Attentive Tracking Research Articles

Attention enhances short-term monocular deprivation effect.

Patching one eye of an adult human for a few hours has been found to promote the dominance of the patched eye, which is called short-term monocular deprivation effect. Interestingly, recent work has reported that prolonged eye-specific attention can also cause a shift of ocular dominance toward the unattended eye though visual inputs during adaptation are balanced across the eyes. Considering that patching blocks all input information from one eye, attention is presumably deployed to the opposite eye. Therefore, the short-term monocular deprivation effect might be, in part, mediated by eye-specific attentional modulation. Yet this question remains largely unanswered. To address this issue, here we asked participants to perform an attentive tracking task with one eye patched. During the tracking, participants were presented with both target gratings (attended stimuli) and distractor gratings (unattended stimuli) that were distinct from each other in fundamental visual features. Before and after one hour of tracking, they completed a binocular rivalry task to measure perceptual ocular dominance. A larger shift of ocular dominance toward the deprived eye was observed when the binocular rivalry testing gratings shared features with the target gratings during the tracking compared to when they shared features with the distractor gratings. This result, for the first time, suggests that attention can boost the strength of the short-term monocular deprivation effect. Therefore, the present study sheds new light on the role of attention in ocular dominance plasticity.

Spatiotemporal processing drives the contralateral delay activity in a dual working memory and attentional tracking task

Spatiotemporal processing drives the contralateral delay activity in a dual working memory and attentional tracking task

How does colour predict multiple object tracking performance? The role of surface feature in attentive tracking

ABSTRACT Simultaneously tracking multiple unique dynamic objects, such as watching soccer games, is common in everyday life. However, the role of intragroup and intergroup features in this cognitive process remains unclear. We set up different colour combinations to investigate the effect of distinctiveness between target and distractor sets (DTD), the complexity of the target set (CT) and distractor set (CD) on multiple object tracking (MOT) on PCs and iPads, and used multiple linear regression to fit the relationship between these factors and tracking performance. The models support our hypothesis, showing that DTD contributes most to the interpretation of tracking performance, followed by CT, while CD has no effect. Furthermore, CT effect is modulated by DTD level. This study demonstrates that DTD and CT jointly contribute to predicting MOT performance regardless of devices. Thus, it would provide the theoretical basis for the future development of MOT-based cognitive applications on mobile devices.

Facial Feature-Based Attention Tracking System for Enhanced Online Learning Engagement

Recognizing and enhancing student engagement is crucial for improving learning outcomes, particularly in the context of online classes where monitoring can be challenging. Traditional methods of attendance tracking, such as calling out names, are impractical and susceptible to manipulation in the virtual environment. Students might appear 'online' without actively participating, and the absence of video feeds makes it difficult for teachers to verify attendance and attention. In order to realize a highly efficient and robust attendance management and engagement level prediction system for online learning, In the proposed System, the learner’s face is monitored by a video camera while attending a video lecture. Facial features were analyzed to predict reaction time (RT) to a task-irrelevant stimulus, which was assumed to be an index of the level of attention. Then apply a machine learning method, light Gradient Boosting Machine (LightGBM), to estimate RTs from facial features extracted as action units (AUs) corresponding to facial muscle movements by an open-source software (OpenFace). This project is to develops a user-friendly system integrated with private online learning and attendance recording system for teachers that can automatically record students ‘engagement state and attendance then generate attendance reports for online classrooms. It encompasses a novel design using the AI based FFCNN (Face Fiducial Convolution Neural Network) model to capture face biometric randomly from students’ video stream and record their attendance automatically. This integrated solution not only streamlines attendance management but also provides valuable insights into students' engagement levels through facial feature analysis.

Psychometric properties of the Arabic version of the everyday memory questionnaire - revised (EMQ-R) among the Lebanese population

