Facial expressions are inherently dynamic, and our visual system is sensitive to subtle changes in their temporal sequence. However, researchers often use dynamic morphs of photographs—simplified, linear representations of motion—to study the neural correlates of dynamic face perception. To explore the brain's sensitivity to natural facial motion, we constructed a novel dynamic face database using generative neural networks, trained on a verified set of video-recorded emotional expressions. The resulting deepfakes, consciously indistinguishable from videos, enabled us to separate biological motion from photorealistic form. Results showed that conventional dynamic morphs elicit distinct responses in the brain compared to videos and photos, suggesting they violate expectations (n400) and have reduced social salience (late positive potential). This suggests that dynamic morphs misrepresent facial dynamism, resulting in misleading insights about the neural and behavioural correlates of face perception. Deepfakes and videos elicited largely similar neural responses, suggesting they could be used as a proxy for real faces in vision research, where video recordings cannot be experimentally manipulated. And yet, despite being consciously undetectable as fake, deepfakes elicited an expectation violation response in the brain. This points to a neural sensitivity to naturalistic facial motion, beyond conscious awareness. Despite some differences in neural responses, the realism and manipulability of deepfakes make them a valuable asset for research where videos are unfeasible. Using these stimuli, we proposed a novel marker for the conscious perception of naturalistic facial motion – Frontal delta activity – which was elevated for videos and deepfakes, but not for photos or dynamic morphs.

People largely differ with respect to how well they can learn, memorize, and perceive faces. In this talk, I address two potential sources of variation. One factor might be people’s ability to adapt their perception to the kind of faces they are currently exposed to. For instance, some studies report that those who show larger adaptation effects are also better at performing face learning and memory tasks. Another factor might be people’s sensitivity to perceive fine differences between similar-looking faces. In fact, one study shows that the brain of good performers in a face memory task shows larger neural differences between similar-looking faces. Capitalizing on this body of evidence, I present a behavioural study where I explore the relationship between people’s perceptual adaptability and sensitivity and their individual face processing performance.

The integrated conflict monitoring theory of Botvinick introduced cognitive demand into conflict monitoring research. We investigated effects of individual differences of cognitive demand and another determinant of conflict monitoring entitled reinforcement sensitivity on conflict monitoring. We showed evidence of differential variability of conflict monitoring intensity using the electroencephalogram (EEG), functional magnet resonance imaging (fMRI) and behavioral data. Our data suggest that individual differences of anxiety and reasoning ability are differentially related to the recruitment of proactive and reactive cognitive control (cf. Braver). Based on previous findings, the team of the Leue-Lab investigated new psychometric data on conflict monitoring and proactive-reactive cognitive control. Moreover, data of the Leue-Lab suggest the relevance of individual differences of conflict monitoring for the context of deception. In this respect, we plan new studies highlighting individual differences of the functioning of the Anterior Cingulate Cortex (ACC). Disentangling the role of individual differences in working memory-related cognitive demand, mental effort, and reinforcement-related processes opens new insights for cognitive-motivational approaches of information processing (Passcode to rewatch: 0R8v&m59).