Par : Frédéric Gosselin
Not all individuals are equally proficient in visual recognition. We recorded high-density EEG in typical and “super-recogniser” participants while they viewed faces, objects, animals, and scenes. We trained linear classifiers to predict whether brain activity belonged to an individual from the “super” or “typical” recogniser group. Using fractional ridge regression, we also predicted individual ability scores for both face and non-face stimuli. Representational similarity analysis and computational models ings revealed two representational signatures of higher face-recognition ability: mid-level visual computations and high-level semantic computations. The ability to identify faces is supported by domain-general brain mechanisms distributed across several information processing steps, from low-level feature integration to high-level semantic processing.
Frédéric Gosselin is Full Professor in the Département de psychologie at UdeM, and co-director of CerebrUM. A leading expert in high-level vision, he is co-inventor of the Bubbles technique. He uses a combination of psychophysical, neuropsychological, brain imaging and computational methods. His recent work is on individual differences in face recognition abilities. Gosselin is the founder and CEO of Elephant Scientific Consulting Inc.. His company has been advising multinational corporations such as Unilever, Cirque du Soleil and Netflix for 19 years.
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