Bodies in motion: Unraveling the distinct roles of motion and shape in dynamic body responses in the temporal cortex.

The temporal cortex represents social stimuli, including bodies. We examine and compare the contributions of dynamic and static features to the single-unit responses to moving monkey bodies in and between a patch in the anterior dorsal bank of the superior temporal sulcus (dorsal patch [DP]) and patches in the anterior inferotemporal cortex (ventral patch [VP]), using fMRI guidance in macaques. The response to dynamics varies within both regions, being higher in DP.

A large-scale brain network of species-specific dynamic human body perception.

This ultrahigh field 7 T fMRI study addressed the question of whether there exists a core network of brain areas at the service of different aspects of body perception. Participants viewed naturalistic videos of monkey and human faces, bodies, and objects along with mosaic-scrambled videos for control of low-level features. Independent component analysis (ICA) based network analysis was conducted to find body and species modulations at both the voxel and the network levels.

Keep the head in the right place: Face-body interactions in inferior temporal cortex.

In primates, faces and bodies activate distinct regions in the inferior temporal (IT) cortex and are typically studied separately. Yet, primates interact with whole agents and not with random concatenations of faces and bodies. Despite its social importance, it is still poorly understood how faces and bodies interact in IT.

Inferior temporal cortex leads prefrontal cortex in response to a violation of a learned sequence.

Primates learn statistical regularities that are embedded in visual sequences, a form of statistical learning. Single-unit recordings in macaques showed that inferior temporal (IT) neurons are sensitive to statistical regularities in visual sequences. Here, we asked whether ventrolateral prefrontal cortex (VLPFC), which is connected to IT, is also sensitive to the transition probabilities in visual sequences and whether the statistical learning signal in IT originates in VLPFC.

Electrical stimulation of the macaque ventral tegmental area drives category-selective learning without attention.

Perception improves by repeated practice with visual stimuli, a phenomenon known as visual perceptual learning (VPL). The interplay of attentional and neuromodulatory reward signals is hypothesized to cause these behavioral and associated neuronal changes, although VPL can occur without attention (i.e.

Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex.

Recent computational studies have emphasized layer-wise quantitative similarity between convolutional neural networks (CNNs) and the primate visual ventral stream. However, whether such similarity holds for the face-selective areas, a subsystem of the higher visual cortex, is not clear. Here, we extensively investigate whether CNNs exhibit tuning properties as previously observed in different macaque face areas.

Significance of Natural Scene Statistics in Understanding the Anisotropies of Perceptual Filling-in at the Blind Spot.

Psychophysical experiments reveal our horizontal preference in perceptual filling-in at the blind spot. On the other hand, tolerance in filling-in exhibit vertical preference. What causes this anisotropy in our perception? Building upon the general notion that the functional properties of the early visual system are shaped by the innate specification as well as the statistics of the environment, we reasoned that the anisotropy in filling-in could be understood in terms of anisotropy in orientation distribution inherent in natural scene statistics.

Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot.

Filling-in at the blind spot is a perceptual phenomenon in which the visual system fills the informational void, which arises due to the absence of retinal input corresponding to the optic disc, with surrounding visual attributes. It is known that during filling-in, nonlinear neural responses are observed in the early visual area that correlates with the perception, but the knowledge of underlying neural mechanism for filling-in at the blind spot is far from complete. In this work, we attempted to present a fresh perspective on the computational mechanism of filling-in process in the framework of hierarchical predictive coding, which provides a functional explanation for a range of neural responses in the cortex.