scholarly journals A dual role for shape skeletons in human vision: perceptual organization and object recognition

2019 ◽  
Author(s):  
Vladislav Ayzenberg ◽  
Frederik S. Kamps ◽  
Daniel D. Dilks ◽  
Stella F. Lourenco
Keyword(s):  
Object Recognition ◽  
Visual System ◽  
Perceptual Organization ◽  
Human Vision ◽  
Shape Information ◽  
Functional Roles ◽  
Response Profiles ◽  
High Level ◽  
Skeletal Model ◽  
Shape Skeletons

AbstractShape perception is crucial for object recognition. However, it remains unknown exactly how shape information is represented, and, consequently, used by the visual system. Here, we hypothesized that the visual system represents “shape skeletons” to both (1) perceptually organize contours and component parts into a shape percept, and (2) compare shapes to recognize objects. Using functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA), we found that a model of skeletal similarity explained significant unique variance in the response profiles of V3 and LO, regions known to be involved in perceptual organization and object recognition, respectively. Moreover, the skeletal model remained predictive in these regions even when controlling for other models of visual similarity that approximate low- to high-level visual features (i.e., Gabor-jet, GIST, HMAX, and AlexNet), and across different surface forms, a manipulation that altered object contours while preserving the underlying skeleton. Together, these findings shed light on the functional roles of shape skeletons in human vision, as well as the computational properties of V3 and LO.

scholarly journals Guest Editorial: Perceptual Organisation and Object Recognition—POOR is the Acronym, Rich the Notion

Perception ◽  
1994 ◽  
Vol 23 (4) ◽  
pp. 371-382 ◽  
Author(s):  
Johan Wagemans ◽  
Régine Kolinsky
Keyword(s):  
Object Recognition ◽  
Visual System ◽  
Guest Editorial ◽  
Individual Characteristics ◽  
Task Demands ◽  
Careful Examination ◽  
Level Versus ◽  
High Level ◽  
Available Information ◽  
Processing Mechanism

Instead of studying perceptual organisation and object recognition in relative isolation, they can be viewed as two highly related sets of processes performed by the visual system to achieve its goal of acquiring information about the world. Fifteen papers devoted to specific subproblems within this active area of research have been brought together in two successive issues of Perception. Collectively they demonstrate that focusing on the functional interrelationships between perceptual organisation and object recognition will enrich our understanding of each of the subprocesses involved. The editorial provides an overview of the papers together with a discussion on how they relate to one another. If a general message is to be extracted from this set of papers, it is that the reported findings and the speculations offered to explain them suggest that the visual system's processes cannot be characterised in general by simple dichotomies such as analytic versus wholistic, bottom-up versus top-down, local versus global, low-level versus high-level, parallel versus serial, etc. Instead, it appears that a wide variety of mechanisms is available to the visual system. Therefore, a complete understanding of its functioning will require careful examination of the circumstances within which one processing mechanism seems to be selected over another, depending on the available information, the task demands, and perhaps even the observer's individual characteristics.

Parts of visual shape as primitives for categorization

1998 ◽  
Vol 21 (1) ◽  
pp. 36-37 ◽  
Author(s):  
Manish Singh ◽  
Barbara Landau
Keyword(s):  
Object Recognition ◽  
Visual System ◽  
Recent Work ◽  
Human Visual System ◽  
Human Vision ◽  
Visual Shape

Converging psychophysical evidence suggests that the human visual system parses shapes into component parts for the purposes of object recognition. We examine the Schyns et al. claim of “creation” of features in light of recent work on part-based representations of visual shape, particularly the perceptual rules that human vision uses to parse shapes.

scholarly journals Vision as oculomotor reward: cognitive contributions to the dynamic control of saccadic eye movements

2021 ◽  
Author(s):  
Christian Wolf ◽  
Markus Lappe
Keyword(s):  
Eye Movements ◽  
Visual Field ◽  
Visual System ◽  
Dynamic Control ◽  
Saccadic Eye Movements ◽  
Saccade Target ◽  
Cognitive Mechanisms ◽  
Foveal Vision ◽  
Subsequent Behavior ◽  
High Level

AbstractHumans and other primates are equipped with a foveated visual system. As a consequence, we reorient our fovea to objects and targets in the visual field that are conspicuous or that we consider relevant or worth looking at. These reorientations are achieved by means of saccadic eye movements. Where we saccade to depends on various low-level factors such as a targets’ luminance but also crucially on high-level factors like the expected reward or a targets’ relevance for perception and subsequent behavior. Here, we review recent findings how the control of saccadic eye movements is influenced by higher-level cognitive processes. We first describe the pathways by which cognitive contributions can influence the neural oculomotor circuit. Second, we summarize what saccade parameters reveal about cognitive mechanisms, particularly saccade latencies, saccade kinematics and changes in saccade gain. Finally, we review findings on what renders a saccade target valuable, as reflected in oculomotor behavior. We emphasize that foveal vision of the target after the saccade can constitute an internal reward for the visual system and that this is reflected in oculomotor dynamics that serve to quickly and accurately provide detailed foveal vision of relevant targets in the visual field.

