scholarly journals Contour junctions underlie neural representations of scene categories in high-level human visual cortex

NeuroImage ◽  
2016 ◽  
Vol 135 ◽  
pp. 32-44 ◽  
Author(s):  
Heeyoung Choo ◽  
Dirk B. Walther
2016 ◽  
Author(s):  
Heeyoung Choo ◽  
Dirk B Walther

Humans efficiently grasp complex visual environments, making highly consistent judgments of entry-level category despite their high variability in visual appearance. How does the human brain arrive at the invariant neural representations underlying categorization of real-world environments? We here show that the neural representation of visual environments in scenes-selective human visual cortex relies on statistics of contour junctions, which provide cues for the three-dimensional arrangement of surfaces in a scene. We manipulated line drawings of real-world environments such that statistics of contour orientations or junctions were disrupted. Manipulated and intact line drawings were presented to participants in an fMRI experiment. Scene categories were decoded from neural activity patterns in the parahippocampal place area (PPA), the occipital place area (OPA) and other visual brain regions. Disruption of junctions but not orientations led to a drastic decrease in decoding accuracy in the PPA and OPA, indicating the reliance of these areas on intact junction statistics. Accuracy of decoding from early visual cortex, on the other hand, was unaffected by either image manipulation. We further show that the correlation of error patterns between decoding from the scene-selective brain areas and behavioral experiments is contingent on intact contour junctions. Finally, a searchlight analysis exposes the reliance of visually active brain regions on different sets of contour properties. Statistics of contour length and curvature dominate neural representations of scene categories in early visual areas and contour junctions in high-level scene-selective brain regions.


2014 ◽  
Vol 98 (2) ◽  
pp. 87-91
Author(s):  
Yasuhiro Kawashima ◽  
Hiroyuki Yamashiro ◽  
Hiroki Yamamoto ◽  
Tomokazu Murase ◽  
Yoshikatsu Ichimura ◽  
...  

2010 ◽  
Vol 103 (3) ◽  
pp. 1501-1507 ◽  
Author(s):  
P.-J. Hsieh ◽  
E. Vul ◽  
N. Kanwisher

Early retinotopic cortex has traditionally been viewed as containing a veridical representation of the low-level properties of the image, not imbued by high-level interpretation and meaning. Yet several recent results indicate that neural representations in early retinotopic cortex reflect not just the sensory properties of the image, but also the perceived size and brightness of image regions. Here we used functional magnetic resonance imaging pattern analyses to ask whether the representation of an object in early retinotopic cortex changes when the object is recognized compared with when the same stimulus is presented but not recognized. Our data confirmed this hypothesis: the pattern of response in early retinotopic visual cortex to a two-tone “Mooney” image of an object was more similar to the response to the full grayscale photo version of the same image when observers knew what the two-tone image represented than when they did not. Further, in a second experiment, high-level interpretations actually overrode bottom-up stimulus information, such that the pattern of response in early retinotopic cortex to an identified two-tone image was more similar to the response to the photographic version of that stimulus than it was to the response to the identical two-tone image when it was not identified. Our findings are consistent with prior results indicating that perceived size and brightness affect representations in early retinotopic visual cortex and, further, show that even higher-level information—knowledge of object identity—also affects the representation of an object in early retinotopic cortex.


2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Jesse Gomez ◽  
Vaidehi Natu ◽  
Brianna Jeska ◽  
Michael Barnett ◽  
Kalanit Grill-Spector

2019 ◽  
Vol 19 (10) ◽  
pp. 174
Author(s):  
Matthew X Lowe ◽  
Yalda Mohsenzadeh ◽  
Benjamin Lahner ◽  
Santani Teng ◽  
Ian Charest ◽  
...  

2018 ◽  
Author(s):  
Marcie L. King ◽  
Iris I. A. Groen ◽  
Adam Steel ◽  
Dwight J. Kravitz ◽  
Chris I. Baker

AbstractNumerous factors have been reported to underlie the representation of complex images in high-level human visual cortex, including categories (e.g. faces, objects, scenes), animacy, and real-world size, but the extent to which this organization is reflected in behavioral judgments of real-world stimuli is unclear. Here, we compared representations derived from explicit similarity judgments and ultra-high field (7T) fMRI of human visual cortex for multiple exemplars of a diverse set of naturalistic images from 48 object and scene categories. Behavioral judgements revealed a coarse division between man-made (including humans) and natural (including animals) images, with clear groupings of conceptually-related categories (e.g. transportation, animals), while these conceptual groupings were largely absent in the fMRI representations. Instead, fMRI responses tended to reflect a separation of both human and non-human faces/bodies from all other categories. This pattern yielded a statistically significant, but surprisingly limited correlation between the two representational spaces. Further, comparison of the behavioral and fMRI representational spaces with those derived from the layers of a deep neural network (DNN) showed a strong correspondence with behavior in the top-most layer and with fMRI in the mid-level layers. These results suggest that there is no simple mapping between responses in high-level visual cortex and behavior – each domain reflects different visual properties of the images and responses in high-level visual cortex may correspond to intermediate stages of processing between basic visual features and the conceptual categories that dominate the behavioral response.Significance StatementIt is commonly assumed there is a correspondence between behavioral judgments of complex visual stimuli and the response of high-level visual cortex. We directly compared these representations across a diverse set of naturalistic object and scene categories and found a surprisingly and strikingly different representational structure. Further, both types of representation showed good correspondence with a deep neural network, but each correlated most strongly with different layers. These results show that behavioral judgments reflect more conceptual properties and visual cortical fMRI responses capture more general visual features. Collectively, our findings highlight that great care must be taken in mapping the response of visual cortex onto behavior, which clearly reflect different information.


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