scholarly journals Neural Sensitivity to Mutual Information in Intermediate-Complexity Face Features Changes during Childhood

2019 ◽  
Vol 9 (7) ◽  
pp. 154
Author(s):  
Benjamin Balas ◽  
Assaf Harel ◽  
Amanda Auen ◽  
Alyson Saville
Keyword(s):  
Face Recognition ◽  
Mutual Information ◽  
Middle Childhood ◽  
Visual Information ◽  
Neural Responses ◽  
Low Level ◽  
Face Features ◽  
Infancy And Childhood ◽  
Recognition Judgments ◽  
High Level

One way in which face recognition develops during infancy and childhood is with regard to the visual information that contributes most to recognition judgments. Adult face recognition depends on critical features spanning a hierarchy of complexity, including low-level, intermediate, and high-level visual information. To date, the development of adult-like information biases for face recognition has focused on low-level features, which are computationally well-defined but low in complexity, and high-level features, which are high in complexity, but not defined precisely. To complement this existing literature, we examined the development of children’s neural responses to intermediate-level face features characterized using mutual information. Specifically, we examined children’s and adults’ sensitivity to varying levels of category diagnosticity at the P100 and N170 components. We found that during middle childhood, sensitivity to mutual information shifts from early components to later ones, which may indicate a critical restructuring of face recognition mechanisms that takes place over several years. This approach provides a useful bridge between the study of low- and high-level visual features for face recognition and suggests many intriguing questions for further investigation.

scholarly journals Children are sensitive to mutual information in intermediate-complexity face and nonface features

2019 ◽  
Author(s):  
Benjamin Balas ◽  
Amanda Auen ◽  
Alyson Saville ◽  
Jamie Schmidt ◽  
Assaf Harel
Keyword(s):  
Mutual Information ◽  
Visual Information ◽  
Diagnostic Category ◽  
Neural Responses ◽  
Information Usage ◽  
Intermediate Complexity ◽  
Developmental Theories ◽  
Specific Category ◽  
Level Information ◽  
High Level

Uncovering when children learn to use specific visual information for recognizingobject categories is essential for understanding how experience shapes recognition.Research on the development of face recognition has focused on children’s use oflow-level information (e.g. orientation sub-bands), or on children's use of high-levelinformation, namely, configural or holistic information. Do children also useintermediate complexity features for categorizing faces and objects, and if so, atwhat age? Intermediate-complexity features bridge the gap between low- and high- level processing: they have computational benefits for object detection and segmentation, and are known to drive neural responses in the ventral visual system.Here, we asked when children develop sensitivity to diagnostic category information in intermediate-complexity features. We presented children (5-10 years old) and adults with image fragments of faces (Experiment 1) and cars (Experiment 2) varying in their mutual information, which quantities a fragment's diagnosticity of a specific category. Our goal was to determine whether children were sensitive to the amount of mutual information in these fragments, and if their information usage is different from adults’. We found that despite better overall categorization performance in adults, all children were sensitive to fragment diagnosticity in both categories, suggesting that intermediate representations of appearance are established early in childhood. Moreover, children's usage of mutual information was not limited to face fragments, suggesting the extracting intermediate complexity features is a process that is not specific only to faces. We discuss the implications of our findings for developmental theories of face and object recognition.

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

2020 ◽  
Vol 64 (1) ◽  
pp. 1302-1307
Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall
Keyword(s):  
Visual Information ◽  
Human Perception ◽  
Low Level ◽  
Hit Rate ◽  
Word Level ◽  
Level Of Automation ◽  
Significant Difference ◽  
Human Operators ◽  
And Performance ◽  
High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

scholarly journals Reconstructing feedback representations in ventral visual pathway with a generative adversarial autoencoder

2020 ◽  
Author(s):  
Haider Al-Tahan ◽  
Yalda Mohsenzadeh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Visual Pathway ◽  
Functional Magnetic Resonance ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
High Level ◽  
Feedback Connections ◽  
The Brain ◽  
Insight Into

