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

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #81591 Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition
sameAs	Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition
http://eprints.org/ontology/hasAccepted	Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition (PDF)
http://eprints.org/ontology/hasDocument	Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition (PDF) Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition (Other) Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition (Other) Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition (Other) Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition (Other) Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition (Other)
dc:hasVersion	Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition (PDF)
Title	Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020
Creator	Brad Wyble Hui Chen Chloe Callahan-Flintoft Aakash Sarkar Toma Marinov Howard Bowman
status	peer reviewed published
Publisher	American Psychological Association
abstract	A 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.
Is Part Of	Psychological Review https://kar.kent.ac.uk/id/repository
Subject	BF41 Psychology and philosophy Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/81591#authors
issue	6
volume	127
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #81591 Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition

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