Behavioral Change and Its Neural Correlates in Visual Agnosia After Expertise Training

Agnosia, the impairment in object and face recognition despite intact vision and intelligence, is one of the most intriguing and debilitating neuropsychological deficits. The goal of this study was to determine whether S.M., an individual with longstanding visual agnosia and concomitant prosopagnosia, can be retrained to perform visual object recognition and, if so, what neural substrates mediate this reacquisition. Additionally, of interest is the extent to which training on one type of visual stimulus generalizes to other visual stimuli, as this informs our understanding of the organization of ventral visual cortex. Greebles were chosen as the stimuli for retraining given that, in neurologically normal individuals, these stimuli can engage the fusiform face area. Posttraining, S.M. showed significant improvement in recognizing Greebles, although he did not attain normal levels of performance. He was also able to recognize untrained Greebles and showed improvement in recognizing common objects. Surprisingly, his performance on face recognition, albeit poor initially, was even more impaired following training. A comparison of preand postintervention functional neuroimaging data mirrored the behavioral findings: Face-selective voxels in the fusiform gyrus prior to training were no longer so and were, in fact, more Greeble-selective. The findings indicate potential for experience-dependent dynamic reorganization in agnosia with the possibility that residual neural tissue, with limited capacity, will compete for representations.

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Face Identification using Histogram

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset2072118 ◽

2020 ◽

pp. 670-674

Author(s):

Tejas Rana

Keyword(s):

Face Recognition ◽

Multiple Scales ◽

Recognition Performance ◽

Computational Time ◽

Visual Object ◽

Feature Descriptor ◽

Visual Object Recognition ◽

Local Contrast ◽

Contrast Normalization ◽

The Impact

Various experiments or methods can be used for face recognition and detection however two of the main contain an experiment that evaluates the impact of facial landmark localization in the face recognition performance and the second experiment evaluates the impact of extracting the HOG from a regular grid and at multiple scales. We observe the question of feature sets for robust visual object recognition. The Histogram of Oriented Gradients outperform other existing methods like edge and gradient based descriptors. We observe the influence of each stage of the computation on performance, concluding that fine-scale gradients, relatively coarse spatial binning, fine orientation binning and high- quality local contrast normalization in overlapping descriptor patches are all important for good results. Comparative experiments show that though HOG is simple feature descriptor, the proposed HOG feature achieves good results with much lower computational time.

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

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01420 ◽

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.

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

Perception ◽

10.1177/0301006619899049 ◽

2020 ◽

Vol 49 (4) ◽

pp. 373-404 ◽

Cited By ~ 5

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.

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Shape Integration for Visual Object Recognition and Its Implication in Category-Specific Visual Agnosia

Visual Cognition ◽

10.1080/713756740 ◽

1996 ◽

Vol 3 (3) ◽

pp. 221-276 ◽

Cited By ~ 58

Author(s):

Martin Arguin

Keyword(s):

Object Recognition ◽

Visual Object ◽

Visual Object Recognition ◽

Visual Agnosia

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An extension of the studyforrest dataset for vision research

10.1101/046573 ◽

2016 ◽

Cited By ~ 3

Author(s):

Ayan Sengupta ◽

Falko R. Kaule ◽

J. Swaroop Guntupalli ◽

Michael B. Hoffmann ◽

Christian Häusler ◽

...

Keyword(s):

Visual Processing ◽

Functional Neuroimaging ◽

Original Cohort ◽

Mapping Procedure ◽

Retinotopic Mapping ◽

Face Area ◽

Neuroimaging Data ◽

Imaging Research ◽

Higher Visual Areas

AbstractThe studyforrest (http://studyforrest.org) dataset is likely the largest neuroimag-ing dataset on natural language and story processing publicly available today. In this article, along with a companion publication, we present an update of this dataset that extends its scope to vision and multi-sensory research. 15 participants of the original cohort volunteered for a series of additional studies: a clinical examination of visual function, a standard retinotopic mapping procedure, and a localization of higher visual areas — such as the fusiform face area. The combination of this update, the previous data releases for the dataset, and the companion publication, which includes neuroimaging and eye tracking data from natural stimulation with a motion picture, form an extremely versatile and comprehensive resource for brain imaging research — with almost six hours of functional neuroimaging data across five different stimulation paradigms for each participant. Furthermore, we describe employed paradigms and present results that document the quality of the data for the purpose of characterising major properties of participants’ visual processing stream.

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Identification of White Matter Networks Engaged in Object (Face) Recognition Showing Differential Responses to Modulated Stimulus Strength

Cerebral Cortex Communications ◽

10.1093/texcom/tgaa067 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Muwei Li ◽

Zhaohua Ding ◽

John C Gore

Keyword(s):

Face Recognition ◽

White Matter ◽

Neural Activity ◽

Visual Processing ◽

Random Noise ◽

Recognition Task ◽

Blood Oxygenation ◽

Visual Object ◽

Visual Object Recognition ◽

Stimulus Strength

Abstract Blood-oxygenation-level-dependent (BOLD) signals in magnetic resonance imaging indirectly reflect neural activity in cortex, but they are also detectable in white matter (WM). BOLD signals in WM exhibit strong correlations with those in gray matter (GM) in a resting state, but their interpretation and relationship to GM activity in a task are unclear. We performed a parametric visual object recognition task designed to modulate the BOLD signal response in GM regions engaged in higher order visual processing, and measured corresponding changes in specific WM tracts. Human faces embedded in different levels of random noise have previously been shown to produce graded changes in BOLD activation in for example, the fusiform gyrus, as well as in electrophysiological (N170) evoked potentials. The magnitudes of BOLD responses in both GM regions and selected WM tracts varied monotonically with the stimulus strength (noise level). In addition, the magnitudes and temporal profiles of signals in GM and WM regions involved in the task coupled strongly across different task parameters. These findings reveal the network of WM tracts engaged in object (face) recognition and confirm that WM BOLD signals may be directly affected by neural activity in GM regions to which they connect.

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