Visual-Tactile Bottom-Up and Top-Down Attention

2013 ◽  
pp. 183-191
Author(s):  
Qiong Wu ◽  
Chunlin Li ◽  
Satoshi Takahashi ◽  
Jinglong Wu
Keyword(s):  
Visual Attention ◽  
Brain Structure ◽  
Frontal Eye Field ◽  
Superior Parietal Lobule ◽  
Top Down ◽  
Bottom Up ◽  
Temporoparietal Junction ◽  
Tactile Attention ◽  
Eye Field ◽  
Processing Mechanism

In recent years, there have been many studies on attention. These studies have found that there are two distinct kinds of neural networks employed for visual attention and tactile attention, respectively. This review summarizes the processing mechanism of these attention-related brain networks. One type is the top-down attention related brain structure, which includes the IPs/SPL (intraparietal sulcus/superior parietal lobule)-FEF (frontal eye field). The other is the bottom-up attention related brain structure, which includes the TPJ (temporoparietal junction)-VFC (ventral frontal cortex). Regarding research into tactile attention, in conclusion, the authors found that tactile attention had a similar neural network to that of visual attention in that there was top-down attention to the relevant IPs-FEF and bottom-up attention to the relevant TPJ-VFC.

scholarly journals Frontal Eye Field Activity Before Visual Search Errors Reveals the Integration of Bottom-Up and Top-Down Salience

2005 ◽  
Vol 93 (1) ◽  
pp. 337-351 ◽  
Author(s):  
Kirk G. Thompson ◽  
Narcisse P. Bichot ◽  
Takashi R. Sato
Keyword(s):  
Visual Search ◽  
Search Task ◽  
Target Selection ◽  
Saccade Target ◽  
Frontal Eye Field ◽  
Top Down ◽  
Bottom Up ◽  
Stimulus Response ◽  
Singleton Search ◽  
Eye Field

We investigated the saccade decision process by examining activity recorded in the frontal eye field (FEF) of monkeys performing 2 separate visual search experiments in which there were errors in saccade target choice. In the first experiment, the difficulty of a singleton search task was manipulated by varying the similarity between the target and distractors; errors were made more often when the distractors were similar to the target. On catch trials in which the target was absent the monkeys occasionally made false alarm errors by shifting gaze to one of the distractors. The second experiment was a popout color visual search task in which the target and distractor colors switched unpredictably across trials. Errors occurred most frequently on the first trial after the switch and less often on subsequent trials. In both experiments, FEF neurons selected the saccade goal on error trials, not the singleton target of the search array. Although saccades were made to the same stimulus locations, presaccadic activation and the magnitude of selection differed across trial conditions. The variation in presaccadic selective activity was accounted for by the variation in saccade probability across the stimulus–response conditions, but not by variations in saccade metrics. These results suggest that FEF serves as a saccade probability map derived from the combination of bottom-up and top-down influences. Peaks on this map represent the behavioral relevance of each item in the visual field rather than just reflecting saccade preparation. This map in FEF may correspond to the theoretical salience map of many models of attention and saccade target selection.

scholarly journals The Research of Bottom-Up and Top-Down Combination Visual Attention Calculative Method

2012 ◽  
Vol 29 ◽  
pp. 3520-3524
Author(s):  
Hui Wang ◽  
Gang Liu ◽  
Yuanyuan Dang
Keyword(s):  
Visual Attention ◽  
Top Down ◽  
Bottom Up

scholarly journals Visual attention strategies for target object detection

2021 ◽  
Author(s):  
◽  
Ibrahim Mohammad Hussain Rahman
Keyword(s):  
Visual Attention ◽  
Object Detection ◽  
Target Object ◽  
Detection Accuracy ◽  
Estimation Model ◽  
Top Down ◽  
Bottom Up ◽  
Feature Map ◽  
Low Level ◽  
Visual Tasks

