four facial muscles. Smile authenticity was judged after each trial. Avatar attractiveness was judged once in response to each avatar’s neutral face. Results suggest that, in contrast to most earlier work using static pictures as stimuli, participants relied less on the Duchenne marker (the presence of crow’s feet wrinkles around the eyes) in their judgments of authenticity. Furthermore, mimicry of smiles occurred in the Zygomaticus Major, Orbicularis Oculi, and Corrugator muscles. Consistent with theories of embodied cognition, activity in these muscles predicted authenticity judgments, suggesting that facial mimicry influences the perception of smiles. However, no significant mediation effect of facial mimicry was found. Avatar attractiveness did not predict authenticity judgments or mimicry patterns.

Artificial intelligence can provide organizations with prescriptive options for decision-making. Based on the notions of algorithmic decision-making and user involvement, we assess the role of artificial intelligence in workplace decisions. Using a case study on the implementation and use of cognitive software in a telecommunications company, we address how actors can become distanced from or remain involved in decision-making. Our results show that humans are increasingly detached from decision-making spatially as well as temporally and in terms of rational distancing and cognitive displacement. At the same time, they remain attached to decision-making because of accidental and infrastructural proximity, imposed engagement, and affective adhesion. When human and algorithmic intelligence become unbalanced in regard to humans’ attachment to decision-making, three performative effects result: deferred decisions, workarounds, and (data) manipulations. We conceptualize the user interface that presents decisions to humans as a mediator between human detachment and attachment and, thus, between algorithmic and humans’ decisions. These findings contrast the traditional view of automated media as diminishing user involvement and have useful implications for research on artificial intelligence and algorithmic decision-making in organizations.

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