Object-based Visual Attention Research Articles

Left-Hemispheric Asymmetry for Object-Based Attention: an ERP Study.

It has been shown that selective attention enhances the activity in visual regions associated with stimulus processing. The left hemisphere seems to have a prominent role when non-spatial attention is directed towards specific stimulus features (e.g., color, spatial frequency). The present electrophysiological study investigated the time course and neural correlates of object-based attention, under the assumption of left-hemispheric asymmetry. Twenty-nine right-handed participants were presented with 3D graphic images representing the shapes of different object categories (wooden dummies, chairs, structures of cubes) which lacked detail. They were instructed to press a button in response to a target stimulus indicated at the beginning of each run. The perception of non-target stimuli elicited a larger anterior N2 component, which was likely associated with motor inhibition. Conversely, target selection resulted in an enhanced selection negativity (SN) response lateralized over the left occipito-temporal regions, followed by a larger centro-parietal P300 response. These potentials were interpreted as indexing attentional selection and categorization processes, respectively. The standardized weighted low-resolution electromagnetic tomography (swLORETA) source reconstruction showed the engagement of a fronto-temporo-limbic network underlying object-based visual attention. Overall, the SN scalp distribution and relative neural generators hinted at a left-hemispheric advantage for non-spatial object-based visual attention.

The emergence of object-based visual attention in infancy: A role for family socioeconomic status and competing visual features.

The development of spatial visual attention has been extensively studied in infants, but far less is known about the emergence of object-based visual attention. We tested 3-5- and 9-12-month-old infants on a task that allowed us to measure infants' attention orienting bias toward whole objects when they competed with color, motion, and orientation feature information. Infants' attention orienting to whole objects was affected by the dimension of the competing visual feature. Whether attention was biased toward the whole object or its salient competing feature (e.g., "ball" or "red") changed with age for the color feature, with infants biased toward whole objects with age. Moreover, family socioeconomic status predicted feature-based attention in the youngest infants and object-based attention in the older infants when color feature information competed with whole-object information.

Object-based Visual Attention Quantification using Head Orientation in VR Applications

Object-based Visual Attention Quantification using Head Orientation in VR Applications

The FVF might be influenced by object-based attention.

Hulleman & Olivers (H&O) argue that the primary unit in search should be fixations, and in doing so posit a Functional View Field (FVF). There is evidence from the object-based visual attention literature that the FVF may not process visual information uniformly. Here I sketch how object-based attention may influence processing within the FVF as well as the shape of the FVF.

Working memory capacity accounts for the ability to switch between object-based and location-based allocation of visual attention.

Bleckley, Durso, Crutchfield, Engle, and Khanna (Psychonomic Bulletin & Review, 10, 884-889, 2003) found that visual attention allocation differed between groups high or low in working memory capacity (WMC). High-span, but not low-span, subjects showed an invalid-cue cost during a letter localization task in which the letter appeared closer to fixation than the cue, but not when the letter appeared farther from fixation than the cue. This suggests that low-spans allocated attention as a spotlight, whereas high-spans allocated their attention to objects. In this study, we tested whether utilizing object-based visual attention is a resource-limited process that is difficult for low-span individuals. In the first experiment, we tested the uses of object versus location-based attention with high and low-span subjects, with half of the subjects completing a demanding secondary load task. Under load, high-spans were no longer able to use object-based visual attention. A second experiment supported the hypothesis that these differences in allocation were due to high-spans using object-based allocation, whereas low-spans used location-based allocation.

Distinct mechanisms subserve location- and object-based visual attention.

