scholarly journals Partially distributed representations of objects and faces in ventral temporal cortex: evidence from the structure of the object categories and neural response patterns

2004 ◽  
Vol 4 (8) ◽  
pp. 903-903
Author(s):  
F. Jiang ◽  
A. J. O'Toole ◽  
H. Abdi ◽  
J. V. Haxby
Keyword(s):  
Temporal Cortex ◽  
Neural Response ◽  
Response Patterns ◽  
Distributed Representations ◽  
Object Categories ◽  
Ventral Temporal Cortex

scholarly journals Birds of a Feather Flock Together: Experience-Driven Formation of Visual Object Categories in Human Ventral Temporal Cortex

PLoS ONE ◽  
2008 ◽  
Vol 3 (12) ◽  
pp. e3995 ◽  
Author(s):  
Marieke van der Linden ◽  
Jaap M. J. Murre ◽  
Miranda van Turennout
Keyword(s):  
Temporal Cortex ◽  
Visual Object ◽  
Object Categories ◽  
Ventral Temporal Cortex

scholarly journals The role of animal faces in the animate-inanimate distinction in the ventral temporal cortex

2020 ◽  
Author(s):  
D. Proklova ◽  
M.A. Goodale
Keyword(s):  
Temporal Cortex ◽  
Visual Features ◽  
Response Patterns ◽  
Prominent Feature ◽  
Fmri Study ◽  
The Face ◽  
Per Se ◽  
Ventral Temporal Cortex ◽  
Animal Faces

AbstractAnimate and inanimate objects elicit distinct response patterns in the human ventral temporal cortex (VTC), but the exact features driving this distinction are still poorly understood. One prominent feature that distinguishes typical animals from inanimate objects and that could potentially explain the animate-inanimate distinction in the VTC is the presence of a face. In the current fMRI study, we investigated this possibility by creating a stimulus set that included animals with faces, faceless animals, and inanimate objects, carefully matched in order to minimize other visual differences. We used both searchlight-based and ROI-based representational similarity analysis (RSA) to test whether the presence of a face explains the animate-inanimate distinction in the VTC. The searchlight analysis revealed that when animals with faces were removed from the analysis, the animate-inanimate distinction almost disappeared. The ROI-based RSA revealed a similar pattern of results, but also showed that, even in the absence of faces, information about agency (a combination of animal’s ability to move and think) is present in parts of the VTC that are sensitive to animacy. Together, these analyses showed that animals with faces do elicit a stronger animate/inanimate response in the VTC, but that this effect is driven not by faces per se, or the visual features of faces, but by other factors that correlate with face presence, such as the capacity for self-movement and thought. In short, the VTC appears to treat the face as a proxy for agency, a ubiquitous feature of familiar animals.Significance StatementMany studies have shown that images of animals are processed differently from inanimate objects in the human brain, particularly in the ventral temporal cortex (VTC). However, what features drive this distinction remains unclear. One important feature that distinguishes many animals from inanimate objects is a face. Here, we used fMRI to test whether the animate/inanimate distinction is driven by the presence of faces. We found that the presence of faces did indeed boost activity related to animacy in the VTC. A more detailed analysis, however, revealed that it was the association between faces and other attributes such as the capacity for self-movement and thinking, not the faces per se, that was driving the activity we observed.

Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex

2005 ◽  
Vol 17 (4) ◽  
pp. 580-590 ◽  
Author(s):  
Alice J. O'Toole ◽  
Fang Jiang ◽  
Hervé Abdi ◽  
James V. Haxby
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Temporal Cortex ◽  
High Spatial Frequency ◽  
Object Classification ◽  
Classification Model ◽  
Neural Structure ◽  
Line Drawings ◽  
Object Categories ◽  
Ventral Temporal Cortex