Objective: This study aimed to evaluate the psychometric properties of the Arabic version of the Everyday Memory Questionnaire- Revised (EMQ-R-A) in a sample of Lebanese adults. Methods: A cross-sectional study was conducted in August 2023. A diverse sample of 483 Lebanese adults aged 18 to 60 years from all Lebanese governorates was recruited. Participants completed an Arabic-language questionnaire comprising three sections: socio-demographic characteristics, health-related inquiries, the EMQ-R-A and the Arabic version of the Prospective and Retrospective Memory Questionnaire (PRMQ). Results: The study’s findings indicated that the EMQ-R-A displayed strong internal consistency (Cronbach’s alpha = 0.925). Convergent validity was supported by a significant correlation with PRMQ. Test-retest results demonstrated strong reliability with a Cronbach’s alpha of 0.925. Confirmatory factor analysis revealed a three-factor model including retrieval related factors, attentional tracking related factors, and other factors. The factors labeled “Retrieval” and “Attentional Tracking” elucidate aspects of memory retrieval system efficacy and attention-related challenges. Higher EMQ-R-A scores were found to be associated with the female gender, lower attention, physical inactivity, lower educational levels, and higher number of comorbid disorders. Conclusion: The EMQ-R-A exhibits good validity and reliability. The identified factors associated with memory decline underscore the importance of addressing lifestyle factors, such as promoting physical activity, better educational attainment, and addressing comorbid health conditions, to potentially mitigate memory challenges.

Abstract TP40: Identification of Domain-Specific Cognitive Phenotypes of Post Stroke Vascular Contributions to Cognitive Impairment and Dementia

Introduction: Vascular contributions to cognitive impairment and dementia (VCID) are variably expressed following stroke. The purpose of this investigation is to identify the domain-specific cognitive phenotypes of VCID in the acute and sub-acute post-stroke interval measured using the Mark VCID battery. Methods: Mark VCID neurocognitive test battery was administered to 33 patients with incident ischemic stroke in the left (n=15), right (n=17), and bilateral (n=1) hemispheres including the anterior and middle cerebral artery territory, and 39 age and sex matched healthy controls (HC). Participants ranged in age from 42 to 88 years (Patient M=65.64 yrs, Control M=64.97 yrs). Patient group consisted of 17 males and 22 females, and the control group had 20 males and 13 females. Mean post stroke interval was 8 weeks (Range: 2.5 to 19 weeks). The test battery assessed: language (naming, fluency), perception (facial recognition, spatial orientation), psychomotor speed (digit symbol substitution), memory (word list, story recall), and attention (trails, number span). Normative test scores were converted to z-scores for assessment of between group differences using independent t-tests. Results: Patients with stroke performed significantly worse (p<0.05) than HCs on measures of language (semantic and phonemic fluency), perception (facial recognition and spatial orientation), and psychomotor speed (symbol digit modalities test) (Figure 1). There were no significant group differences in the scores of memory (Hopkins Verbal Learning, Craft Story) and attention tracking (Oral Trails A, Number Span). Conclusion: Cognitive abnormalities in these acute stoke patients represent impairments in diverse dominant (language) and non-dominant (perceptual/spatial) hemisphere cortical and subcortical (psychomotor speed) abilities. At the group level, the findings suggest a phenotype of VCID in patients with acute middle and anterior cerebral artery ischemic stroke.

A study of visual attention patterns of snow and ice athletes based on eye-tracking technology

Abstract The visual system has a strong information processing ability, and visual attention tracking has various applications in various scenes. This paper mainly focuses on the sports scene of ice and snow far mobilization. It constructs a visual attention system model based on eye tracking. It first establishes an eye tracking system framework using deep learning, and improves the gaze estimation by optimizing the feature extraction network. The visual attention system model was constructed using particle filtering based on motion feature cognition. In the eye-tracking visual attention system model experiments, the Accuracy of the improved eye-tracking system in this paper can be significantly improved to 1.13°, and the error of the visual attention system can be kept within 10°. Furthermore, the four ice and snow sports scene types have an average accuracy of 85.47%, and the constructed model performs well. This study offers a guide for effectively combining eye tracking technology and visual attention.

Attentional tracking within and across visual hemifields and brain hemispheres.

Attentional tracking within and across visual hemifields and brain hemispheres.

Cross-Correlation Fusion Graph Convolution-Based Object Tracking

Most popular graph attention networks treat pixels of a feature map as individual nodes, which makes the feature embedding extracted by the graph convolution lack the integrity of the object. Moreover, matching between a template graph and a search graph using only part-level information usually causes tracking errors, especially in occlusion and similarity situations. To address these problems, we propose a novel end-to-end graph attention tracking framework that has high symmetry, combining traditional cross-correlation operations directly. By utilizing cross-correlation operations, we effectively compensate for the dispersion of graph nodes and enhance the representation of features. Additionally, our graph attention fusion model performs both part-to-part matching and global matching, allowing for more accurate information embedding in the template and search regions. Furthermore, we optimize the information embedding between the template and search branches to achieve better single-object tracking results, particularly in occlusion and similarity scenarios. The flexibility of graph nodes and the comprehensiveness of information embedding have brought significant performance improvements in our framework. Extensive experiments on three challenging public datasets (LaSOT, GOT-10k, and VOT2016) show that our tracker outperforms other state-of-the-art trackers.