<p>The human visual attention system (HVA) encompasses a set of interconnected neurological modules that are responsible for analyzing visual stimuli by attending to those regions that are salient. Two contrasting biological mechanisms exist in the HVA systems; bottom-up, data-driven attention and top-down, task-driven attention. The former is mostly responsible for low-level instinctive behaviors, while the latter is responsible for performing complex visual tasks such as target object detection.  Very few computational models have been proposed to model top-down attention, mainly due to three reasons. The first is that the functionality of top-down process involves many influential factors. The second reason is that there is a diversity in top-down responses from task to task. Finally, many biological aspects of the top-down process are not well understood yet.  For the above reasons, it is difficult to come up with a generalized top-down model that could be applied to all high level visual tasks. Instead, this thesis addresses some outstanding issues in modelling top-down attention for one particular task, target object detection. Target object detection is an essential step for analyzing images to further perform complex visual tasks. Target object detection has not been investigated thoroughly when modelling top-down saliency and hence, constitutes the may domain application for this thesis.  The thesis will investigate methods to model top-down attention through various high-level data acquired from images. Furthermore, the thesis will investigate different strategies to dynamically combine bottom-up and top-down processes to improve the detection accuracy, as well as the computational efficiency of the existing and new visual attention models. The following techniques and approaches are proposed to address the outstanding issues in modelling top-down saliency:  1. A top-down saliency model that weights low-level attentional features through contextual knowledge of a scene. The proposed model assigns weights to features of a novel image by extracting a contextual descriptor of the image. The contextual descriptor plays the role of tuning the weighting of low-level features to maximize detection accuracy. By incorporating context into the feature weighting mechanism we improve the quality of the assigned weights to these features.  2. Two modules of target features combined with contextual weighting to improve detection accuracy of the target object. In this proposed model, two sets of attentional feature weights are learned, one through context and the other through target features. When both sources of knowledge are used to model top-down attention, a drastic increase in detection accuracy is achieved in images with complex backgrounds and a variety of target objects.  3. A top-down and bottom-up attention combination model based on feature interaction. This model provides a dynamic way for combining both processes by formulating the problem as feature selection. The feature selection exploits the interaction between these features, yielding a robust set of features that would maximize both the detection accuracy and the overall efficiency of the system.  4. A feature map quality score estimation model that is able to accurately predict the detection accuracy score of any previously novel feature map without the need of groundtruth data. The model extracts various local, global, geometrical and statistical characteristic features from a feature map. These characteristics guide a regression model to estimate the quality of a novel map.  5. A dynamic feature integration framework for combining bottom-up and top-down saliencies at runtime. If the estimation model is able to predict the quality score of any novel feature map accurately, then it is possible to perform dynamic feature map integration based on the estimated value. We propose two frameworks for feature map integration using the estimation model. The proposed integration framework achieves higher human fixation prediction accuracy with minimum number of feature maps than that achieved by combining all feature maps.  The proposed works in this thesis provide new directions in modelling top-down saliency for target object detection. In addition, dynamic approaches for top-down and bottom-up combination show considerable improvements over existing approaches in both efficiency and accuracy.</p>

Psychology of attention

2012 ◽  
pp. 245-249
Author(s):  
Elizabeth Coulthard ◽  
Masud Husain
Keyword(s):  
Human Brain ◽  
Parietal Lobe ◽  
Inferior Frontal Gyrus ◽  
Good Deal ◽  
Anterior Cingulate ◽  
Frontal Eye Field ◽  
Temporoparietal Junction ◽  
Flexible Control ◽  
Eye Field ◽  
Brain Processing

