Neuronal Function in the Cortical Face Perception Network

Author(s):  
Bin Wang ◽  
Tianyi Yan ◽  
Jinglong Wu

Face perception is considered the most developed visual perceptual skill in humans. Functional magnetic resonance imaging (fMRI) studies have graphically illustrated that multiple regions exhibit a stronger neural response to faces than to other visual object categories, which were specialized for face processing. These regions are in the lateral side of the fusiform gyrus, the “fusiform face area” or FFA, in the inferior occipital gyri, the “occipital face area” or OFA, and in the superior temporal sulcus (pSTS). These regions are supposed to perform the visual analysis of faces and appear to participate differentially in different types of face perception. An important question is how faces are represented within these areas. In this chapter, the authors review the function, interaction, and topography of these regions relevant to face perception. They also discuss the human neural systems that mediate face perception and attempt to show some research dictions for face perception and neural representations.

Author(s):  
Amirhossein Farzmahdi ◽  
Fatemeh Fallah ◽  
Reza Rajimehr ◽  
Reza Ebrahimpour

2018 ◽  
Vol 129 (8) ◽  
pp. e80-e81
Author(s):  
A. Haeger ◽  
C. Pouzat ◽  
V. Luecken ◽  
K. N’Diaye ◽  
C.E. Elger ◽  
...  

NeuroImage ◽  
2014 ◽  
Vol 90 ◽  
pp. 74-83 ◽  
Author(s):  
Kei Majima ◽  
Takeshi Matsuo ◽  
Keisuke Kawasaki ◽  
Kensuke Kawai ◽  
Nobuhito Saito ◽  
...  

2017 ◽  
Vol 117 (1) ◽  
pp. 388-402 ◽  
Author(s):  
Michael A. Cohen ◽  
George A. Alvarez ◽  
Ken Nakayama ◽  
Talia Konkle

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.


PLoS ONE ◽  
2008 ◽  
Vol 3 (12) ◽  
pp. e3995 ◽  
Author(s):  
Marieke van der Linden ◽  
Jaap M. J. Murre ◽  
Miranda van Turennout

Author(s):  
Chris Eliasmith

This article describes the neural engineering framework (NEF), a systematic approach to studying neural systems that has collected and extended a set of consistent methods that are highly general. The NEF draws heavily on past work in theoretical neuroscience, integrating work on neural coding, population representation, and neural dynamics to enable the construction of large-scale biologically plausible neural simulations. It is based on the principles that neural representations defined by a combination of nonlinear encoding and optimal linear decoding and that neural dynamics are characterized by considering neural representations as control theoretic state variables.


2013 ◽  
Vol 25 (4) ◽  
pp. 1020-1031 ◽  
Author(s):  
Fraser W. Smith ◽  
Melvyn A. Goodale

NeuroImage ◽  
2000 ◽  
Vol 11 (5) ◽  
pp. 380-391 ◽  
Author(s):  
James V. Haxby ◽  
Laurent Petit ◽  
Leslie G. Ungerleider ◽  
Susan M. Courtney

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