Given the importance of mTOR signaling in a number of illnesses, it looks suitable to use miR 99 family members as a therapeutic intervention to deal with these illnesses by using gene therapy tools

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Therapeutic Intervention ◽  
Regulatory Network ◽  
Immune Cell ◽  
Family Members ◽  
Cell Activity ◽  
Mtor Signaling ◽  
Critical Pathways ◽  
Functional Roles ◽  
Cell Life ◽  
High Level

This review outlines the activities of mirR99 family members in different cancers. Though the functional roles of these miRs are well described in some malignancies, the functional functions of these family members in other forms of cancer remain uncertain. The development and use of engineered mouse models such as miR99a KO, miR100 KO, or miR99a/100 DKO mice challenged with the type or subtype of the cancer in question would be extremely beneficial in determining the physiological and pathological roles of members of this family in different types of cancer and immune cell subtypes.The miR99 family members, which include miR99a, miR99b, and miR100, are key components of a regulatory network that governs several aspects of the cell life cycle, including differentiation, metabolism, survival and death. They are involved in the deregulation of numerous critical pathways including growth factor receptors like FGFR and IGF1R, Notch, mTOR, TGFB and Wnt signaling pathways to alter cellular function. In addition, the typical miR99 target, mTOR, appears to be at the core of the regulatory network miR99, and is more commonly involved in miR99-mediated dysregulation of cell activity. Given the importance of mTOR signaling in a number of illnesses, it looks suitable to use miR99 family members as a therapeutic intervention to deal with these illnesses. mTOR depletion did not result in upregulating miR99a in OSCC cells. In addition, an aberrant activation of PI3K/AKT/mTOR signaling in AMLs, despite increased expression of miR99 family members in AMLs. All in all, this evidence alludes to the existence of an unknown mechanism that maintains mTOR activity running despite the presence in these cells of a high level of miR99 family members. Modulation of miR99 activity might be a viable method for changing the expression of Treg in autoimmune diseases.

scholarly journals Visual search for object categories is predicted by the representational architecture of high-level visual cortex

2017 ◽  
Vol 117 (1) ◽  
pp. 388-402 ◽  
Author(s):  
Michael A. Cohen ◽  
George A. Alvarez ◽  
Ken Nakayama ◽  
Talia Konkle
Keyword(s):  
Visual Cortex ◽  
Visual Search ◽  
Visual System ◽  
Visual Processing ◽  
Search Task ◽  
Visual Search Task ◽  
Visual Object ◽  
Neural Responses ◽  
Object Categories ◽  
High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

scholarly journals Capsule Networks as Recurrent Models ofGrouping and Segmentation

2019 ◽  
Author(s):  
Adrien Doerig ◽  
Lynn Schmittwilken ◽  
Bilge Sayim ◽  
Mauro Manassi ◽  
Michael H. Herzog
Keyword(s):  
Neural Networks ◽  
Visual System ◽  
Convolutional Neural Networks ◽  
Human Vision ◽  
Global Shape ◽  
Grouping And Segmentation ◽  
Segmentation Process ◽  
Shape Processing ◽  
Visual Crowding ◽  
Recurrent Connections

AbstractClassically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we previously showed that no classic model of vision, including ffCNNs, can explain human global shape processing (1). Here, we show that Capsule Neural Networks (CapsNets; 2), combining ffCNNs with recurrent grouping and segmentation, solve this challenge. We also show that ffCNNs and standard recurrent CNNs do not, suggesting that the grouping and segmentation capabilities of CapsNets are crucial. Furthermore, we provide psychophysical evidence that grouping and segmentation are implemented recurrently in humans, and show that CapsNets reproduce these results well. We discuss why recurrence seems needed to implement grouping and segmentation efficiently. Together, we provide mutually reinforcing psychophysical and computational evidence that a recurrent grouping and segmentation process is essential to understand the visual system and create better models that harness global shape computations.Author SummaryFeedforward Convolutional Neural Networks (ffCNNs) have revolutionized computer vision and are deeply transforming neuroscience. However, ffCNNs only roughly mimic human vision. There is a rapidly expanding body of literature investigating differences between humans and ffCNNs. Several findings suggest that, unlike humans, ffCNNs rely mostly on local visual features. Furthermore, ffCNNs lack recurrent connections, which abound in the brain. Here, we use visual crowding, a well-known psychophysical phenomenon, to investigate recurrent computations in global shape processing. Previously, we showed that no model based on the classic feedforward framework of vision can explain global effects in crowding. Here, we show that Capsule Neural Networks (CapsNets), combining ffCNNs with recurrent grouping and segmentation, solve this challenge. ffCNNs and recurrent CNNs with lateral and top-down recurrent connections do not, suggesting that grouping and segmentation are crucial for human-like global computations. Based on these results, we hypothesize that one computational function of recurrence is to efficiently implement grouping and segmentation. We provide psychophysical evidence that, indeed, grouping and segmentation is based on time consuming recurrent processes in the human brain. CapsNets reproduce these results too. Together, we provide mutually reinforcing computational and psychophysical evidence that a recurrent grouping and segmentation process is essential to understand the visual system and create better models that harness global shape computations.