Visual attention can be allocated to either a location or an object, named location- or object-based attention, respectively. Despite the burgeoning evidence in support of the existence of two kinds of attention, little is known about their underlying mechanisms in terms of whether they are achieved by enhancing signal strength or excluding external noises. We adopted the noise-masking paradigm in conjunction with the double-rectangle method to probe the mechanisms of location-based attention and object-based attention. Two rectangles were shown, and one end of one rectangle was cued, followed by the target appearing at (a) the cued location; (b) the uncued end of the cued rectangle; and (c) the equal-distant end of the uncued rectangle. Observers were required to detect the target that was superimposed at different levels of noise contrast. We explored how attention affects performance by assessing the threshold versus external noise contrast (TvC) functions and fitted them with a divisive inhibition model. Results show that location-based attention – lower threshold at cued location than at uncued location – was observed at all noise levels, a signature of signal enhancement. However, object-based attention – lower threshold at the uncued end of the cued than at the uncued rectangle – was found only in high-noise conditions, a signature of noise exclusion. Findings here shed a new insight into the current theories of object-based attention.

A Computational Model of Visual Attention Based on Space and Object

Object-based visual attention has got more and more attention in image processing. A computational model of visual attention based on space and object is proposed in this study. Firstly spatial visual saliency of each pixel is calculated and edges of the input image are extracted. Salient edges are obtained according to the visual saliency of each edge. Secondly, a graph-based clustering process is done to get the homogeneity regions of the image. Then the most salient homogeneity regions are extracted based on their spatial visual saliency. Perceptual objects can be extracted by combining salient edges and salient regions. Attention value of each perceptual object is computed according to the area and saliency. Focus of attention is shifted among these perceptual objects in terms of the attention value. The proposed computational model was tested on lots of natural images. Experiment results indicate that our model is valid and effective.

Online decoding of object‐based attention using real‐time fMRI

Visual attention is used to selectively filter relevant information depending on current task demands and goals. Visual attention is called object-based attention when it is directed to coherent forms or objects in the visual field. This study used real-time functional magnetic resonance imaging for moment-to-moment decoding of attention to spatially overlapped objects belonging to two different object categories. First, a whole-brain classifier was trained on pictures of faces and places. Subjects then saw transparently overlapped pictures of a face and a place, and attended to only one of them while ignoring the other. The category of the attended object, face or place, was decoded on a scan-by-scan basis using the previously trained decoder. The decoder performed at 77.6% accuracy indicating that despite competing bottom-up sensory input, object-based visual attention biased neural patterns towards that of the attended object. Furthermore, a comparison between different classification approaches indicated that the representation of faces and places is distributed rather than focal. This implies that real-time decoding of object-based attention requires a multivariate decoding approach that can detect these distributed patterns of cortical activity.

Development and Evaluation of Object-Based Visual Attention for Automatic Perception of Robots

Bottom-up visual attention is an automatic behavior to guide visual perception to a conspicuous object in a scene. This paper develops a new object-based bottom-up attention (OBA) model for robots. This model includes four modules: Extraction of preattentive features, preattentive segmentation, estimation of space-based saliency, and estimation of proto-object-based saliency. In terms of computation, preattentive segmentation serves as a bridge to connect the space-based saliency and object-based saliency. This paper therefore proposes a preattentive segmentation algorithm, which is able to self-determine the number of proto-objects, has low computational cost, and is robust in a variety of conditions such as noise and spatial transformations. Experimental results have shown that the proposed OBA model outperforms space-based attention model and other object-based attention methods in terms of accuracy of attentional selection, consistency under a series of noise settings and object completion.

Fragmented Perception: Slower Space-Based but Faster Object-Based Attention in Recent-Onset Psychosis with and without Schizophrenia

BackgroundSchizophrenia is associated with impairments of the perception of objects, but how this affects higher cognitive functions, whether this impairment is already present after recent onset of psychosis, and whether it is specific for schizophrenia related psychosis, is not clear. We therefore tested the hypothesis that because schizophrenia is associated with impaired object perception, schizophrenia patients should differ in shifting attention between objects compared to healthy controls. To test this hypothesis, a task was used that allowed us to separately observe space-based and object-based covert orienting of attention. To examine whether impairment of object-based visual attention is related to higher order cognitive functions, standard neuropsychological tests were also administered.MethodPatients with recent onset psychosis and normal controls performed the attention task, in which space- and object-based attention shifts were induced by cue-target sequences that required reorienting of attention within an object, or reorienting attention between objects.ResultsPatients with and without schizophrenia showed slower than normal spatial attention shifts, but the object-based component of attention shifts in patients was smaller than normal. Schizophrenia was specifically associated with slowed right-to-left attention shifts. Reorienting speed was significantly correlated with verbal memory scores in controls, and with visual attention scores in patients, but not with speed-of-processing scores in either group.Conclusionsdeficits of object-perception and spatial attention shifting are not only associated with schizophrenia, but are common to all psychosis patients. Schizophrenia patients only differed by having abnormally slow right-to-left visual field reorienting. Deficits of object-perception and spatial attention shifting are already present after recent onset of psychosis. Studies investigating visual spatial attention should take into account the separable effects of space-based and object-based shifting of attention. Impaired reorienting in patients was related to impaired visual attention, but not to deficits of processing speed and verbal memory.

