How does our brain generate consciousness, that is, the subjective experience of what it is like to see face or hear a sound? Do we become aware of a stimulus during early sensory processing or only later when information is shared in a wide-spread fronto-parietal network? Neural correlates of consciousness are typically identified by comparing brain activity when a constant stimulus (e.g., a face) is perceived versus not perceived. However, in most previous experiments, conscious perception was systematically confounded with post-perceptual processes such as decision-making and report. In this talk, I will present recent EEG and fMRI studies dissociating neural correlates of consciousness and task-related processing in visual and auditory perception. Our results suggest that consciousness emerges during early sensory processing, while late, fronto-parietal activity is associated with post-perceptual processes rather than awareness. These findings challenge predominant theories of consciousness and highlight the importance of considering task relevance as a confound across different neuroscientific methods, experimental paradigms and sensory modalities.

Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.