Object and face representations in ventral temporal (VT) cortex were investigated by combining object confusability data from a computational model of object classification with neural response confusability data from a functional neuroimaging experiment. A pattern-based classification algorithm learned to categorize individual brain maps according to the object category being viewed by the subject. An identical algorithm learned to classify an image-based, view-dependent representation of the stimuli. High correlations were found between the confusability of object categories and the confusability of brain activity maps. This occurred even with the inclusion of multiple views of objects, and when the object classification model was tested with high spatial frequency “line drawings” of the stimuli. Consistent with a distributed representation of objects in VT cortex, the data indicate that object categories with shared image-based attributes have shared neural structure.

scholarly journals Attention selectively reshapes the geometry of distributed semantic representation

2016 ◽  
Author(s):  
Samuel A. Nastase ◽  
Andrew C. Connolly ◽  
Nikolaas N. Oosterhof ◽  
Yaroslav O. Halchenko ◽  
J. Swaroop Guntupalli ◽  
...  
Keyword(s):  
Animal Behavior ◽  
Semantic Representation ◽  
Temporal Cortex ◽  
Neural Representation ◽  
Natural Environments ◽  
Complex Stimulus ◽  
Semantic Features ◽  
Response Patterns ◽  
Brain Extracts ◽  
Ventral Temporal Cortex

AbstractHumans prioritize different semantic qualities of a complex stimulus depending on their behavioral goals. These semantic features are encoded in distributed neural populations, yet it is unclear how attention might operate across these distributed representations. To address this, we presented participants with naturalistic video clips of animals behaving in their natural environments while the participants attended to either behavior or taxonomy. We used models of representational geometry to investigate how attentional allocation affects the distributed neural representation of animal behavior and taxonomy. Attending to animal behavior transiently increased the discriminability of distributed population codes for observed actions in anterior intraparietal, pericentral, and ventral temporal cortices. Attending to animal taxonomy while viewing the same stimuli increased the discriminability of distributed animal category representations in ventral temporal cortex. For both tasks, attention selectively enhanced the discriminability of response patterns along behaviorally relevant dimensions. These findings suggest that behavioral goals alter how the brain extracts semantic features from the visual world. Attention effectively disentangles population responses for downstream read-out by sculpting representational geometry in late-stage perceptual areas.

Object Category Structure in Response Patterns of Neuronal Population in Monkey Inferior Temporal Cortex

2007 ◽  
Vol 97 (6) ◽  
pp. 4296-4309 ◽  
Author(s):  
Roozbeh Kiani ◽  
Hossein Esteky ◽  
Koorosh Mirpour ◽  
Keiji Tanaka
Keyword(s):  
Temporal Cortex ◽  
Neuronal Population ◽  
Category Structure ◽  
Inferior Temporal Cortex ◽  
Response Patterns ◽  
Categorical Representation ◽  
Object Categories ◽  
Daily Behavior ◽  
Categorical Structure ◽  
Fixation Task

Our mental representation of object categories is hierarchically organized, and our rapid and seemingly effortless categorization ability is crucial for our daily behavior. Here, we examine responses of a large number (>600) of neurons in monkey inferior temporal (IT) cortex with a large number (>1,000) of natural and artificial object images. During the recordings, the monkeys performed a passive fixation task. We found that the categorical structure of objects is represented by the pattern of activity distributed over the cell population. Animate and inanimate objects created distinguishable clusters in the population code. The global category of animate objects was divided into bodies, hands, and faces. Faces were divided into primate and nonprimate faces, and the primate-face group was divided into human and monkey faces. Bodies of human, birds, and four-limb animals clustered together, whereas lower animals such as fish, reptile, and insects made another cluster. Thus the cluster analysis showed that IT population responses reconstruct a large part of our intuitive category structure, including the global division into animate and inanimate objects, and further hierarchical subdivisions of animate objects. The representation of categories was distributed in several respects, e.g., the similarity of response patterns to stimuli within a category was maintained by both the cells that maximally responded to the category and the cells that responded weakly to the category. These results advance our understanding of the nature of the IT neural code, suggesting an inherently categorical representation that comprises a range of categories including the amply investigated face category.

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