Spatial attention effects during conscious and nonconscious processing of visual features and objects.

2013 ◽  
Vol 39 (3) ◽  
pp. 745-756 ◽  
Author(s):  
Evelina Tapia ◽  
Bruno G. Breitmeyer ◽  
Jane Jacob ◽  
Elizabeth C. Broyles
Keyword(s):  
Spatial Attention ◽  
Visual Features ◽  
Nonconscious Processing

scholarly journals Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms

2020 ◽  
Vol 10 (12) ◽  
pp. 4312 ◽  
Author(s):  
Jie Xu ◽  
Haoliang Wei ◽  
Linke Li ◽  
Qiuru Fu ◽  
Jinhong Guo
Keyword(s):  
Neural Network ◽  
Spatial Attention ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Visual Features ◽  
Feature Maps ◽  
Global Features ◽  
Model Based ◽  
Video Description ◽  
Video Visualization

Video description plays an important role in the field of intelligent imaging technology. Attention perception mechanisms are extensively applied in video description models based on deep learning. Most existing models use a temporal-spatial attention mechanism to enhance the accuracy of models. Temporal attention mechanisms can obtain the global features of a video, whereas spatial attention mechanisms obtain local features. Nevertheless, because each channel of the convolutional neural network (CNN) feature maps has certain spatial semantic information, it is insufficient to merely divide the CNN features into regions and then apply a spatial attention mechanism. In this paper, we propose a temporal-spatial and channel attention mechanism that enables the model to take advantage of various video features and ensures the consistency of visual features between sentence descriptions to enhance the effect of the model. Meanwhile, in order to prove the effectiveness of the attention mechanism, this paper proposes a video visualization model based on the video description. Experimental results show that, our model has achieved good performance on the Microsoft Video Description (MSVD) dataset and a certain improvement on the Microsoft Research-Video to Text (MSR-VTT) dataset.

Spatial Attention Enhances Crowded Stimulus Encoding across Modeled Receptive Fields by Increasing Redundancy of Feature Representations

2021 ◽  
pp. 1-29
Author(s):  
Justin D. Theiss ◽  
Joel D. Bowen ◽  
Michael A. Silver
Keyword(s):  
Spatial Attention ◽  
Spatial Resolution ◽  
Receptive Fields ◽  
Visual Features ◽  
Target Classification ◽  
Dynamic Changes ◽  
Feature Representations ◽  
Feature Interactions ◽  
Spatial Locations ◽  
Visual Crowding

Abstract Any visual system, biological or artificial, must make a trade-off between the number of units used to represent the visual environment and the spatial resolution of the sampling array. Humans and some other animals are able to allocate attention to spatial locations to reconfigure the sampling array of receptive fields (RFs), thereby enhancing the spatial resolution of representations without changing the overall number of sampling units. Here, we examine how representations of visual features in a fully convolutional neural network interact and interfere with each other in an eccentricity-dependent RF pooling array and how these interactions are influenced by dynamic changes in spatial resolution across the array. We study these feature interactions within the framework of visual crowding, a well-characterized perceptual phenomenon in which target objects in the visual periphery that are easily identified in isolation are much more difficult to identify when flanked by similar nearby objects. By separately simulating effects of spatial attention on RF size and on the density of the pooling array, we demonstrate that the increase in RF density due to attention is more beneficial than changes in RF size for enhancing target classification for crowded stimuli. Furthermore, by varying target and flanker spacing, as well as the spatial extent of attention, we find that feature redundancy across RFs has more influence on target classification than the fidelity of the feature representations themselves. Based on these findings, we propose a candidate mechanism by which spatial attention relieves visual crowding through enhanced feature redundancy that is mostly due to increased RF density.

The Role of Spatial Attention in Nonconscious Processing

2009 ◽  
Vol 20 (1) ◽  
pp. 42-51 ◽  
Author(s):  
Matthew Finkbeiner ◽  
Romina Palermo
Keyword(s):  
Spatial Attention ◽  
Nonconscious Processing

Visual Spatial Attention in Migraine Sufferers in Postictal and Interictal Phases: An Event-Related Potential Study

2001 ◽  
Vol 15 (1) ◽  
pp. 22-34 ◽  
Author(s):  
D.H. de Koning ◽  
J.C. Woestenburg ◽  
M. Elton
Keyword(s):  
Cerebral Cortex ◽  
Visual Attention ◽  
Migraine Attack ◽  
Spatial Attention ◽  
Event Related Potential ◽  
Significant Enhancement ◽  
Pathophysiological Mechanism ◽  
Attention Task ◽  
Visual Spatial Attention ◽  
Visual Spatial

Migraineurs with and without aura (MWAs and MWOAs) as well as controls were measured twice with an interval of 7 days. The first session of recordings and tests for migraineurs was held about 7 hours after a migraine attack. We hypothesized that electrophysiological changes in the posterior cerebral cortex related to visual spatial attention are influenced by the level of arousal in migraineurs with aura, and that this varies over the course of time. ERPs related to the active visual attention task manifested significant differences between controls and both types of migraine sufferers for the N200, suggesting a common pathophysiological mechanism for migraineurs. Furthermore, migraineurs without aura (MWOAs) showed a significant enhancement for the N200 at the second session, indicating the relevance of time of measurement within migraine studies. Finally, migraineurs with aura (MWAs) showed significantly enhanced P240 and P300 components at central and parietal cortical sites compared to MWOAs and controls, which seemed to be maintained over both sessions and could be indicative of increased noradrenergic activity in MWAs.

Controlling spatial attention without central attentional resources: Evidence from electrophysiology

2009 ◽  
Author(s):  
Khara Croswaite ◽  
Mei-Ching Lien ◽  
Eric Ruthruff ◽  
Min-Ju Liao
Keyword(s):  
Spatial Attention ◽  
Attentional Resources

Reading Without Spatial Attention: What Is So Special About Stroop?

2011 ◽  
Author(s):  
Matthew Thomas ◽  
Semeon Risom ◽  
Mei-Ching Lien ◽  
Eric Ruthruff ◽  
Joel Lachter
Keyword(s):  
Spatial Attention
Sign in / Sign up

Export Citation Format

Share Document