scholarly journals A New Paradigm for Training Hyperactive Dopamine Transporter Knockout Rats: Influence of Novel Stimuli on Object Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Natalia P. Kurzina ◽  
Anna B. Volnova ◽  
Irina Y. Aristova ◽  
Raul R. Gainetdinov
Keyword(s):  
Object Recognition ◽  
Dopamine Transporter ◽  
Cognitive Task ◽  
Recognition Task ◽  
Long Term Memory ◽  
New Paradigm ◽  
Dopaminergic Transmission ◽  
Novel Objects ◽  
High Level ◽  
The Brain

Attention deficit hyperactivity disorder (ADHD) is believed to be connected with a high level of hyperactivity caused by alterations of the control of dopaminergic transmission in the brain. The strain of hyperdopaminergic dopamine transporter knockout (DAT-KO) rats represents an optimal model for investigating ADHD-related pathological mechanisms. The goal of this work was to study the influence of the overactivated dopamine system in the brain on a motor cognitive task fulfillment. The DAT-KO rats were trained to learn an object recognition task and store it in long-term memory. We found that DAT-KO rats can learn to move an object and retrieve food from the rewarded familiar objects and not to move the non-rewarded novel objects. However, we observed that the time of task performance and the distances traveled were significantly increased in DAT-KO rats in comparison with wild-type controls. Both groups of rats explored the novel objects longer than the familiar cubes. However, unlike controls, DAT-KO rats explored novel objects significantly longer and with fewer errors, since they preferred not to move the non-rewarded novel objects. After a 3 months’ interval that followed the training period, they were able to retain the learned skills in memory and to efficiently retrieve them. The data obtained indicate that DAT-KO rats have a deficiency in learning the cognitive task, but their hyperactivity does not prevent the ability to learn a non-spatial cognitive task under the presentation of novel stimuli. The longer exploration of novel objects during training may ensure persistent learning of the task paradigm. These findings may serve as a base for developing new ADHD learning paradigms.

scholarly journals Marmoset invariant object recognition behavior mirrors that of humans and macaques at fine scale

2020 ◽  
Author(s):  
Alexander J.E. Kell ◽  
Sophie L. Bokor ◽  
You-Nah Jeon ◽  
Tahereh Toosi ◽  
Elias B. Issa
Keyword(s):  
Object Recognition ◽  
Model Organism ◽  
High Standard ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Neuroscience ◽  
Brain Functions ◽  
Invariant Object Recognition ◽  
Small Model ◽  
High Level

The marmoset—a small monkey with a flat cortex—offers powerful techniques for studying neural circuits in a primate. However, it remains unclear whether brain functions typically studied in larger primates can be studied in the marmoset. Here, we asked whether the 300-gram marmosets’ perceptual and cognitive repertoire approaches human levels or is instead closer to rodents’. Using high-level visual object recognition as a testbed, we found that on the same task marmosets substantially outperformed rats and generalized far more robustly across images, all while performing ∼1000 trials/day. We then compared marmosets against the high standard of human behavior. Across the same 400 images, marmosets’ image-by-image recognition behavior was strikingly human-like—essentially as human-like as macaques’. These results demonstrate that marmosets have been substantially underestimated and that high-level abilities have been conserved across simian primates. Consequently, marmosets are a potent small model organism for visual neuroscience, and perhaps beyond.

scholarly journals Visual Behavior Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics

2012 ◽  
pp. 243-272 ◽  
Author(s):  
Abd El Rahman Shabayek ◽  
Olivier Morel ◽  
David Fofi
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Perception ◽  
Polarization Vision ◽  
Visual Behavior ◽  
Biologically Inspired ◽  
Long Time ◽  
Comprehensive Survey ◽  
High Level ◽  
And Robotics

For long time, it was thought that the sensing of polarization by animals is invariably related to their behavior, such as navigation and orientation. Recently, it was found that polarization can be part of a high-level visual perception, permitting a wide area of vision applications. Polarization vision can be used for most tasks of color vision including object recognition, contrast enhancement, camouflage breaking, and signal detection and discrimination. The polarization based visual behavior found in the animal kingdom is briefly covered. Then, the authors go in depth with the bio-inspired applications based on polarization in computer vision and robotics. The aim is to have a comprehensive survey highlighting the key principles of polarization based techniques and how they are biologically inspired.

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