Action Video Games Make Dyslexic Children Read Better

Learning to read is extremely difficult for about 10% of children; they are affected by a neurodevelopmental disorder called dyslexia [1, 2]. The neurocognitive causes of dyslexia are still hotly debated [3-12]. Dyslexia remediation is far from being fully achieved [13], and the current treatments demand high levels of resources [1]. Here, we demonstrate that only 12 hr of playing action video games-not involving any direct phonological or orthographic training-drastically improve the reading abilities of children with dyslexia. We tested reading, phonological, and attentional skills in two matched groups of children with dyslexia before and after they played action or nonaction video games for nine sessions of 80 min per day. We found that only playing action video games improved children's reading speed, without any cost in accuracy, more so than 1 year of spontaneous reading development and more than or equal to highly demanding traditional reading treatments. Attentional skills also improved during action video game training. It has been demonstrated that action video games efficiently improve attention abilities [14, 15]; our results showed that this attention improvement can directly translate into better reading abilities, providing a new, fast, fun remediation of dyslexia that has theoretical relevance in unveiling the causal role of attention in reading acquisition.

Object-based visual attention in 8-month-old infants: Evidence from an eye-tracking study.

Visual attention is one of the infant's primary tools for gathering relevant information from the environment for further processing and learning. The space-based component of visual attention in infants has been widely investigated; however, the object-based component of visual attention has received scarce interest. This scarcity is surprising, given the importance of objects in driving infants' attention and predispositions to attend to object information. Here, we investigated the object-based component of attention in 8-month-old infants. An eye tracker measured the saccade latencies to find a target that could appear in a previously cued end of 2 bars (valid targets), in the other end of the cued bar (invalid same-object targets), or in the other bar but at the same distance from the cue (invalid different-object targets). Bars were unoccluded or partly occluded; if attention is object based, it should also operate on objects that require perceptual completion. After verifying in a sample of adults (Experiment 1) that a measure of saccade latency suitably assessed space-based and object-based attention, we tested 8-month-old infants (Experiment 2) using the same procedure. The results showed that in both adults and infants, target detection was faster for valid targets than for invalid ones (space-based effect). Moreover, for both the unoccluded and partly occluded conditions, detection was faster on invalid within-object trials than on invalid between-objects trials (object-based effect). These findings demonstrate that visual objects can operate as units of attention for infants by the age of 8 months, offering implications for cognitive development.

A Computational Model for Object-based Visual Attention

Computational model for object-based visual attention is a hot research field. A computational model of object-based visual attention is proposed in this paper. This model mainly includes three key technologies which are perceptual objects extraction, attention value calculation of perceptual objects and focus of attention shift. Firstly spatial visual saliency of each pixel is calculated and a saliency map is generated. The most salient homogeneity regions are extracted based on their spatial visual saliency. The extracted salient regions can be considered as perceptual objects. Attention value of each perceptual object is computed according to its area, location and saliency. Then a hierarchical attention shift method is used in the proposed model. The proposed model has been tested on many natural images. Experiment results show that the proposed model is valid and the attention results are consistent with human visual system.

