scholarly journals A hierarchical model of visual processing simulates neural mechanisms underlying reflexive attention.

2018 ◽  
Vol 147 (9) ◽  
pp. 1273-1294 ◽  
Author(s):  
Chloe Callahan-Flintoft ◽  
Hui Chen ◽  
Brad Wyble
Keyword(s):  
Hierarchical Model ◽  
Visual Processing ◽  
Neural Mechanisms ◽  
Reflexive Attention

Repetition Suppression of Induced Gamma Activity Predicts Enhanced Orienting toward a Novel Stimulus in 6-month-old Infants

2008 ◽  
Vol 20 (12) ◽  
pp. 2137-2152 ◽  
Author(s):  
Kelly A. Snyder ◽  
Andreas Keil
Keyword(s):  
Visual Processing ◽  
Novelty Detection ◽  
Neural Mechanisms ◽  
Human Infant ◽  
Gamma Activity ◽  
Repeated Stimulus ◽  
Repetition Suppression ◽  
The Face ◽  
Over 40 Years

Habituation refers to a decline in orienting or responding to a repeated stimulus, and can be inferred to reflect learning about the properties of the repeated stimulus when followed by increased orienting to a novel stimulus (i.e., novelty detection). Habituation and novelty detection paradigms have been used for over 40 years to study perceptual and mnemonic processes in the human infant, yet important questions remain about the nature of these processes in infants. The aim of the present study was to examine the neural mechanisms underlying habituation and novelty detection in infants. Specifically, we investigated changes in induced alpha, beta, and gamma activity in 6-month-old infants during repeated presentations of either a face or an object, and examined whether these changes predicted behavioral responses to novelty at test. We found that induced gamma activity over occipital scalp regions decreased with stimulus repetition in the face condition but not in the toy condition, and that greater decreases in the gamma band were associated with enhanced orienting to a novel face at test. The pattern and topography of these findings are consistent with observations of repetition suppression in the occipital–temporal visual processing pathway, and suggest that encoding in infant habituation paradigms may reflect a form of perceptual learning. Implications for the role of repetition suppression in infant habituation and novelty detection are discussed with respect to a biased competition model of visual attention.

Two distinct neural mechanisms in early visual cortex determine subsequent visual processing

Cortex ◽  
2014 ◽  
Vol 59 ◽  
pp. 1-11 ◽  
Author(s):  
Christianne Jacobs ◽  
Tom A. de Graaf ◽  
Alexander T. Sack
Keyword(s):  
Visual Cortex ◽  
Visual Processing ◽  
Neural Mechanisms ◽  
Early Visual Cortex

scholarly journals The neural mechanism of aesthetic judgments of dynamic landscapes: an fMRI study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xueru Zhao ◽  
Junjing Wang ◽  
Jinhui Li ◽  
Guang Luo ◽  
Ting Li ◽  
...  
Keyword(s):  
Visual Processing ◽  
Occipital Lobe ◽  
Neural Mechanism ◽  
Aesthetic Judgment ◽  
Neural Mechanisms ◽  
Middle Temporal Gyrus ◽  
Dynamic Stimuli ◽  
Dynamic Landscapes ◽  
The Aesthetic ◽  
Aesthetic Judgments

AbstractMost previous neuroaesthetics research has been limited to considering the aesthetic judgment of static stimuli, with few studies examining the aesthetic judgment of dynamic stimuli. The present study explored the neural mechanisms underlying aesthetic judgment of dynamic landscapes, and compared the neural mechanisms between the aesthetic judgments of dynamic landscapes and static ones. Participants were scanned while they performed aesthetic judgments on dynamic landscapes and matched static ones. The results revealed regions of occipital lobe, frontal lobe, supplementary motor area, cingulate cortex and insula were commonly activated both in the aesthetic judgments of dynamic and static landscapes. Furthermore, compared to static landscapes, stronger activations of middle temporal gyrus (MT/V5), and hippocampus were found in the aesthetic judgments of dynamic landscapes. This study provided neural evidence that visual processing related regions, emotion-related regions were more active when viewing dynamic landscapes than static ones, which also indicated that dynamic stimuli were more beautiful than static ones.

