scholarly journals Spatial receptive fields persist at the latest stages of the human ventral visual stream

2014 ◽  
Vol 14 (10) ◽  
pp. 717-717
Author(s):  
K. Kay ◽  
K. Weiner ◽  
K. Grill-Spector
Keyword(s):  
Receptive Fields ◽  
Visual Stream ◽  
Ventral Visual Stream

scholarly journals Image content is more important than Bouma’s Law for scene metamers

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Thomas SA Wallis ◽  
Christina M Funke ◽  
Alexander S Ecker ◽  
Leon A Gatys ◽  
Felix A Wichmann ◽  
...  
Keyword(s):  
Perceptual Experience ◽  
Receptive Fields ◽  
Natural Images ◽  
Retinal Eccentricity ◽  
High Fidelity ◽  
Prior Work ◽  
Visual Stream ◽  
Area V2 ◽  
Ventral Visual Stream ◽  
Stream Model

We subjectively perceive our visual field with high fidelity, yet peripheral distortions can go unnoticed and peripheral objects can be difficult to identify (crowding). Prior work showed that humans could not discriminate images synthesised to match the responses of a mid-level ventral visual stream model when information was averaged in receptive fields with a scaling of about half their retinal eccentricity. This result implicated ventral visual area V2, approximated ‘Bouma’s Law’ of crowding, and has subsequently been interpreted as a link between crowding zones, receptive field scaling, and our perceptual experience. However, this experiment never assessed natural images. We find that humans can easily discriminate real and model-generated images at V2 scaling, requiring scales at least as small as V1 receptive fields to generate metamers. We speculate that explaining why scenes look as they do may require incorporating segmentation and global organisational constraints in addition to local pooling.

scholarly journals Development differentially sculpts receptive fields across human visual cortex

2017 ◽  
Author(s):  
Jesse Gomez ◽  
Vaidehi Natu ◽  
Brianna Jeska ◽  
Michael Barnett ◽  
Kalanit Grill-Spector
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Right Hemisphere ◽  
Receptive Fields ◽  
Visual Stream ◽  
Differential Development ◽  
Processing Information ◽  
Ventral Visual Stream ◽  
Visual Field Maps ◽  
The Right

ABSTRACTReceptive fields (RFs) processing information in restricted parts of the visual field are a key property of neurons in the visual system. However, how RFs develop in humans is unknown. Using fMRI and population receptive field (pRF) modeling in children and adults, we determined where and how pRFs develop across the ventral visual stream. We find that pRF properties in visual field maps, V1 through VO1, are adult-like by age 5. However, pRF properties in face- and word-selective regions develop into adulthood, increasing the foveal representation and the visual field coverage for faces in the right hemisphere and words in the left hemisphere. Eye-tracking indicates that pRF changes are related to changing fixation patterns on words and faces across development. These findings suggest a link between viewing behavior of faces and words and the differential development of pRFs across visual cortex, potentially due to competition on foveal coverage.

scholarly journals Image content is more important than Bouma’s Law for scene metamers

2018 ◽  
Author(s):  
Thomas S. A. Wallis ◽  
Christina M. Funke ◽  
Alexander S. Ecker ◽  
Leon A. Gatys ◽  
Felix A. Wichmann ◽  
...  
Keyword(s):  
Perceptual Experience ◽  
Receptive Fields ◽  
Retinal Eccentricity ◽  
High Fidelity ◽  
Neural Responses ◽  
Visual Stream ◽  
Scale Factors ◽  
Area V2 ◽  
Ventral Visual Stream ◽  
Human Participants

AbstractWe subjectively perceive our visual field with high fidelity, yet large peripheral distortions can go unnoticed and peripheral objects can be difficult to identify (crowding). A recent paper proposed a model of the mid-level ventral visual stream in which neural responses were averaged over an area of space that increased as a function of eccentricity (scaling). Human participants could not discriminate synthesised model images from each other (they were metamers) when scaling was about half the retinal eccentricity. This result implicated ventral visual area V2 and approximated “Bouma’s Law” of crowding. It has subsequently been interpreted as a link between crowding zones, receptive field scaling, and our rich perceptual experience. However, participants in this experiment never saw the original images. We find that participants can easily discriminate real and model-generated images at V2 scaling. Lower scale factors than even V1 receptive fields may be required to generate metamers. Efficiently explaining why scenes look as they do may require incorporating segmentation processes and global organisational constraints in addition to local pooling.

Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

2019 ◽  
Author(s):  
Sushrut Thorat
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Large Degree ◽  
Visual Features ◽  
Visual Feature ◽  
Visual Stream ◽  
Feature Information ◽  
Ventral Visual Stream ◽  
Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

Orthographic processing deficits in developmental dyslexia: Beyond the ventral visual stream

NeuroImage ◽  
2016 ◽  
Vol 128 ◽  
pp. 316-327 ◽  
Author(s):  
Marianna Boros ◽  
Jean-Luc Anton ◽  
Catherine Pech-Georgel ◽  
Jonathan Grainger ◽  
Marcin Szwed ◽  
...  
Keyword(s):  
Developmental Dyslexia ◽  
Orthographic Processing ◽  
Visual Stream ◽  
Ventral Visual Stream ◽  
Processing Deficits

scholarly journals Development of population receptive fields in the lateral visual stream improves spatial coding amid stable structural-functional coupling

NeuroImage ◽  
2019 ◽  
Vol 188 ◽  
pp. 59-69 ◽  
Author(s):  
Jesse Gomez ◽  
Alexis Drain ◽  
Brianna Jeska ◽  
Vaidehi S. Natu ◽  
Michael Barnett ◽  
...  
Keyword(s):  
Receptive Fields ◽  
Functional Coupling ◽  
Spatial Coding ◽  
Visual Stream

scholarly journals Predictive coding of action intentions in dorsal and ventral visual stream is based on visual anticipations, memory-based information and motor preparation

2018 ◽  
Author(s):  
Simona Monaco ◽  
Giulia Malfatti ◽  
Alessandro Zendron ◽  
Elisa Pellencin ◽  
Luca Turella
Keyword(s):  
Visual Information ◽  
Visual Memory ◽  
Visual Recognition ◽  
Predictive Coding ◽  
Action Planning ◽  
Neural Signals ◽  
Motor Representation ◽  
Visual Stream ◽  
Ventral Visual Stream ◽  
Action Intention

AbstractPredictions of upcoming movements are based on several types of neural signals that span the visual, somatosensory, motor and cognitive system. Thus far, pre-movement signals have been investigated while participants viewed the object to be acted upon. Here, we studied the contribution of information other than vision to the classification of preparatory signals for action, even in absence of online visual information. We used functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA) to test whether the neural signals evoked by visual, memory-based and somato-motor information can be reliably used to predict upcoming actions in areas of the dorsal and ventral visual stream during the preparatory phase preceding the action, while participants were lying still. Nineteen human participants (nine women) performed one of two actions towards an object with their eyes open or closed. Despite the well-known role of ventral stream areas in visual recognition tasks and the specialization of dorsal stream areas in somato-motor processes, we decoded action intention in areas of both streams based on visual, memory-based and somato-motor signals. Interestingly, we could reliably decode action intention in absence of visual information based on neural activity evoked when visual information was available, and vice-versa. Our results show a similar visual, memory and somato-motor representation of action planning in dorsal and ventral visual stream areas that allows predicting action intention across domains, regardless of the availability of visual information.

scholarly journals Parallel processing of colors and faces in human ventral visual stream: functional evidence and technical challenges

2014 ◽  
Vol 14 (10) ◽  
pp. 985-985
Author(s):  
R. Lafer-Sousa ◽  
A. Kell ◽  
A. Takahashi ◽  
J. Feather ◽  
B. Conway ◽  
...  
Keyword(s):  
Parallel Processing ◽  
Visual Stream ◽  
Ventral Visual Stream ◽  
Technical Challenges

scholarly journals Predictive coding of action intentions in dorsal and ventral visual stream is based on visual anticipations, memory-based information and motor preparation

2019 ◽  
Vol 224 (9) ◽  
pp. 3291-3308 ◽  
Author(s):  
Simona Monaco ◽  
Giulia Malfatti ◽  
Alessandro Zendron ◽  
Elisa Pellencin ◽  
Luca Turella
Keyword(s):  
Predictive Coding ◽  
Motor Preparation ◽  
Visual Stream ◽  
Ventral Visual Stream
Sign in / Sign up

Export Citation Format

Share Document