scholarly journals Visual recognition of the female body axis drives spatial elements of male courtship in Drosophila melanogaster

2019 ◽  
Author(s):  
Ross M McKinney ◽  
Yehuda Ben-Shahar
Keyword(s):  
Visual Cues ◽  
Visual Recognition ◽  
Virgin Female ◽  
Mechanistic Explanation ◽  
Visual Object ◽  
Projection Neurons ◽  
Visual Object Recognition ◽  
Male Courtship ◽  
Courtship Behaviors ◽  
Spatial Locations

SummaryLike other mating behaviors, the courtship ritual exhibited by male Drosophila towards a virgin female is comprised of spatiotemporal sequences of innate behavioral elements. Yet, the specific stimuli and neural circuits that determine when and where males release individual courtship elements are not well understood. Here, we investigated the role of visual object recognition in the release of specific behavioral elements during bouts of male courtship. By using a computer vision and machine learning based approach for high-resolution analyses of the male courtship ritual, we show that the release of distinct behavioral elements occur at stereotyped locations around the female and depends on the ability of males to recognize visual landmarks present on the female. Specifically, we show that independent of female motion, males utilize unique populations of visual projection neurons to recognize the eyes of a target female, which is essential for the release of courtship behaviors at their appropriate spatial locations. Together, these results provide a mechanistic explanation for how relatively simple visual cues could play a role in driving both spatially- and temporally-complex social interactions.

Minimal Recognizable Configurations Elicit Category-selective Responses in Higher Order Visual Cortex

2019 ◽  
Vol 31 (9) ◽  
pp. 1354-1367
Author(s):  
Yael Holzinger ◽  
Shimon Ullman ◽  
Daniel Harari ◽  
Marlene Behrmann ◽  
Galia Avidan
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Visual Recognition ◽  
Recognition Performance ◽  
Occipital Cortex ◽  
Building Blocks ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Face Area ◽  
Early Visual Cortex

Visual object recognition is performed effortlessly by humans notwithstanding the fact that it requires a series of complex computations, which are, as yet, not well understood. Here, we tested a novel account of the representations used for visual recognition and their neural correlates using fMRI. The rationale is based on previous research showing that a set of representations, termed “minimal recognizable configurations” (MIRCs), which are computationally derived and have unique psychophysical characteristics, serve as the building blocks of object recognition. We contrasted the BOLD responses elicited by MIRC images, derived from different categories (faces, objects, and places), sub-MIRCs, which are visually similar to MIRCs, but, instead, result in poor recognition and scrambled, unrecognizable images. Stimuli were presented in blocks, and participants indicated yes/no recognition for each image. We confirmed that MIRCs elicited higher recognition performance compared to sub-MIRCs for all three categories. Whereas fMRI activation in early visual cortex for both MIRCs and sub-MIRCs of each category did not differ from that elicited by scrambled images, high-level visual regions exhibited overall greater activation for MIRCs compared to sub-MIRCs or scrambled images. Moreover, MIRCs and sub-MIRCs from each category elicited enhanced activation in corresponding category-selective regions including fusiform face area and occipital face area (faces), lateral occipital cortex (objects), and parahippocampal place area and transverse occipital sulcus (places). These findings reveal the psychological and neural relevance of MIRCs and enable us to make progress in developing a more complete account of object recognition.

The Role of Visual Field Position in Object Recognition

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 126-126
Author(s):  
M Dill ◽  
S Edelman
Keyword(s):  
Object Recognition ◽  
Visual Recognition ◽  
Human Subjects ◽  
Visual Object ◽  
Translation Invariance ◽  
Visual Object Recognition ◽  
Theoretical Approaches ◽  
Object Based ◽  
New Locations ◽  
Incomplete Performance

Visual recognition of objects is generally assumed to be independent of the location in the field of view. Experiments have shown, however, that for stimuli such as random-dot clouds recognition can be severely affected by retinal displacement (Foster and Kahn, 1985 Biological Cybernetics51 305 – 312; Nazir and O'Regan, 1990 Spatial Vision5 81 – 100; Dill and Fahle, Perception & Psychophysics in press). In a series of new experiments, we tested whether similar shortcomings of translation invariance can be obtained also with more natural-looking objects. For that purpose, we tested human subjects with 3-D animal-like shapes that had been employed previously in studies of rotation invariance (Edelman, 1995 Biological Cybernetics72 207 – 220). Some of our experiments included same - different discrimination, while in others the subjects had to label the briefly displayed stimulus with one of two possible labels. In both tasks, translation invariance was found to be incomplete: performance was significantly reduced when object memory had to be transferred to new locations. This positional specificity parallels the imperfect generalisation of recognition over rotation in depth, reported in the past years by many research groups. Similar to those findings, our present results suggest that the mechanisms of visual object recognition may be view-based rather than object-based. As before, these results have implications concerning the various theoretical approaches to the understanding of recognition currently under consideration.

