Stimulus features coded by single neurons of a macaque body category selective patch

Popivanov, Ivo; Schyns, Philippe G.; Vogels, Rufin

doi:10.1073/pnas.1520371113

Cited by 29 publications

(24 citation statements)

References 39 publications

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“…Analogous to classical hierarchical feature models of visual object recognition (Tanaka 1997), a viable hypothesis is that the brain processes whole-body movements by coding a range of movement features at different levels of complexity and ultimately arrives at a coherent percept through feature integration. As an example in line with this, recent monkey studies found that information in the mid-superior temporal sulcus (STS) related to body category perception is organized in the brain not so much by semantic categories than by feature statistics of the body (Popivanov et al 2016). Yet, there is currently no example of a hierarchical computational model-based approach to visual processes involved in movement perception in humans.…”

Section: Introductionmentioning

confidence: 75%

Computational Feature Analysis of Body Movements Reveals Hierarchical Brain Organization

et al. 2018

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Social species spend considerable time observing the body movements of others to understand their actions, predict their emotions, watch their games, or enjoy their dance movements. Given the important information obtained from body movements, we still know surprisingly little about the details of brain mechanisms underlying movement perception. In this fMRI study, we investigated the relations between movement features obtained from automated computational analyses of video clips and the corresponding brain activity. Our results show that low-level computational features map to specific brain areas related to early visual- and motion-sensitive regions, while mid-level computational features are related to dynamic aspects of posture encoded in occipital–temporal cortex, posterior superior temporal sulcus and superior parietal lobe. Furthermore, behavioral features obtained from subjective ratings correlated with activity in higher action observation regions. Our computational feature-based analysis suggests that the neural mechanism of movement encoding is organized in the brain not so much by semantic categories than by feature statistics of the body movements.

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Section: Introductionmentioning

confidence: 75%

Computational Feature Analysis of Body Movements Reveals Hierarchical Brain Organization

et al. 2018

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“…As such, our approach adds to a growing body of studies combining classification images with other computational methods to predict perceptual decisions [ 30 , 41 , 42 , 43 ] and infer visual processing mechanisms from behavioral and neurophysiological data [ 37 , 43 , 44 , 45 , 46 ]. Future behavioral studies could take inspiration from these approaches to investigate aspects of rodent vision that were beyond the scope of our experiments—e.g., the representation of object information in different spatial frequency bands could be explored, by relying on multi-resolution generative or filtering manipulations [ 34 , 44 , 47 ].…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Rats in Discriminating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy

et al. 2018

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SummaryDespite their growing popularity as models of visual functions, it remains unclear whether rodents are capable of deploying advanced shape-processing strategies when engaged in visual object recognition. In rats, for instance, pattern vision has been reported to range from mere detection of overall object luminance to view-invariant processing of discriminative shape features. Here we sought to clarify how refined object vision is in rodents, and how variable the complexity of their visual processing strategy is across individuals. To this aim, we measured how well rats could discriminate a reference object from 11 distractors, which spanned a spectrum of image-level similarity to the reference. We also presented the animals with random variations of the reference, and processed their responses to these stimuli to derive subject-specific models of rat perceptual choices. Our models successfully captured the highly variable discrimination performance observed across subjects and object conditions. In particular, they revealed that the animals that succeeded with the most challenging distractors were those that integrated the wider variety of discriminative features into their perceptual strategies. Critically, these strategies were largely preserved when the rats were required to discriminate outlined and scaled versions of the stimuli, thus showing that rat object vision can be characterized as a transformation-tolerant, feature-based filtering process. Overall, these findings indicate that rats are capable of advanced processing of shape information, and point to the rodents as powerful models for investigating the neuronal underpinnings of visual object recognition and other high-level visual functions.

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“…Popivanov et al (2016) showed that single MSB neurons are sensitive to removal of some parts of an effective shape. Thus, this part manipulation provides an additional set of stimuli to which we measured responses and that could be used as an independent test of the shape selectivity models.…”

Section: Methodsmentioning

confidence: 99%

Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons

Kalfas

Kumar

Vogels

2017

eNeuro

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Functional MRI studies in primates have demonstrated cortical regions that are strongly activated by visual images of bodies. The presence of such body patches in macaques allows characterization of the stimulus selectivity of their single neurons. Middle superior temporal sulcus body (MSB) patch neurons showed similar stimulus selectivity for natural, shaded, and textured images compared with their silhouettes, suggesting that shape is an important determinant of MSB responses. Here, we examined and modeled the shape selectivity of single MSB neurons. We measured the responses of single MSB neurons to a variety of shapes producing a wide range of responses. We used an adaptive stimulus sampling procedure, selecting and modifying shapes based on the responses of the neuron. Forty percent of shapes that produced the maximal response were rated by humans as animal-like, but the top shape of many MSB neurons was not judged as resembling a body. We fitted the shape selectivity of MSB neurons with a model that parameterizes shapes in terms of curvature and orientation of contour segments, with a pixel-based model, and with layers of units of convolutional neural networks (CNNs). The deep convolutional layers of CNNs provided the best goodness-of-fit, with a median explained explainable variance of the neurons’ responses of 77%. The goodness-of-fit increased along the convolutional layers’ hierarchy but was lower for the fully connected layers. Together with demonstrating the successful modeling of single unit shape selectivity with deep CNNs, the data suggest that semantic or category knowledge determines only slightly the single MSB neuron’s shape selectivity.

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Stimulus features coded by single neurons of a macaque body category selective patch

Cited by 29 publications

References 39 publications

Computational Feature Analysis of Body Movements Reveals Hierarchical Brain Organization

Computational Feature Analysis of Body Movements Reveals Hierarchical Brain Organization

Accuracy of Rats in Discriminating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy

Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons

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