Foveal Attention and Inhibition of Return: A Model for the Generation of Perceptual Scan Paths

A neurobiological model for object-based visual attention of the ventral visual pathway and its mathematical formulation is presented. The attention matrix comprises the pulvinar for attentional selection, the superior colliculus for the selection of a saccade target, the frontal eye field as a working memory map for inhibition of return, and the posterior parietal cortex as a spatial map for representing locations in a head-centered coordinate frame. The model is applied to covert attentive picture perception.

Perceptual Object Extraction Based on Saliency and Clustering

Object-based visual attention has received an increasing interest in recent years. Perceptual object is the basic attention unit of object-based visual attention. The definition and extraction of perceptual objects is one of the key technologies in object-based visual attention computation model. A novel perceptual object definition and extraction method is proposed in this paper. Based on Gestalt theory and visual feature integration theory, perceptual object is defined using homogeneity region, salient region and edges. An improved saliency map generating algorithm is employed first. Based on the saliency map, salient edges are extracted. Then graph-based clustering algorithm is introduced to get homogeneity regions in the image. Finally an integration strategy is adopted to combine salient edges and homogeneity regions to extract perceptual objects. The proposed perceptual object extraction method has been tested on lots of natural images. Experiment results and analysis are presented in this paper also. Experiment results show that the proposed method is reasonable and valid.

A biologically inspired object-based visual attention model

A biologically inspired object-based visual attention model is proposed in this paper. This model includes a training phase and an attention phase. In the training phase, all training targets are fused into a target class and all training backgrounds are fused into a background class. Weight vector is computed as the ratio of the mean target class saliency and the mean background class saliency for each feature. In the attention phase, for an attended scene, all feature maps are combined into a top-down salience map with the weight vector by a hierarchy method. Then, top-down and bottom-up salience map are fused into a global salience map which guides the visual attention. At last, the size of each salient region is obtained by maximizing entropy. The merit of our model is that it can attend a class target object which can appear in the corresponding background class. Experimental results indicate that: when the attended target object doesn't always appear in the background corresponding to that in the training images, our proposed model is excellent to Navalpakkam's model and the top-down approach of VOCUS.

The time course of neural activity in object-based visual attention

The time course of neural activity in object-based visual attention

An Object-Based Visual Attention Model for Robotic Applications

By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.

Online learning of task-driven object-based visual attention control

We propose a biologically-motivated computational model for learning task-driven and object-based visual attention control in interactive environments. In this model, top-down attention is learned interactively and is used to search for a desired object in the scene through biasing the bottom-up attention in order to form a need-based and object-driven state representation of the environment. Our model consists of three layers. First, in the early visual processing layer, most salient location of a scene is derived using the biased saliency-based bottom-up model of visual attention. Then a cognitive component in the higher visual processing layer performs an application specific operation like object recognition at the focus of attention. From this information, a state is derived in the decision making and learning layer. Top-down attention is learned by the U-TREE algorithm which successively grows an object-based binary tree. Internal nodes in this tree check the existence of a specific object in the scene by biasing the early vision and the object recognition parts. Its leaves point to states in the action value table. Motor actions are associated with the leaves. After performing a motor action, the agent receives a reinforcement signal from the critic. This signal is alternately used for modifying the tree or updating the action selection policy. The proposed model is evaluated on visual navigation tasks, where obtained results lend support to the applicability and usefulness of the developed method for robotics.

Content-Based Attention Ranking Using Visual and Contextual Attention Model for Baseball Videos

The attention analysis of multimedia data is challenging since different models have to be constructed according to different attention characteristics. This paper analyzes how people are excited about the watched video content and proposes a content-driven attention ranking strategy which enables client users to iteratively browse the video according to their preference. The proposed attention rank (AR) algorithm, which is extended from the Google PageRank algorithm that sorts the websites based on the importance, can effectively measure the user interest (UI) level for each video frame. The degree of attention is derived by integrating the object-based visual attention model (VAM) with the contextual attention model (CAM), which not only can more reliably take advantage of the human perceptual characteristics, but also can effectively identify which video content may attract users' attention. The information of users' feedback is utilized in re-ranking procedure to further improve the retrieving accuracy. The proposed algorithm is specifically evaluated on broadcasted baseball videos.