Summary
We recorded a large dataset of high-density electroencephalographic signals and used a combination of behavioural tests and machine learning to characterise the brain computations covarying with face recognition in individuals with extraordinary abilities. We show that individual face recognition ability can be accurately decoded from brain activity in an extended temporal interval for face and non-face objects. We demonstrate that this decoding is supported by perceptual and semantic brain computations.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Posted March 21, 2022.
