scholarly journals Experience Shapes the Development of Neural Substrates of Face Processing in Human Ventral Temporal Cortex

2015 ◽  
pp. bhv314 ◽  
Author(s):  
Golijeh Golarai ◽  
Alina Liberman ◽  
Kalanit Grill-Spector
Keyword(s):  
Face Processing ◽  
Temporal Cortex ◽  
Neural Substrates ◽  
Ventral Temporal Cortex

What Evidence Supports Special Processing for Faces? A Cautionary Tale for fMRI Interpretation

2013 ◽  
Vol 25 (11) ◽  
pp. 1777-1793 ◽  
Author(s):  
Rosemary A. Cowell ◽  
Garrison W. Cottrell
Keyword(s):  
Face Processing ◽  
Temporal Cortex ◽  
Cautionary Tale ◽  
Face Area ◽  
Fmri Study ◽  
Topography Model ◽  
Ventral Temporal Cortex ◽  
Object Form ◽  
Multivariate Pattern ◽  
Processing Mechanisms

We trained a neurocomputational model on six categories of photographic images that were used in a previous fMRI study of object and face processing. Multivariate pattern analyses of the activations elicited in the object-encoding layer of the model yielded results consistent with two previous, contradictory fMRI studies. Findings from one of the studies [Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430, 2001] were interpreted as evidence for the object-form topography model. Findings from the other study [Spiridon, M., & Kanwisher, N. How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron, 35, 1157–1165, 2002] were interpreted as evidence for neural processing mechanisms in the fusiform face area that are specialized for faces. Because the model contains no special processing mechanism or specialized architecture for faces and yet it can reproduce the fMRI findings used to support the claim that there are specialized face-processing neurons, we argue that these fMRI results do not actually support that claim. Results from our neurocomputational model therefore constitute a cautionary tale for the interpretation of fMRI data.

scholarly journals How learning to read affects the function and structure of ventral temporal cortex

2019 ◽  
Vol 19 (10) ◽  
pp. 4c
Author(s):  
Kalanit Grill-Spector ◽  
Marisa Nordt ◽  
Vaidehi Natu ◽  
Jesse Gomez ◽  
Brianna Jeska ◽  
...  
Keyword(s):  
Temporal Cortex ◽  
Learning To Read ◽  
Ventral Temporal Cortex

scholarly journals A cortical route for face-like pattern processing in human newborns

2018 ◽  
Author(s):  
Marco Buiatti ◽  
Elisa Di Giorgio ◽  
Manuela Piazza ◽  
Carlo Polloni ◽  
Giuseppe Menna ◽  
...  
Keyword(s):  
Face Processing ◽  
Visual Stimulation ◽  
Neural Substrates ◽  
Large Set ◽  
Source Reconstruction ◽  
Pattern Processing ◽  
Cortical Circuit ◽  
Processing Circuit ◽  
Limited Duration ◽  
Inverted Face

AbstractHumans are endowed with an exceptional ability for detecting faces, a competence that in adults is supported by a set of face-specific cortical patches. Human newborns already shortly after birth preferentially orient to faces even when they are presented in the form of highly schematic geometrical patterns, over perceptually equivalent non-face-like stimuli. The neural substrates underlying this early preference are still largely unexplored. Is the adult face-specific cortical circuit already active at birth, or does its specialization develop slowly as a function of experience and/or maturation? We measured EEG responses in 1-4 days old awake, attentive human newborns to schematic face-like patterns and non-face-like control stimuli, visually presented with a slow oscillatory “peekaboo” dynamics (0.8 Hz) in a frequency-tagging design. Despite the limited duration of newborns’ attention, reliable frequency-tagged responses could be estimated for each stimulus from the peak of the EEG power spectrum at the stimulation frequency. Upright face-like stimuli elicited a significantly stronger frequency-tagged response than inverted face-like controls in a large set of electrodes. Source reconstruction of the underlying cortical activity revealed the recruitment of a partially right-lateralized network comprising lateral occipito-temporal and medial parietal areas largely overlapping with the adult face-processing circuit. This result suggests that the cortical route specialized in face processing is already functional at birth.Significance statementNewborns show a remarkable ability to detect faces even minutes after birth, an ecologically fundamental skill that is instrumental for interacting with their conspecifics. What are the neural bases of this expertise? Using EEG and a slow oscillatory visual stimulation, we identified a reliable response specific to face-like patterns in newborns, which underlying cortical sources largely overlap with the adult face-specific cortical circuit. This suggests that the development of face perception in infants might rely on an early cortical route specialized in face processing already shortly after birth.

scholarly journals Near-perfect prediction of reaction time for face gender judgments based on activity in ventral temporal cortex

2015 ◽  
Vol 15 (12) ◽  
pp. 753
Author(s):  
Kalanit Grill-Spector ◽  
Kevin Weiner ◽  
Nikolaus Kriegeskorte ◽  
Kendrick Kay
Keyword(s):  
Reaction Time ◽  
Temporal Cortex ◽  
Ventral Temporal Cortex

scholarly journals Real-world size of objects serves as an axis of object space

2021 ◽  
Author(s):  
Taicheng Huang ◽  
Yiying Song ◽  
Jia Liu
Keyword(s):  
Real World ◽  
Temporal Cortex ◽  
Principal Component ◽  
Physical World ◽  
Task Demands ◽  
Deep Convolutional Neural Networks ◽  
Object Space ◽  
The Real ◽  
Ventral Temporal Cortex ◽  
Dimensional Object

Our mind can represent various objects from the physical world metaphorically into an abstract and complex high-dimensional object space, with a finite number of orthogonal axes encoding critical object features. Previous fMRI studies have shown that the middle fusiform sulcus in the ventral temporal cortex separates the real-world small-size map from the large-size map. Here we asked whether the feature of objects' real-world size constructed an axis of object space with deep convolutional neural networks (DCNNs) based on three criteria of sensitivity, independence and necessity that are impractical to be examined altogether with traditional approaches. A principal component analysis on features extracted by the DCNNs showed that objects' real-world size was encoded by an independent component, and the removal of this component significantly impaired DCNN's performance in recognizing objects. By manipulating stimuli, we found that the shape and texture of objects, rather than retina size, co-occurrence and task demands, accounted for the representation of the real-world size in the DCNNs. A follow-up fMRI experiment on humans further demonstrated that the shape, but not the texture, was used to infer the real-world size of objects in humans. In short, with both computational modeling and empirical human experiments, our study provided the first evidence supporting the feature of objects' real-world size as an axis of object space, and devised a novel paradigm for future exploring the structure of object space.

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