AbstractWhile vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.Author summaryIt has been shown that the ventral visual cortex consists of a dense network of regions with feedforward and feedback connections. The feedforward path processes visual inputs along a hierarchy of cortical areas that starts in early visual cortex (an area tuned to low level features e.g. edges/corners) and ends in inferior temporal cortex (an area that responds to higher level categorical contents e.g. faces/objects). Alternatively, the feedback connections modulate neuronal responses in this hierarchy by broadcasting information from higher to lower areas. In recent years, deep neural network models which are trained on object recognition tasks achieved human-level performance and showed similar activation patterns to the visual brain. In this work, we developed a generative neural network model that consists of encoding and decoding sub-networks. By comparing this computational model with the human brain temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) response patterns, we found that the encoder processes resemble the brain feedforward processing dynamics and the decoder shares similarity with the brain feedback processing dynamics. These results provide an algorithmic insight into the spatiotemporal dynamics of feedforward and feedback processes in biological vision.

scholarly journals Inferotemporal cortex multiplexes behaviorally-relevant target match signals and visual representations in a manner that minimizes their interference

2017 ◽  
Author(s):  
Noam Roth ◽  
Nicole C. Rust
Keyword(s):  
Visual Information ◽  
Visual Representations ◽  
Target Object ◽  
Object Identity ◽  
Neural Responses ◽  
Inferotemporal Cortex ◽  
Match To Sample ◽  
Sample Object ◽  
High Level ◽  
Detrimental Impact

AbstractFinding a sought visual target object requires combining visual information about a scene with a remembered representation of the target to create a “target match” signal that indicates when a target is in view. Target match signals have been reported to exist within high-level visual brain areas including inferotemporal cortex (IT), where they are mixed with representations of image and object identity. However, these signals are not well understood, particularly in the context of the real-world challenge that the objects we search for typically appear at different positions, sizes, and within different background contexts. To investigate these signals, we recorded neural responses in IT as two rhesus monkeys performed a delayed-match-to-sample object search task in which target objects could appear at a variety of identity-preserving transformations. Consistent with the existence of behaviorally-relevant target match signals in IT, we found that IT contained a linearly separable target match representation that reflected behavioral confusions on trials in which the monkeys made errors. Additionally, target match signals were highly distributed across the IT population, and while a small fraction of units reflected target match signals as target match suppression, most units reflected target match signals as target match enhancement. Finally, we found that the potentially detrimental impact of target match signals on visual representations was mitigated by target match modulation that was approximately (albeit imperfectly) multiplicative. Together, these results support the existence of a robust, behaviorally-relevant target match representation in IT that is configured to minimally interfere with IT visual representations.

scholarly journals Continuous Flash Suppression Known and Unknowns

2019 ◽  
Author(s):  
Ali Pournaghdali ◽  
Bennett L Schwartz
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Continuous Flash Suppression ◽  
Low Level ◽  
Experimental Configuration ◽  
High Level ◽  
The Impact ◽  
Nonconscious Perception

Studies utilizing continuous flash suppression (CFS) provide valuable information regarding conscious and nonconscious perception. There are, however, crucial unanswered questions regarding the mechanisms of suppression and the level of visual processing in the absence of consciousness with CFS. Research suggests that the answers to these questions depend on the experimental configuration and how we assess consciousness in these studies. The aim of this review is to evaluate the impact of different experimental configurations and the assessment of consciousness on the results of the previous CFS studies. We review studies that evaluated the influence of different experimental configuration on the depth of suppression with CFS and discuss how different assessments of consciousness may impact the results of CFS studies. Finally, we review behavioral and brain recording studies of CFS. In conclusion, previous studies provide evidence for survival of low-level visual information and complete impairment of high-level visual information under the influence of CFS. That is, studies suggest that nonconscious perception of lower-level visual information happens with CFS but there is no evidence for nonconscious highlevel recognition with CFS.

Bio-Inspired Scheme for Classification of Visual Information

2011 ◽  
pp. 238-262
Author(s):  
Le Dong ◽  
Ebroul Izquierdo ◽  
Shuzhi Ge
Keyword(s):  
Selective Attention ◽  
Visual Information ◽  
Natural Images ◽  
Top Down ◽  
Biologically Inspired ◽  
Low Level ◽  
Research Problems ◽  
Knowledge Structuring ◽  
High Level