Attention is generally taken to be the process by which people are able to concentrate on certain information or processes, while ignoring other events. It appears to be a fundamental attribute of human brain processing, although difficult to pin down in terms of mechanism. Psychologists have attempted to fractionate attention in many different ways, using ingenious behavioural paradigms. In this section we, too, will consider different aspects of attention: selective, phasic and sustained, divided and executive control of attention. However, it would be fair to say that all these aspects of attention do not normally operate in isolation. Instead they interact, and deficiencies in one aspect of attention, for example, in a patient population, often to do not occur in isolation. Functional imaging and lesion studies of attention have proliferated in recent years, attempting to place a neurobiological framework to these varied processes. In general, these studies also tend to confirm the view that attention is likely an emergent property of widespread brain networks, with a special emphasis on frontal and parietal regions of the human brain (Fig. 2.5.2.1). In this discussion we illustrate several aspects of attention with examples particularly from literature on visual attention, which is the most widely studied area, but it should be appreciated that many of the concepts discussed here extend to other domains. In fact, there is a good deal of evidence to suggest that several aspects of attention operate at a supra- or cross-modal level allowing integration of information from different sources. Recent studies suggest there are two fronto-parietal networks: (Fig. 2.5.2.1) a dorsal parieto-frontal network involving the superior parietal lobe (SPL) and dorsal frontal regions such as the frontal eye field (FEF); and a ventral network involving the inferior parietal lobe (IPL), temporoparietal junction (TPJ) and inferior frontal gyrus (IFG). In addition, dorsomedial frontal areas, including the anterior cingulate cortex (ACC) and pre-supplementary area (pre-SMA) may play a key role in flexible control of attention for strategic behaviour.

Neural substrates of visual percepts, imagery, and hallucinations

2002 ◽  
Vol 25 (2) ◽  
pp. 194-195
Author(s):  
Stephen Grossberg
Keyword(s):  
Visual Attention ◽  
Normal Control ◽  
Mental Imagery ◽  
Visual Information ◽  
Neural Substrates ◽  
Visual Hallucinations ◽  
Neural Models ◽  
Top Down ◽  
Bottom Up

Recent neural models clarify many properties of mental imagery as part of the process whereby bottom-up visual information is influenced by top-down expectations, and how these expectations control visual attention. Volitional signals can transform modulatory top-down signals into supra-threshold imagery. Visual hallucinations can occur when the normal control of these volitional signals is lost.

Dissection of early bottom-up and top-down deficits during visual attention in schizophrenia

2011 ◽  
Vol 122 (1) ◽  
pp. 90-98 ◽  
Author(s):  
Andres H. Neuhaus ◽  
Christine Karl ◽  
Eric Hahn ◽  
Niklas R. Trempler ◽  
Carolin Opgen-Rhein ◽  
...  
Keyword(s):  
Visual Attention ◽  
Top Down ◽  
Bottom Up

Visual Attention With Cognitive Aging

2019 ◽  
Author(s):  
David J. Madden ◽  
Zachary A. Monge
Keyword(s):  
Visual Attention ◽  
Visual Processing ◽  
Cognitive Aging ◽  
Brain Regions ◽  
Object Identification ◽  
Features Selection ◽  
Top Down ◽  
Bottom Up ◽  
Attentional Functioning ◽  
Age Related

Age-related decline occurs in several aspects of fluid, speed-dependent cognition, particularly those related to attention. Empirical research on visual attention has determined that attention-related effects occur across a range of information processing components, including the sensory registration of features, selection of information from working memory, controlling motor responses, and coordinating multiple perceptual and cognitive tasks. Thus, attention is a multifaceted construct that is relevant at virtually all stages of object identification. A fundamental theme of attentional functioning is the interaction between the bottom-up salience of visual features and top-down allocation of processing based on the observer’s goals. An underlying age-related slowing is prominent throughout visual processing stages, which in turn contributes to age-related decline in some aspects of attention, such as the inhibition of irrelevant information and the coordination of multiple tasks. However, some age-related preservation of attentional functioning is also evident, particularly the top-down allocation of attention. Neuroimaging research has identified networks of frontal and parietal brain regions relevant for top-down and bottom-up attentional processing. Disconnection among these networks contributes to an age-related decline in attention, but preservation and perhaps even increased patterns of functional brain activation and connectivity also contribute to preserved attentional functioning.

Frontal eye field: A cortical salience map

1999 ◽  
Vol 22 (4) ◽  
pp. 699-700 ◽  
Author(s):  
Kirk G. Thompson ◽  
Narcisse P. Bichot
Keyword(s):  
Visual Attention ◽  
Frontal Eye Field ◽  
Frontal Eye Fields ◽  
Eye Field ◽  
Salience Map ◽  
Saccade Generation

The concept of a salience map has become important for the development of theories of visual attention and saccade generation. Recent studies have shown that the frontal eye fields have all of the characteristics of a salience map.

Sign in / Sign up

Export Citation Format

Share Document