EEG-Based Focus of Attention Tracking and Regulation During Dual-Task Training for Neural Rehabilitation of Stroke Patients.

Dual-task training under variable-priority instructions (DT-VP), during which subjects are required to vary their focus of attention (FOA) between two concurrent tasks, has shown a more significant improvement in neural rehabilitation than that under fixed-priority instructions. Failed FOA switching not only diminishes the recovery benefits, but also causes anxieties, which is detrimental to rehabilitation. Developing a strategy for tracking and regulating patients' FOA to achieve a better performance in task priority-following during DT-VP is thus imperative. In this study, fifteen stroke patients participated in DT-VP that comprised two tasks: a mathematical problem-solving task and a cycling task, during which their electroencephalograms were recorded simultaneously. The significantly differentiated power spectra of four brain regions extracted from single-task training were fed into a support vector machine to build a FOA tracking algorithm for patients' attention assessment during the DT-VP. Moreover, dual-task difficulty adaptation method was designed to regulate patients' FOA when their FOA and the high-priority task were not coincident. The comparison experimental results showed that the proposed method significantly improved patients' FOA distributed on the high-priority task (analysis of variance, 0.05). Meanwhile, the absolute power spectral densities of the motor cortex and the frontal region could also be improved during DT-VP under high motor and cognitive task priority instructions, respectively. These phenomena demonstrated the feasibility of the proposed method in helping stroke patients better implement FOA switching and maintenance.

AttentionMNIST: a mouse-click attention tracking dataset for handwritten numeral and alphabet recognition

Multiple attention-based models that recognize objects via a sequence of glimpses have reported results on handwritten numeral recognition. However, no attention-tracking data for handwritten numeral or alphabet recognition is available. Availability of such data would allow attention-based models to be evaluated in comparison to human performance. We collect mouse-click attention tracking data from 382 participants trying to recognize handwritten numerals and alphabets (upper and lowercase) from images via sequential sampling. Images from benchmark datasets are presented as stimuli. The collected dataset, called AttentionMNIST, consists of a sequence of sample (mouse click) locations, predicted class label(s) at each sampling, and the duration of each sampling. On average, our participants observe only 12.8% of an image for recognition. We propose a baseline model to predict the location and the class(es) a participant will select at the next sampling. When exposed to the same stimuli and experimental conditions as our participants, a highly-cited attention-based reinforcement model falls short of human efficiency.

Nontarget Suppression in Ball Sports Multitarget-Tracking Task

In order to solve the problem of large interference caused by nontarget tasks on the ball sports field, the author proposes a method for multitarget-tracking tasks. By changing the movement speed of the athletes in the expert group, the novice group, and the control group, we investigate the performance of visual attention tracking and neural oscillatory characteristics in ice hockey players. Experimental results show that the main effect of speed was significant (F = 120.58,P<0.01,ηP2 = 0.81), and post hoc comparison results showed that the faster the movement speed, the lower the tracking accuracy. The main effect of speed was significant (F = 31.96,P<0.01,ηP2 = 0.53). Post hoc comparison results showed that the faster the movement speed, the lower the ERSP value of the high-frequency alpha rhythm. It proves the influence of speed on the inhibitory effect of nontarget tasks, which is beneficial for athletes to suppress more interference information brought by opponents or the court in the game and plays an important role in improving sports performance and developing the scientific level of sports.

Storage in Visual Working Memory Recruits a Content-Independent Pointer System

Past work has shown that storage in working memory elicits stimulus-specific neural activity that tracks the stored content. Here, we present evidence for a distinct class of load-sensitive neural activity that indexes items without representing their contents per se. We recorded electroencephalogram (EEG) activity while adult human subjects stored varying numbers of items in visual working memory. Multivariate analysis of the scalp topography of EEG voltage enabled precise tracking of the number of individuated items stored and robustly predicted individual differences in working memory capacity. Critically, this signature of working memory load generalized across variations in both the type and number of visual features stored about each item, suggesting that it tracked the number of individuated memory representations and not the content of those memories. We hypothesize that these findings reflect the operation of a capacity-limited pointer system that supports on-line storage and attentive tracking.

Effects of divided attention on visual control of steering toward a goal.