In this chapter, research on visual information classification based on biologically inspired visually selective attention with knowledge structuring is presented. The research objective is to develop visual models and corresponding algorithms to automatically extract features from selective essential areas of natural images, and finally, to achieve knowledge structuring and classification within a structural description scheme. The proposed scheme consists of three main aspects: biologically inspired visually selective attention, knowledge structuring and classification of visual information. Biologically inspired visually selective attention closely follow the mechanisms of the visual “what” and “where” pathways in the human brain. The proposed visually selective attention model uses a bottom-up approach to generate essential areas based on low-level features extracted from natural images. This model also exploits a low-level top-down selective attention mechanism which performs decisions on interesting objects by human interaction with preference or refusal inclination. Knowledge structuring automatically creates a relevance map from essential areas generated by visually selective attention. The developed algorithms derive a set of well-structured representations from low-level description to drive the final classification. The knowledge structuring relays on human knowledge to produce suitable links between low-level descriptions and high-level representation on a limited training set. The backbone is a distribution mapping strategy involving two novel modules: structured low-level feature extraction using convolution neural network and topology preservation based on sparse representation and unsupervised learning algorithm. Classification is achieved by simulating high-level top-down visual information perception and classification using an incremental Bayesian parameter estimation method. The utility of the proposed scheme for solving relevant research problems is validated. The proposed modular architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available.

Content-Based Image Retrieval

2011 ◽  
pp. 744-749
Author(s):  
Alan Wee-Chung Liew ◽  
Ngai-Fong Law
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Image Features ◽  
User Preferences ◽  
Multimedia Systems ◽  
Content Based Image Retrieval ◽  
Top Down ◽  
Low Level ◽  
Feature Based ◽  
High Level

With the rapid growth of Internet and multimedia systems, the use of visual information has increased enormously, such that indexing and retrieval techniques have become important. Historically, images are usually manually annotated with metadata such as captions or keywords (Chang & Hsu, 1992). Image retrieval is then performed by searching images with similar keywords. However, the keywords used may differ from one person to another. Also, many keywords can be used for describing the same image. Consequently, retrieval results are often inconsistent and unreliable. Due to these limitations, there is a growing interest in content-based image retrieval (CBIR). These techniques extract meaningful information or features from an image so that images can be classified and retrieved automatically based on their contents. Existing image retrieval systems such as QBIC and Virage extract the so-called low-level features such as color, texture and shape from an image in the spatial domain for indexing. Low-level features sometimes fail to represent high level semantic image features as they are subjective and depend greatly upon user preferences. To bridge the gap, a top-down retrieval approach involving high level knowledge can complement these low-level features. This articles deals with various aspects of CBIR. This includes bottom-up feature- based image retrieval in both the spatial and compressed domains, as well as top-down task-based image retrieval using prior knowledge.

scholarly journals Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder

2021 ◽  
Vol 17 (3) ◽  
pp. e1008775
Author(s):  
Haider Al-Tahan ◽  
Yalda Mohsenzadeh
Keyword(s):  
Visual Information ◽  
Activity Patterns ◽  
Visual Pathway ◽  
Generative Model ◽  
Data Set ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
Neural Feedback ◽  
High Level ◽  
Diverse Data

While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.

scholarly journals A Flexible Neural Representation of Faces in the Human Brain

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Runnan Cao ◽  
Xin Li ◽  
Alexander Todorov ◽  
Shuo Wang
Keyword(s):  
Human Brain ◽  
Visual Processing ◽  
Brain Regions ◽  
Neural Representation ◽  
Neural Responses ◽  
Low Level ◽  
Feature Vectors ◽  
Social Traits ◽  
Task Instructions ◽  
High Level

Abstract An important question in human face perception research is to understand whether the neural representation of faces is dynamically modulated by context. In particular, although there is a plethora of neuroimaging literature that has probed the neural representation of faces, few studies have investigated what low-level structural and textural facial features parametrically drive neural responses to faces and whether the representation of these features is modulated by the task. To answer these questions, we employed 2 task instructions when participants viewed the same faces. We first identified brain regions that parametrically encoded high-level social traits such as perceived facial trustworthiness and dominance, and we showed that these brain regions were modulated by task instructions. We then employed a data-driven computational face model with parametrically generated faces and identified brain regions that encoded low-level variation in the faces (shape and skin texture) that drove neural responses. We further analyzed the evolution of the neural feature vectors along the visual processing stream and visualized and explained these feature vectors. Together, our results showed a flexible neural representation of faces for both low-level features and high-level social traits in the human brain.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Sign in / Sign up

Export Citation Format

Share Document