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

2016 ◽  
Author(s):  
Heeyoung Choo ◽  
Dirk B Walther
Keyword(s):  
Visual Cortex ◽  
Real World ◽  
Brain Regions ◽  
Neural Representation ◽  
Contour Length ◽  
Behavioral Experiments ◽  
Line Drawings ◽  
Human Visual Cortex ◽  
Neural Representations ◽  
High Level

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.

scholarly journals Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images

2018 ◽  
Author(s):  
Marcie L. King ◽  
Iris I. A. Groen ◽  
Adam Steel ◽  
Dwight J. Kravitz ◽  
Chris I. Baker
Keyword(s):  
Neural Network ◽  
Visual Cortex ◽  
Real World ◽  
Deep Neural Network ◽  
Visual Features ◽  
Visual Responses ◽  
Good Correspondence ◽  
Human Visual Cortex ◽  
Similarity Judgments ◽  
High Level

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.

scholarly journals The Effect of High-Level Illumination on Brain Activity in the Human Visual Cortex

2014 ◽  
Vol 98 (2) ◽  
pp. 87-91
Author(s):  
Yasuhiro Kawashima ◽  
Hiroyuki Yamashiro ◽  
Hiroki Yamamoto ◽  
Tomokazu Murase ◽  
Yoshikatsu Ichimura ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Brain Activity ◽  
Human Visual Cortex ◽  
High Level

scholarly journals Disentangling Representations of Object Shape and Object Category in Human Visual Cortex: The Animate–Inanimate Distinction

2016 ◽  
Vol 28 (5) ◽  
pp. 680-692 ◽  
Author(s):  
Daria Proklova ◽  
Daniel Kaiser ◽  
Marius V. Peelen
Keyword(s):  
Visual Cortex ◽  
Activity Patterns ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Visual Object ◽  
Object Category ◽  
Human Visual Cortex ◽  
Object Properties ◽  
Occipitotemporal Cortex ◽  
Visual Properties

Objects belonging to different categories evoke reliably different fMRI activity patterns in human occipitotemporal cortex, with the most prominent distinction being that between animate and inanimate objects. An unresolved question is whether these categorical distinctions reflect category-associated visual properties of objects or whether they genuinely reflect object category. Here, we addressed this question by measuring fMRI responses to animate and inanimate objects that were closely matched for shape and low-level visual features. Univariate contrasts revealed animate- and inanimate-preferring regions in ventral and lateral temporal cortex even for individually matched object pairs (e.g., snake–rope). Using representational similarity analysis, we mapped out brain regions in which the pairwise dissimilarity of multivoxel activity patterns (neural dissimilarity) was predicted by the objects' pairwise visual dissimilarity and/or their categorical dissimilarity. Visual dissimilarity was measured as the time it took participants to find a unique target among identical distractors in three visual search experiments, where we separately quantified overall dissimilarity, outline dissimilarity, and texture dissimilarity. All three visual dissimilarity structures predicted neural dissimilarity in regions of visual cortex. Interestingly, these analyses revealed several clusters in which categorical dissimilarity predicted neural dissimilarity after regressing out visual dissimilarity. Together, these results suggest that the animate–inanimate organization of human visual cortex is not fully explained by differences in the characteristic shape or texture properties of animals and inanimate objects. Instead, representations of visual object properties and object category may coexist in more anterior parts of the visual system.

scholarly journals Neural codes of seeing architectural styles

2016 ◽  
Author(s):  
Heeyoung Choo ◽  
Jack Nasar ◽  
Bardia Nikrahei ◽  
Dirk B. Walther
Keyword(s):  
Architectural Design ◽  
Brain Regions ◽  
Neural Representation ◽  
Neural Encoding ◽  
Substantial Impact ◽  
Fine Grained ◽  
Face Area ◽  
Architectural Styles ◽  
Subordinate Level ◽  
High Level

AbstractImages of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people’s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

scholarly journals Probabilistic representation in human visual cortex reflects uncertainty in serial decisions

2019 ◽  
Author(s):  
R.S. van Bergen ◽  
J.F.M. Jehee
Keyword(s):  
Visual Cortex ◽  
Neural Coding ◽  
Probability Distributions ◽  
Sensory Information ◽  
Population Level ◽  
Neural Representation ◽  
Serial Dependence ◽  
Population Activity ◽  
Human Visual Cortex ◽  
Sensory Uncertainty

AbstractHow does the brain represent the reliability of its sensory evidence? Here, we test whether sensory uncertainty is encoded in cortical population activity as the width of a probability distribution – a hypothesis that lies at the heart of Bayesian models of neural coding. We probe the neural representation of uncertainty by capitalizing on a well-known behavioral bias called serial dependence. Human observers of either sex reported the orientation of stimuli presented in sequence, while activity in visual cortex was measured with fMRI. We decoded probability distributions from population-level activity and found that serial dependence effects in behavior are consistent with a statistically advantageous sensory integration strategy, in which uncertain sensory information is given less weight. More fundamentally, our results suggest that probability distributions decoded from human visual cortex reflect the sensory uncertainty that observers rely on in their decisions, providing critical evidence for Bayesian theories of perception.

scholarly journals Recognition Alters the Spatial Pattern of fMRI Activation in Early Retinotopic Cortex

2010 ◽  
Vol 103 (3) ◽  
pp. 1501-1507 ◽  
Author(s):  
P.-J. Hsieh ◽  
E. Vul ◽  
N. Kanwisher
Keyword(s):  
Magnetic Resonance Imaging ◽  
Visual Cortex ◽  
Object Identity ◽  
Stimulus Information ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Neural Representations ◽  
Level Information ◽  
High Level ◽  
Magnetic Resonance Imaging Pattern

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.

scholarly journals Computational mechanisms for neural representation of words

2021 ◽  
Author(s):  
Ze Fu ◽  
Xiaosha Wang ◽  
Xiaoying Wang ◽  
Huichao Yang ◽  
Jiahuan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Activity Patterns ◽  
Explanatory Power ◽  
Word Meaning ◽  
Brain Regions ◽  
Neural Representation ◽  
Space Representation ◽  
Neural Representations

A critical way for humans to acquire, represent and communicate information is through language, yet the underlying computation mechanisms through which language contributes to our word meaning representations are poorly understood. We compared three major types of word computation mechanisms from large language corpus (simple co-occurrence, graph-space relations and neural-network-vector-embedding relations) in terms of the association of words’ brain activity patterns, measured by two functional magnetic resonance imaging (fMRI) experiments. Word relations derived from a graph-space representation, and not neural-network-vector-embedding, had unique explanatory power for the neural activity patterns in brain regions that have been shown to be particularly sensitive to language processes, including the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were robust across different window sizes and graph sizes and were relatively specific to language inputs. These findings highlight the role of cumulative language inputs in organizing word meaning neural representations and provide a mathematical model to explain how different brain regions capture different types of language-derived information.

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