Daily locomotion tasks such as walking and driving often occur in a complex environment in which people frequently need to divide their attention to keep track of multiple moving objects. In this study, we examined the effects of divided attention on the visual control of goal-oriented locomotion by using a dual-task paradigm in which participants were instructed to steer a virtual vehicle toward a target while dividing their attention to a concurrent object tracking task. We found that divided attention reduced the accuracy of the early-stage control of steering and the precision of the steady-state control. We then instructed participants to rely on the target egocentric direction or optic flow cue for steering. Although we found similar negative effects of divided attention on the control of steering regardless of the cue instructions, divided attention adversely affected the accuracy of the steady-state control of steering only for participants who were instructed to rely on the optic flow cue. Furthermore, participants consistently showed lower attentional tracking accuracy when steering at a fast (15 m/s) than a low (2 m/s) speed. We conclude that divided attention negatively affects the control of steering toward a goal, and steering at higher travel speeds demands more attentional resource. Compared with optic flow, relying on target egocentric direction to steer toward a goal is less affected by a concurrent attention-demanding task, suggesting that target egocentric direction is the primary cue for the control of goal-oriented locomotion. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Enhanced Attention Tracking With Multi-Branch Network for Egocentric Activity Recognition

The emergence of wearable devices has opened up new potentials for egocentric activity recognition. Although some methods integrate attention mechanisms into deep neural networks to capture fine-grained human-object interactions in a weak-supervision manner, they either ignore exploiting the temporal consistency or generate attention based on considering appearance cues only. To address these limitations, in this paper, we propose an enhanced attention-tracking method, combined with multi-branch network (EAT-MBNet), for egocentric activity recognition. Specifically, we propose class-aware attention maps (CAAMs) by employing a self-attention-based module to refine the class activation maps (CAMs). Our proposed method can enhance the semantic dependency between the activity categories and the feature maps. To highlight the discriminative features from the regions of interest across frames, we propose a flow-guided attention-tracking (F-AT) module, by simultaneously leveraging historical attention and motion patterns. Furthermore, we propose a cross-modality modeling branch based on an interactive GRU module, which captures the time-synchronized long-term relationships between the appearance and motion branches. Experimental results on four egocentric activity benchmarks demonstrate that the proposed method achieves state-of-the-art performance.

Making sense of periodicity glimpses in a prediction-update-loop-A computational model of attentive voice tracking.

Humans are able to follow a speaker even in challenging acoustic conditions. The perceptual mechanisms underlying this ability remain unclear. A computational model of attentive voice tracking, consisting of four computational blocks: (1) sparse periodicity-based auditory features (sPAF) extraction, (2) foreground-background segregation, (3) state estimation, and (4) top-down knowledge, is presented. The model connects the theories about auditory glimpses, foreground-background segregation, and Bayesian inference. It is implemented with the sPAF, sequential Monte Carlo sampling, and probabilistic voice models. The model is evaluated by comparing it with the human data obtained in the study by Woods and McDermott [Curr. Biol. 25(17), 2238-2246 (2015)], which measured the ability to track one of two competing voices with time-varying parameters [fundamental frequency (F0) and formants (F1,F2)]. Three model versions were tested, which differ in the type of information used for the segregation: version (a) uses the oracle F0, version (b) uses the estimated F0, and version (c) uses the spectral shape derived from the estimated F0 and oracle F1 and F2. Version (a) simulates the optimal human performance in conditions with the largest separation between the voices, version (b) simulates the conditions in which the separation in not sufficient to follow the voices, and version (c) is closest to the human performance for moderate voice separation.

Paranormal Experience Profiles and Their Association With Variations in Executive Functions: A Latent Profile Analysis.

This study investigated relationships between inter-class variations in paranormal experience and executive functions. A sample of 516 adults completed self-report measures assessing personal encounter-based paranormal occurrences (i.e., Experience, Practitioner Visiting, and Ability), executive functions (i.e., General Executive Function, Working and Everyday Memory, and Decision Making) together with Emotion Regulation and Belief in the Paranormal. Paranormal belief served as a measure of convergent validity for experience-based phenomena. Latent profile analysis (LPA) combined experience-based indices into four classes based on sample subpopulation scores. Multivariate analysis of variance (MANOVA) then examined interclass differences. Results revealed that breadth of paranormal experience was associated with higher levels of executive functioning difficulties for General Executive Function, Working Memory, Decision Making, and Belief in the Paranormal. On the Everyday Memory Questionnaire, scores differed on Attention Tracking (focus loss) and Factor 3 (visual reconstruction), but not Retrieval (distinct memory failure). In the case of the Emotion Regulation Scale, class scores varied on Expressive Suppression (control), however, no difference was evident on Cognitive Reappraisal (reframing). Overall, inter-class comparisons identified subtle differences in executive functions related to experience. Since the present study was exploratory, sampled only a limited subset of executive functions, and used subjective, self-report measures, further research is necessary to confirm these outcomes. This should employ objective tests and include a broader range of executive functions.

