Identifying the cortical face network with dynamic face stimuli: A large group fMRI study.

Functional magnetic resonance imaging (fMRI) studies have identified a network of face-selective regions distributed across the human brain. In the present study, we analyzed data from a large group of gender-balanced participants to investigate how reliably these face-selective regions could be identified across both cerebral hemispheres. Participants ( =52) were scanned with fMRI while viewing short videos of faces, bodies, and objects.

Inability to move one's face dampens facial expression perception.

Humans rely heavily on facial expressions for social communication to convey their thoughts and emotions and to understand them in others. One prominent but controversial view is that humans learn to recognize the significance of facial expressions by mimicking the expressions of others. This view predicts that an inability to make facial expressions (e.

Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network.

Both the primate visual system and artificial deep neural network (DNN) models show an extraordinary ability to simultaneously classify facial expression and identity. However, the neural computations underlying the two systems are unclear. Here, we developed a multi-task DNN model that optimally classified both monkey facial expressions and identities.

Disentangling Object Category Representations Driven by Dynamic and Static Visual Input.

Humans can label and categorize objects in a visual scene with high accuracy and speed, a capacity well characterized with studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed by the brain in the absence of luminance-defined form information, we created a novel stimulus set of "object kinematograms" to isolate motion-defined signals from other sources of visual information.