Search for: All records

A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks.

 

Autism spectrum disorder (ASD) is characterized by difficulties in social processes, interactions, and communication. Yet, the neurocognitive bases underlying these difficulties are unclear. Here, we triangulated the ‘trans-diagnostic’ approach to personality, social trait judgments of faces, and neurophysiology to investigate (1) the relative position of autistic traits in a comprehensive social-affective personality space, and (2) the distinct associations between the social-affective personality dimensions and social trait judgment from faces in individuals with ASD and neurotypical individuals. We collected personality and facial judgment data from a large sample of online participants (N = 89 self-identified ASD;N = 307 neurotypical controls). Factor analysis with 33 subscales of 10 social-affective personality questionnaires identified a 4-dimensional personality space. This analysis revealed that ASD and control participants did not differ significantly along the personality dimensions of empathy and prosociality, antisociality, or social agreeableness. However, the ASD participants exhibited a weaker association between prosocial personality dimensions and judgments of facial trustworthiness and warmth than the control participants. Neurophysiological data also indicated that ASD participants had a weaker association with neuronal representations for trustworthiness and warmth from faces. These results suggest that the atypical association between social-affective personality and social trait judgment from faces may contribute to the social and affective difficulties associated with ASD.

 

Faces are salient social stimuli that attract a stereotypical pattern of eye movement. The human amygdala and hippocampus are involved in various aspects of face processing; however, it remains unclear how they encode the content of fixations when viewing faces. To answer this question, we employed single-neuron recordings with simultaneous eye tracking when participants viewed natural face stimuli. We found a class of neurons in the human amygdala and hippocampus that encoded salient facial features such as the eyes and mouth. With a control experiment using non-face stimuli, we further showed that feature selectivity was specific to faces. We also found another population of neurons that differentiated saccades to the eyes vs. the mouth. Population decoding confirmed our results and further revealed the temporal dynamics of face feature coding. Interestingly, we found that the amygdala and hippocampus played different roles in encoding facial features. Lastly, we revealed two functional roles of feature-selective neurons: 1) they encoded the salient region for face recognition, and 2) they were related to perceived social trait judgments. Together, our results link eye movement with neural face processing and provide important mechanistic insights for human face perception.

 

It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling. A mathematical abstraction of object recognition by the visual cortex is how to untangle the manifolds associated with different object categories. Such a manifold untangling problem is closely related to the celebrated kernel trick in metric space. In this paper, we conjecture that there is a more general solution to manifold untangling in the topological space without artificially defining any distance metric. Geometrically, we can either embed a manifold in a higher-dimensional space to promote selectivity or flatten a manifold to promote tolerance. General strategies of both global manifold embedding and local manifold flattening are presented and connected with existing work on the untangling of image, audio, and language data. We also discuss the implications of untangling the manifold into motor control and internal representations. 

Blindrestoration of low-quality faces in the real world has advanced rapidly in recent years. The rich and diverse priors encapsulated by pre-trained face GAN have demonstrated their effectiveness in reconstructing high-quality faces from low-quality observations in the real world. However, the modeling of degradation in real-world face images remains poorly understood, affecting the property of generalization of existing methods. Inspired by the success of pre-trained models and transformers in recent years, we propose to solve the problem of blind restoration by jointly exploiting their power for degradation and prior learning, respectively. On the one hand, we train a two-generator architecture for degradation learning to transfer the style of low-quality real-world faces to the high-resolution output of pre-trained StyleGAN. On the other hand, we present a hybrid architecture, called Skip-Transformer (ST), which combines transformer encoder modules with a pre-trained StyleGAN-based decoder using skip layers. Such a hybrid design is innovative in that it represents the first attempt to jointly exploit the global attention mechanism of the transformer and pre-trained StyleGAN-based generative facial priors. We have compared our DL-ST model with the latest three benchmarks for blind image restoration (DFDNet, PSFRGAN, and GFP-GAN). Our experimental results have shown that this work outperforms all other competing methods, both subjectively and objectively (as measured by the Fréchet Inception Distance and NIQE metrics).