scholarly journals Hemispheric Organization for Visual Object Recognition: A Theoretical Account and Empirical Evidence

Perception ◽  
2020 ◽  
Vol 49 (4) ◽  
pp. 373-404 ◽  
Author(s):  
Marlene Behrmann ◽  
David C. Plaut
Keyword(s):  
Face Recognition ◽  
Word Recognition ◽  
Visual Recognition ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Alternative Account ◽  
Domain Specific ◽  
Adults And Children ◽  
The Right ◽  
Language Areas

Despite the similarity in structure, the hemispheres of the human brain have somewhat different functions. A traditional view of hemispheric organization asserts that there are independent and largely lateralized domain-specific regions in ventral occipitotemporal (VOTC), specialized for the recognition of distinct classes of objects. Here, we offer an alternative account of the organization of the hemispheres, with a specific focus on face and word recognition. This alternative account relies on three computational principles: distributed representations and knowledge, cooperation and competition between representations, and topography and proximity. The crux is that visual recognition results from a network of regions with graded functional specialization that is distributed across both hemispheres. Specifically, the claim is that face recognition, which is acquired relatively early in life, is processed by VOTC regions in both hemispheres. Once literacy is acquired, word recognition, which is co-lateralized with language areas, primarily engages the left VOTC and, consequently, face recognition is primarily, albeit not exclusively, mediated by the right VOTC. We review psychological and neural evidence from a range of studies conducted with normal and brain-damaged adults and children and consider findings which challenge this account. Last, we offer suggestions for future investigations whose findings may further refine this account.

Visual Recognition

2005 ◽  
Vol 16 (2) ◽  
pp. 152-160 ◽  
Author(s):  
Kalanit Grill-Spector ◽  
Nancy Kanwisher
Keyword(s):  
Object Recognition ◽  
Exposure Duration ◽  
Visual Recognition ◽  
Natural Images ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Perceptual Categorization ◽  
Identification Task ◽  
Categorization Task ◽  
Processing Steps

What is the sequence of processing steps involved in visual object recognition? We varied the exposure duration of natural images and measured subjects' performance on three different tasks, each designed to tap a different candidate component process of object recognition. For each exposure duration, accuracy was lower and reaction time longer on a within-category identification task (e.g., distinguishing pigeons from other birds) than on a perceptual categorization task (e.g., birds vs. cars). However, strikingly, at each exposure duration, subjects performed just as quickly and accurately on the categorization task as they did on a task requiring only object detection: By the time subjects knew an image contained an object at all, they already knew its category. These findings place powerful constraints on theories of object recognition.

A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition

2003 ◽  
Vol 15 (4) ◽  
pp. 600-609 ◽  
Author(s):  
Moshe Bar
Keyword(s):  
Object Recognition ◽  
Visual Recognition ◽  
Temporal Cortex ◽  
Input Image ◽  
Initial Guess ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Top Down ◽  
Bottom Up ◽  
Level Information

The majority of the research related to visual recognition has so far focused on bottom-up analysis, where the input is processed in a cascade of cortical regions that analyze increasingly complex information. Gradually more studies emphasize the role of top-down facilitation in cortical analysis, but it remains something of a mystery how such processing would be initiated. After all, top-down facilitation implies that high-level information is activated earlier than some relevant lower-level information. Building on previous studies, I propose a specific mechanism for the activation of top-down facilitation during visual object recognition. The gist of this hypothesis is that a partially analyzed version of the input image (i.e., a blurred image) is projected rapidly from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC expectations about the most likely interpretations of the input image, which are then back-projected as an “initial guess” to the temporal cortex to be integrated with the bottom-up analysis. The top-down process facilitates recognition by substantially limiting the number of object representations that need to be considered. Furthermore, such a rapid mechanism may provide critical information when a quick response is necessary.

scholarly journals An ecologically motivated image dataset for deep learning yields better models of human vision

2021 ◽  
Vol 118 (8) ◽  
pp. e2011417118
Author(s):  
Johannes Mehrer ◽  
Courtney J. Spoerer ◽  
Emer C. Jones ◽  
Nikolaus Kriegeskorte ◽  
Tim C. Kietzmann
Keyword(s):  
Deep Learning ◽  
Visual Information ◽  
Large Scale ◽  
Visual Recognition ◽  
Network Models ◽  
Progressive Increase ◽  
Human Vision ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Neuroscience