Visual Attention and Cognitive Archaeology: An Eye-Tracking Study of Palaeolithic Stone Tools

The study of lithic technology can provide information on human cultural evolution. This article aims to analyse visual behaviour associated with the exploration of ancient stone artefacts and how this relates to perceptual mechanisms in humans. In Experiment 1, we used eye tracking to record patterns of eye fixations while participants viewed images of stone tools, including examples of worked pebbles and handaxes. The results showed that the focus of gaze was directed more towards the upper regions of worked pebbles and on the basal areas for handaxes. Knapped surfaces also attracted more fixation than natural cortex for both tool types. Fixation distribution was different to that predicted by models that calculate visual salience. Experiment 2 was an online study using a mouse-click attention tracking technique and included images of unworked pebbles and 'mixed' images combining the handaxe's outline with the pebble's unworked texture. The pattern of clicks corresponded to that revealed using eye tracking and there were differences between tools and other images. Overall, the findings suggest that visual exploration is directed towards functional aspects of tools. Studies of visual attention and exploration can supply useful information to inform understanding of human cognitive evolution and tool use.

How do humans group non-rigid objects in multipleobject tracking?: Evidence from grouping by self-rotation.

Previous studies on perceptual grouping found that people can use spatiotemporal and featural information to group spatially separated rigid objects into a unit while tracking moving objects. However, few studies have tested the role of objects' self-motion information in perceptual grouping, although it is of great significance to the motion perception in the three-dimensional space. In natural environments, objects always move in translation and rotation at the same time. The self-rotation of the objects seriously destroys objects' rigidity and topology, creates conflicting movement signals and results in crowding effects. Thus, this study sought to examine the specific role played by self-rotation information on grouping spatially separated non-rigid objects through a modified multiple object tracking (MOT) paradigm with self-rotating objects. Experiment 1 found that people could use self-rotation information to group spatially separated non-rigid objects, even though this information was deleterious for attentive tracking and irrelevant to the task requirements, and people seemed to use it strategically rather than automatically. Experiment 2 provided stronger evidence that this grouping advantage did come from the self-rotation per se rather than surface-level cues arising from self-rotation (e.g. similar 2D motion signals and common shapes). Experiment 3 changed the stimuli to more natural 3D cubes to strengthen the impression of self-rotation and again found that self-rotation improved grouping. Finally, Experiment 4 demonstrated that grouping by self-rotation and grouping by changing shape were statistically comparable but additive, suggesting that they were two different sources of the object information. Thus, grouping by self-rotation mainly benefited from the perceptual differences in motion flow fields rather than in deformation. Overall, this study is the first attempt to identify self-motion as a new feature that people can use to group objects in dynamic scenes and shed light on debates about what entities/units we group and what kinds of information about a target we process while tracking objects.

Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG

Objective. Neuro-steered assistive technologies have been suggested to offer a major advancement in future devices like neuro-steered hearing aids. Auditory attention decoding (AAD) methods would in that case allow for identification of an attended speaker within complex auditory environments, exclusively from neural data. Decoding the attended speaker using neural information has so far only been done in controlled laboratory settings. Yet, it is known that ever-present factors like distraction and movement are reflected in the neural signal parameters related to attention. Approach. Thus, in the current study we applied a two-competing speaker paradigm to investigate performance of a commonly applied electroencephalography-based AAD model outside of the laboratory during leisure walking and distraction. Unique environmental sounds were added to the auditory scene and served as distractor events. Main results. The current study shows, for the first time, that the attended speaker can be accurately decoded during natural movement. At a temporal resolution of as short as 5 s and without artifact attenuation, decoding was found to be significantly above chance level. Further, as hypothesized, we found a decrease in attention to the to-be-attended and the to-be-ignored speech stream after the occurrence of a salient event. Additionally, we demonstrate that it is possible to predict neural correlates of distraction with a computational model of auditory saliency based on acoustic features. Significance. Taken together, our study shows that auditory attention tracking outside of the laboratory in ecologically valid conditions is feasible and a step towards the development of future neural-steered hearing aids.