Effects of Neural Mechanisms of Pretask Resting EEG Alpha Information on Situational Awareness: A Functional Connectivity Approach

2019 ◽  
Vol 62 (7) ◽  
pp. 1150-1170 ◽  
Author(s):  
Ardaman Kaur ◽  
Rishu Chaujar ◽  
Vijayakumar Chinnadurai
Keyword(s):  
Reaction Time ◽  
Functional Connectivity ◽  
Visual Processing ◽  
Emotional Regulation ◽  
Stroop Effect ◽  
Situational Awareness ◽  
Strong Association ◽  
Neural Mechanisms ◽  
Emotion And Memory ◽  
Neural Underpinnings

Objective In this study, the influence of pretask resting neural mechanisms on situational awareness (SA)-task is studied. Background Pretask electroencephalography (EEG) information and Stroop effect are known to influence task engagement independently. However, neural mechanisms of pretask resting absolute alpha (PRAA) and pretask resting alpha frontal asymmetry (PRAFA) in influencing SA-task which is undergoing Stroop effect is still not understood. Method The study involved pretask resting EEG measurements from 18 healthy individuals followed by functional magnetic resonance imaging (fMRI) acquisition during SA-task. To understand the effect of pretask alpha information and Stroop effect on SA, a robust correlation between mean reaction time, SA Index, PRAA, and PRAFA were assessed. Furthermore, neural underpinnings of PRAA, PRAFA in SA-task, and functional connectivity were analyzed through the EEG-informed fMRI approach. Results Significant robust correlation of reaction time was observed with SA Index (Pearson: r = .50, pcorr = .05) and PRAFA (Pearson: r = .63; pcorr = .01), respectively. Similarly, SA Index significantly correlated with PRAFA (Pearson: r = .56, pcorr = .01; Spearman: r = .61, pcorr = .007), and PRAA (Pearson: r = .59, pcorr = .005; Spearman: r = .59, pcorr = .002). Neural underpinnings of SA-task revealed regions involved in visual-processing and higher-order cognition. PRAA was primarily underpinned at frontal-temporal areas and functionally connected to SA-task regions pertaining to the emotional regulation. PRAFA has correlated with limbic and parietal regions, which are involved in integration of visual, emotion, and memory information of SA-task. Conclusion The results suggest a strong association of reaction time with SA-task and PRAFA and strongly support the hypothesis that PRAFA, PRAA, and associated neural mechanisms significantly influence the outcome of SA-task. Application It is beneficial to study the effect of pretask resting information on SA-task to improve SA.

scholarly journals Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition

2018 ◽  
Author(s):  
Brad Wyble ◽  
Chloe Callahan-Flintoft ◽  
Hui Chen ◽  
Toma Marinov ◽  
Aakash Sarkar ◽  
...  
Keyword(s):  
Visual Attention ◽  
Relevant Information ◽  
Neural Correlates ◽  
Neural Mechanisms ◽  
Covert Attention ◽  
Lower Priority ◽  
Reflexive Attention ◽  
Visual Hierarchy ◽  
New Information ◽  
Suppression Effects

AbstractA quintessential challenge for any perceptual system is the need to focus on task-relevant information without being blindsided by unexpected, yet important information. The human visual system incorporates several solutions to this challenge, one of which is a reflexive covert attention system that is rapidly responsive to both the physical salience and the task-relevance of new information. This paper presents a model that simulates behavioral and neural correlates of reflexive attention as the product of brief neural attractor states that are formed across the visual hierarchy when attention is engaged. Such attractors emerge from an attentional gradient distributed over a population of topographically organized neurons and serve to focus processing at one or more locations in the visual field, while inhibiting the processing of lower priority information. The model moves towards a resolution of key debates about the nature of reflexive attention, such as whether it is parallel or serial, and whether suppression effects are distributed in a spatial surround, or selectively at the location of distractors. Most importantly, the model develops a framework for understanding the neural mechanisms of visual attention as a spatiotopic decision process within a hierarchy and links them to observable correlates such as accuracy, reaction time, and the N2pc and PD components of the EEG. This last contribution is the most crucial for repairing the disconnect that exists between our understanding of behavioral and neural correlates of attention.