Deep neural networks provide the current best models of visual information processing in the primate brain. Drawing on work from computer vision, the most commonly used networks are pretrained on data from the ImageNet Large Scale Visual Recognition Challenge. This dataset comprises images from 1,000 categories, selected to provide a challenging testbed for automated visual object recognition systems. Moving beyond this common practice, we here introduce ecoset, a collection of >1.5 million images from 565 basic-level categories selected to better capture the distribution of objects relevant to humans. Ecoset categories were chosen to be both frequent in linguistic usage and concrete, thereby mirroring important physical objects in the world. We test the effects of training on this ecologically more valid dataset using multiple instances of two neural network architectures: AlexNet and vNet, a novel architecture designed to mimic the progressive increase in receptive field sizes along the human ventral stream. We show that training on ecoset leads to significant improvements in predicting representations in human higher-level visual cortex and perceptual judgments, surpassing the previous state of the art. Significant and highly consistent benefits are demonstrated for both architectures on two separate functional magnetic resonance imaging (fMRI) datasets and behavioral data, jointly covering responses to 1,292 visual stimuli from a wide variety of object categories. These results suggest that computational visual neuroscience may take better advantage of the deep learning framework by using image sets that reflect the human perceptual and cognitive experience. Ecoset and trained network models are openly available to the research community.

scholarly journals Posterior inferotemporal cortex cells use multiple visual pathways to complement fine and coarse discriminations

2016 ◽  
Author(s):  
C. R. Ponce ◽  
S. G. Lomber ◽  
M. S. Livingstone
Keyword(s):  
Visual Recognition ◽  
Temporal Cortex ◽  
Macaque Monkey ◽  
Inferior Temporal Cortex ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Areas ◽  
Cortical Cooling ◽  
Feature Selectivity ◽  
Pit Cells

ABSTRACTIn the macaque monkey brain, posterior inferior temporal cortex (PIT) cells are responsible for visual object recognition. They receive concurrent inputs from visual areas V4, V3 and V2. We asked how these different anatomical pathways contribute to PIT response properties by deactivating them while monitoring PIT activity. Using cortical cooling of areas V2/V3 or V4 and a hierarchical model of visual recognition, we conclude that these distinct pathways do not transmit different classes of visual features, but serve instead to maintain a balance of local-and global-feature selectivity in IT.

Short article: Forgotten but not gone: Context effects on recognition do not require explicit memory for context

2008 ◽  
Vol 61 (11) ◽  
pp. 1620-1628 ◽  
Author(s):  
Daniel A. Levy ◽  
Elinor Rabinyan ◽  
Eli Vakil
Keyword(s):  
Recognition Memory ◽  
Visual Recognition ◽  
Context Effects ◽  
Contextual Information ◽  
Temporal Context ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Short Article ◽  
Object Recognition Memory ◽  
Indirect Assay

Context effects on recognition memory provide an important indirect assay of associative learning and source memory. Neuropsychological studies have indicated that such context effects may obtain even if the contexts themselves are not remembered—for example, in individuals impaired on direct tests of memory for contextual information. In contrast, a recent study indicated that the effects of temporal context reinstatement on visual recognition obtain only when the contextual information itself was explicitly recollected. Here we report that the effects of reinstatement of spatial-simultaneous context on visual object recognition memory obtain irrespective of whether those context stimuli are explicitly recognized. We suggest that spatial-simultaneous context effects might be based on ensemble unitization of target and context stimuli at encoding, whereas temporal context effects may require recollective processes.

scholarly journals Atoms of recognition in human and computer vision

2016 ◽  
Vol 113 (10) ◽  
pp. 2744-2749 ◽  
Author(s):  
Shimon Ullman ◽  
Liav Assif ◽  
Ethan Fetaya ◽  
Daniel Harari
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Visual Recognition ◽  
Computational Models ◽  
Human Performance ◽  
Image Interpretation ◽  
Network Models ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Neural Network Models

Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.

scholarly journals A Data-Driven and Biologically Inspired Preprocessing Scheme to Improve Visual Object Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zahra Sadat Shariatmadar ◽  
Karim Faez
Keyword(s):  
Object Recognition ◽  
Visual Recognition ◽  
Ganglion Cells ◽  
Recognition System ◽  
Processing Technique ◽  
Data Driven ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Initial Image ◽  
Biologically Inspired

Autonomous object recognition in images is one of the most critical topics in security and commercial applications. Due to recent advances in visual neuroscience, the researchers tend to extend biologically plausible schemes to improve the accuracy of object recognition. Preprocessing is one part of the visual recognition system that has received much less attention. In this paper, we propose a new, simple, and biologically inspired pre processing technique by using the data-driven mechanism of visual attention. In this part, the responses of Retinal Ganglion Cells (RGCs) are simulated. After obtaining these responses, an efficient threshold is selected. Then, the points of the raw image with the most information are extracted according to it. Then, the new images with these points are created, and finally, by combining these images with entropy coefficients, the most salient object is located. After extracting appropriate features, the classifier categorizes the initial image into one of the predefined object categories. Our system was evaluated on the Caltech-101 dataset. Experimental results demonstrate the efficacy and effectiveness of this novel method of preprocessing.

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