Deep convolutional neural network with face identity recognition experience exhibits brain-like neural representations of personality traits

Faces contain both identity and personality trait information. Previous studies have found that convolutional neural networks trained for face identity recognition spontaneously generate personality trait information. However, the successful classification of different personality traits does not necessarily mean that convolutional neural networks adopt brain-like representation mechanisms to achieve the same computational goals. Our study found that convolutional neural network with visual experience in face identity recognition (VGG-face) exhibited brain-like neural representations of personality traits, including coupling effects and confusion effects, while convolutional neural networks with the same network architecture but lacked visual experience for face identity recognition (VGG-16 and VGG-untrained) did not exhibit brain-like effects. In addition, compared to the VGG-16 and the VGG-untrained, the VGG-face exhibited higher similarity in neural representations with the human brain across all individual personality traits. In summary, these findings revealed the necessity of visual experience in face identity recognition for developing face personality traits judgment.