Automatic Versus Contingent Mechanisms of Sensory-Driven Neural Biasing and Reflexive Attention

2005 ◽  
Vol 17 (8) ◽  
pp. 1341-1352 ◽  
Author(s):  
Joseph B. Hopfinger ◽  
Anthony J. Ries
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Response Times ◽  
Event Related Potentials ◽  
Top Down ◽  
Manual Response ◽  
Reflexive Attention ◽  
Manual Responses ◽  
Related Potentials ◽  
The Brain

Recent studies have generated debate regarding whether reflexive attention mechanisms are triggered in a purely automatic stimulus-driven manner. Behavioral studies have found that a nonpredictive “cue” stimulus will speed manual responses to subsequent targets at the same location, but only if that cue is congruent with actively maintained top-down settings for target detection. When a cue is incongruent with top-down settings, response times are unaffected, and this has been taken as evidence that reflexive attention mechanisms were never engaged in those conditions. However, manual response times may mask effects on earlier stages of processing. Here, we used event-related potentials to investigate the interaction of bottom-up sensory-driven mechanisms and top-down control settings at multiple stages of processing in the brain. Our results dissociate sensory-driven mechanisms that automatically bias early stages of visual processing from later mechanisms that are contingent on top-down control. An early enhancement of target processing in the extrastriate visual cortex (i.e., the P1 component) was triggered by the appearance of a unique bright cue, regardless of top-down settings. The enhancement of visual processing was prolonged, however, when the cue was congruent with top-down settings. Later processing in posterior temporal-parietal regions (i.e., the ipsilateral invalid negativity) was triggered automatically when the cue consisted of the abrupt appearance of a single new object. However, in cases where more than a single object appeared during the cue display, this stage of processing was contingent on top-down control. These findings provide evidence that visual information processing is biased at multiple levels in the brain, and the results distinguish automatically triggered sensory-driven mechanisms from those that are contingent on top-down control settings.

scholarly journals Human-level saccade detection performance using deep neural networks

2018 ◽  
Author(s):  
Marie E. Bellet ◽  
Joachim Bellet ◽  
Hendrikje Nienborg ◽  
Ziad M. Hafed ◽  
Philipp Berens
Keyword(s):  
Eye Movements ◽  
Cognitive Processes ◽  
Visual Processing ◽  
Performance Metrics ◽  
Visual Space ◽  
Neural Mechanisms ◽  
Eye Tracker ◽  
Noise Levels ◽  
Performance Accuracy ◽  
Level Performance

Saccades are ballistic eye movements that rapidly shift gaze from one location of visual space to another. Detecting saccades in eye movement recordings is important not only for studying the neural mechanisms underlying sensory, motor, and cognitive processes, but also as a clinical and diagnostic tool. However, automatically detecting saccades can be difficult, particularly when such saccades are generated in coordination with other tracking eye movements, like smooth pursuits, or when the saccade amplitude is close to eye tracker noise levels, like with microsaccades. In such cases, labeling by human experts is required, but this is a tedious task prone to variability and error. We developed a convolutional neural network (CNN) to automatically detect saccades at human-level performance accuracy. Our algorithm surpasses state of the art according to common performance metrics, and will facilitate studies of neurophysiological processes underlying saccade generation and visual processing.

Learning

2011 ◽  
pp. 41-58
Author(s):  
Frank van der Velde
Keyword(s):  
Animal Models ◽  
Associative Learning ◽  
Experimental Research ◽  
Animal Behavior ◽  
Visual Processing ◽  
Visual Learning ◽  
Complex Form ◽  
Neural Mechanisms ◽  
Complex Forms

This chapter reviews research into human and animal forms of learning. It concentrates on two forms of learning in particular. The first is conditioning. The study of conditioning constitutes the first example of experimental research on learning. At first, it seemed to corroborate the view that learning consists of establishing associations. This form of learning was proposed by the early empiricists. The notion of associative learning influenced the emergence of behaviorism, which used conditioning to account for all forms of human and animal behavior. More recent research, however, has shown that conditioning is a more complex form of learning, related to propositional learning. This makes conditioning important for the study of the mechanisms of other, more complex, forms of propositional learning, as found in language and reasoning. The second form of learning reviewed here is visual learning. The study of this form of learning is important for understanding visual processing. And it is important for investigating the neural mechanisms of learning, given the availability of animal models of visual processing.

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