Android robots capable of emotional interactions with humans have considerable potential for application to research. While several studies developed androids that can exhibit human-like emotional facial expressions, few have empirically validated androids’ facial expressions. To investigate this issue, we developed an android head called Nikola based on human psychology and conducted three studies to test the validity of its facial expressions. In Study 1, Nikola produced single facial actions, which were evaluated in accordance with the Facial Action Coding System. The (...) results showed that 17 action units were appropriately produced. In Study 2, Nikola produced the prototypical facial expressions for six basic emotions, and naïve participants labeled photographs of the expressions. The recognition accuracy of all emotions was higher than chance level. In Study 3, Nikola produced dynamic facial expressions for six basic emotions at four different speeds, and naïve participants evaluated the naturalness of the speed of each expression. The effect of speed differed across emotions, as in previous studies of human expressions. These data validate the spatial and temporal patterns of Nikola’s emotional facial expressions, and suggest that it may be useful for future psychological studies and real-life applications. (shrink)

Bowers et al. focus their criticisms on research that compares behavioral and brain data from the ventral stream with a class of deep neural networks for object recognition. While they are right to identify issues with current benchmarking research programs, they overlook a much more fundamental limitation of this literature: Disregarding the importance of action and interaction for perception.

Previous studies have shown that the human visual system can detect a face and elicit a saccadic eye movement toward it very efficiently compared to other categories of visual stimuli. In the first experiment, we tested the influence of facial expressions on fast face detection using a saccadic choice task. Face‐vehicle pairs were simultaneously presented and participants were asked to saccade toward the target (the face or the vehicle). We observed that saccades toward faces were initiated faster, and more often (...) in the correct direction, than saccades toward vehicles, regardless of the facial expressions (happy, fearful, or neutral). We also observed that saccade endpoints on face images were lower when the face was happy and higher when it was neutral. In the second experiment, we explicitly tested the detection of facial expressions. We used a saccadic choice task with emotional‐neutral pairs of faces and participants were asked to saccade toward the emotional (happy or fearful) or the neutral face. Participants were faster when they were asked to saccade toward the emotional face. They also made fewer errors, especially when the emotional face was happy. Using computational modeling, we showed that this happy face advantage can, at least partly, be explained by perceptual factors. Also, saccade endpoints were lower when the target was happy than when it was fearful. Overall, we suggest that there is no automatic prioritization of emotional faces, at least for saccades with short latencies, but that salient local face features can automatically attract attention. (shrink)

The ability to read emotions in faces helps humans efficiently assess social situations. We tested how this ability is affected by aspects of familiarization with face masks and personality, with a focus on emotional intelligence. To address aspects of the current pandemic situation, we used photos of not only faces per se but also of faces that were partially covered with face masks. The sample, the size of which was determined by an a priori power test, was recruited in Germany (...) and consisted of healthy individuals of different ages [M = 24.8 years]. Participants assessed the emotional expressions displayed by six different faces determined by a 2 × 3 design. Each person was presented with six different emotional displays with or without a face mask. Accuracy and confidence were lower with masks—in particular for the emotion disgust but also for happiness, anger, and sadness. When comparing the present data collected in July 2021 with data from a different sample collected in May 2020, when people first started to familiarize themselves with face masks in Western countries during the first wave of the COVID-19 pandemic, we did not detect an improvement in performance. There were no effects of participants’ emotional intelligence, sex, or age regarding their accuracy in assessing emotional states in faces for unmasked or masked faces. (shrink)

Facial information is a powerful channel for human-to-human communication. Characteristically, faces can be defined as biological objects that are four-dimensional (4D) patterns, whereby they have concurrently a spatial structure as well as temporal dynamics. The spatial characteristics of facial objects possess three dimensions (3D), namely breadth, height and importantly, depth. The temporal properties of facial objects are defined by how a 3D facial structure evolves dynamically over time; where time is referred to as the fourth dimension (4D). Our entire perspective (...) of another’s face, whether it be social, affective or cognitive perceptions, is therefore built on a combination of 3D and 4D visual cues. Counterintuitively, over the past few decades of experimental research in psychology, facial stimuli have largely been presented to participants as a spatial pattern that has been flattened into two dimensions (2D), while remaining largely static. The following review aims to advance and update facial researchers, on the recent revolution in computer-generated, realistic 4D facial models produced from real-life human subjects. We delve in-depth to summarize recent studies which have utilized facial stimuli that possess 3D structural cues (geometry and depth) and 4D temporal cues (3D structure + dynamic viewpoint and movement). In sum, we have found that higher-order perceptions such as identity, gender, ethnicity, emotion and personality, are critically influenced by the 4D characteristics embedded within natural human faces. In future, it is recommended that facial stimuli incorporate the 4D space-time perspective with the proposed time-resolved methods. (shrink)