scholarly journals Own-species bias in the representations of monkey and human face categories in the primate temporal lobe

2011 ◽  
Vol 105 (6) ◽  
pp. 2740-2752 ◽  
Author(s):  
R. Sigala ◽  
N. K. Logothetis ◽  
G. Rainer

Face categorization is fundamental for social interactions of primates and is crucial for determining conspecific groups and mate choice. Current evidence suggests that faces are processed by a set of well-defined brain areas. What is the fine structure of this representation, and how is it affected by visual experience? Here, we investigated the neural representations of human and monkey face categories using realistic three-dimensional morphed faces that spanned the continuum between the two species. We found an “own-species” bias in the categorical representation of human and monkey faces in the monkey inferior temporal cortex at the level of single neurons as well as in the population response analyzed using a pattern classifier. For monkey and human subjects, we also found consistent psychophysical evidence indicative of an own-species bias in face perception. For both behavioural and neural data, the species boundary was shifted away from the center of the morph continuum, for each species toward their own face category. This shift may reflect visual expertise for members of one's own species and be a signature of greater brain resources assigned to the processing of privileged categories. Such boundary shifts may thus serve as sensitive and robust indicators of encoding strength for categories of interest.

2019 ◽  
Author(s):  
Kamila M. Jozwik ◽  
Michael Lee ◽  
Tiago Marques ◽  
Martin Schrimpf ◽  
Pouya Bashivan

Image features computed by specific convolutional artificial neural networks (ANNs) can be used to make state-of-the-art predictions of primate ventral stream responses to visual stimuli.However, in addition to selecting the specific ANN and layer that is used, the modeler makes other choices in preprocessing the stimulus image and generating brain predictions from ANN features. The effect of these choices on brain predictivity is currently underexplored.Here, we directly evaluated many of these choices by performing a grid search over network architectures, layers, image preprocessing strategies, feature pooling mechanisms, and the use of dimensionality reduction. Our goal was to identify model configurations that produce responses to visual stimuli that are most similar to the human neural representations, as measured by human fMRI and MEG responses. In total, we evaluated more than 140,338 model configurations. We found that specific configurations of CORnet-S best predicted fMRI responses in early visual cortex, and CORnet-R and SqueezeNet models best predicted fMRI responses in inferior temporal cortex. We found specific configurations of VGG-16 and CORnet-S models that best predicted the MEG responses.We also observed that downsizing input images to ~50-75% of the input tensor size lead to better performing models compared to no downsizing (the default choice in most brain models for vision). Taken together, we present evidence that brain predictivity is sensitive not only to which ANN architecture and layer is used, but choices in image preprocessing and feature postprocessing, and these choices should be further explored.


PLoS ONE ◽  
2011 ◽  
Vol 6 (4) ◽  
pp. e18913 ◽  
Author(s):  
Satoshi Eifuku ◽  
Wania C. De Souza ◽  
Ryuzaburo Nakata ◽  
Taketoshi Ono ◽  
Ryoi Tamura

Neuron ◽  
2000 ◽  
Vol 27 (2) ◽  
pp. 385-397 ◽  
Author(s):  
Peter Janssen ◽  
Rufin Vogels ◽  
Guy A Orban

2007 ◽  
Vol 19 (3) ◽  
pp. 543-555 ◽  
Author(s):  
Bruno Rossion ◽  
Daniel Collins ◽  
Valérie Goffaux ◽  
Tim Curran

The degree of commonality between the perceptual mechanisms involved in processing faces and objects of expertise is intensely debated. To clarify this issue, we recorded occipito-temporal event-related potentials in response to faces when concurrently processing visual objects of expertise. In car experts fixating pictures of cars, we observed a large decrease of an evoked potential elicited by face stimuli between 130 and 200 msec, the N170. This sensory suppression was much lower when the car and face stimuli were separated by a 200-msec blank interval. With and without this delay, there was a strong correlation between the face-evoked N170 amplitude decrease and the subject's level of car expertise as measured in an independent behavioral task. Together, these results show that neural representations of faces and nonface objects in a domain of expertise compete for visual processes in the occipito-temporal cortex as early as 130–200 msec following stimulus onset.


2007 ◽  
Vol 97 (6) ◽  
pp. 4296-4309 ◽  
Author(s):  
Roozbeh Kiani ◽  
Hossein Esteky ◽  
Koorosh Mirpour ◽  
Keiji Tanaka

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.


2011 ◽  
Vol 71 ◽  
pp. e279-e280
Author(s):  
Satoshi Eifuku ◽  
Wania C. De Souza ◽  
Ryuzaburo Nakata ◽  
Taketoshi Ono ◽  
Ryoi Tamura

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