Using deep neural networks to disentangle visual and semantic information in human perception and memory.

Mental representations of familiar categories are composed of visual and semantic information. Disentangling the contributions of visual and semantic information in humans is challenging because they are intermixed in mental representations. Deep neural networks that are trained either on images or on text or by pairing images and text enable us now to disentangle human mental representations into their visual, visual-semantic and semantic components.

Perceptual similarity modulates effects of learning from variability on face recognition.

Face recognition is a challenging classification task that humans perform effortlessly for familiar faces. Recent studies have emphasized the importance of exposure to high variability appearances of the same identity to perform this task. However, these studies did not explicitly measure the perceptual similarity between the learned images and the images presented at test, which may account for the advantage of learning from high variability.

Learning Faces as Concepts Improves Face Recognition by Engaging the Social Brain Network.

Face recognition benefits from associating social information to faces during learning. This has been demonstrated by better recognition for faces that underwent social than perceptual evaluations. Two hypotheses were proposed to account for this effect.