Visual Task Performance Research Articles

The Impact of Ruminative Thinking on Verbal and Visual Task Performance

Research shows the detrimental effects of rumination on individuals’ cognitive performance. However, there is limited research regarding rumination interference on task performance. The aim of this study was to investigate the link between rumination and cognition and explore if its impact is better described by Baddeley’s working memory model or Borkovec’s cognitive avoidance theory of worry. A laboratory experiment was conducted comparing three ruminative valence groups (negative, neutral, and positive). The aim was to explore individuals’ performance on a verbal and visual free-recall task and determine interference effects. Task scores pre- and post-rumination were collected from 69 university student participants and two 3 × 2 ANOVAs were conducted. The results suggest rumination may not interfere with individuals’ verbal and visual task performance, indicating the link between rumination and cognition may not be as significant as previously suggested. Furthermore, the results suggest that Baddeley’s model is a better description of rumination impacts on task performance compared to Borkovec’s theoretical model.

Autistic traits are associated with enhanced working memory capacity for abstract visual stimuli

We tested whether the association between autistic traits and enhanced performance in visual-perceptual tasks extends to visual working memory capacity. We predicted that any positive effect of autistic traits on visual working memory performance would be greatest during domain-specific tasks, in which visual resources must be relied upon. We used a visual ‘matrix’ task, involving recall of black-and-white chequered patterns which increased in size, to establish participants' capacity (span). We assessed 144 young adults' (M = 22.0 years, SD = 2.5) performance on abstract, ‘low semantic’ versus ‘high semantic’ task versions. The latter offered multimodal coding due to the availability of long-term memory resources that could supplement visual working memory. Participants also completed measures of autistic traits and trait anxiety. Autistic traits, especially Attention to Detail, Attention Switching, and Communication, positively predicted visual working memory capacity, specifically in the low semantic task, which relies on visual working memory resources. Autistic traits are therefore associated with enhanced processing and recall of visual information. The benefit is removed, however, when multimodal coding may be incorporated, emphasising the visual nature of the benefit. Strengths in focused attention to detail therefore appear to benefit domain-specific visual working memory task performance.

Earcons to reduce mode confusions in partially automated vehicles: Development and application of an evaluation method

Partially automated vehicles have systems that can ensure lateral and longitudinal control through adaptive cruise control and lane centering assist, meaning that there are three possible levels (modes) of automation: manual driving, automated longitudinal control, and automated lateral and longitudinal control. Confusions can occur when drivers fail to perceive or comprehend the automation mode indicated by the interface. Earcons reflecting the hierarchy of these automation modes through the use of pitch, rhythm, and number of note variations could improve mode awareness. The goal of the present study was therefore to develop and test a method for assessing the efficiency of earcons as indicators of automation mode, based on a mode awareness model. This method consisted in checking during a controlled laboratory situation, that the earcons were perceived correctly (Experiment 1), comprehended in isolation (Experiment 2), and comprehended during the performance of a parallel visual task that mimicked the visual demand of driving with partial automation and having to react to changes of visual icons (Experiment 3). This method was applied to earcons indicating the hierarchy of automation modes. The results of these experiments indicated that the earcons were efficiently perceived. They were comprehended in isolation and had a provoked a small decrement to a visual task. The parameters used here to create earcons for the purpose of reducing mode confusions were therefore validated to future simulator studies. The method for assessing them could be applied elsewhere, to ensure that other auditory signals adequately contribute to all dimensions of mode awareness before being integrated to a vehicle.

Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting.

Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman performance in visual recognition tasks. However, CNNs can easily be fooled by adversarial examples (AEs), i.e., maliciously crafted images that force the networks to predict an incorrect output while being extremely similar to those for which a correct output is predicted. Regular AEs are not robust to input image transformations, which can then be used to detect whether an AE is presented to the network. Nevertheless, it is still possible to generate AEs that are robust to such transformations. This article extensively explores the detection of AEs via image transformations and proposes a novel methodology, called defense perturbation, to detect robust AEs with the same input transformations the AEs are robust to. Such a defense perturbation is shown to be an effective counter-measure to robust AEs. Furthermore, multinetwork AEs are introduced. This kind of AEs can be used to simultaneously fool multiple networks, which is critical in systems that use network redundancy, such as those based on architectures with majority voting over multiple CNNs. An extensive set of experiments based on state-of-the-art CNNs trained on the Imagenet dataset is finally reported.

Presaccadic attention sharpens visual acuity

Visual perception is limited by spatial resolution, the ability to discriminate fine details. Spatial resolution not only declines with eccentricity but also differs for polar angle locations around the visual field, also known as ‘performance fields'. To compensate for poor peripheral resolution, we make rapid eye movements—saccades—to bring peripheral objects into high-acuity foveal vision. Already before saccade onset, visual attention shifts to the saccade target location and prioritizes visual processing. This presaccadic shift of attention improves performance in many visual tasks, but whether it changes resolution is unknown. Here, we investigated whether presaccadic attention sharpens peripheral spatial resolution; and if so, whether such effect interacts with performance fields asymmetries. We measured acuity thresholds in an orientation discrimination task during fixation and saccade preparation around the visual field. The results revealed that presaccadic attention sharpens acuity, which can facilitate a smooth transition from peripheral to foveal representation. This acuity enhancement is similar across the four cardinal locations; thus, the typically robust effect of presaccadic attention does not change polar angle differences in resolution.

Development of a quantitative measurement on visual clutter in see through display.

With the wide use of transmission displays to improve operation performance, the display information highlights clutter because of the contradiction between the massive amount of information and limited display area. Our study aimed to develop a quantitative measurement for declutter design and appraisal. Using the ergonomics research system of characters and symbols in a see-through cockpit display, we set the simulated flight task interface at four pixel scale levels by enlarging all the display elements in a certain ratio. Flight task videos of 12 clutter degrees were recorded using each flight interface matched with three flight scene complexity levels. A total of 60 pilots completed the visual search tasks in the flight task video while the eye tracker was used to record the view path in real time. Visual search performance was analyzed to study the effect of various clutter factors and levels on pilots' performance in visual search tasks, and acquire quantitative clutter measure parameters. GLM univariate test revealed that there were significant differences among the fixation time in areas of interest (AOI), total Fixation point number, total fixation time at four pixel scale levels, and three flight scene complexity levels (P < 0.05). Visual search performance declined after the cutoff point, while the clutter degree increased. According to the visual search performance data, the recommend feature congestion upper pixel number limit in a 600*800 display was 18,576, and the pixel ratio was 3.87%. A quantitative measurement for declutter design and appraisal of cockpit displays was developed, which can be used to support see-through display design.

Automatic Postoperative Brain Tumor Segmentation with Limited Data using Transfer Learning and Triplet Attention

Accurate brain tumor segmentation is clinically important for diagnosis and treatment planning. Convolutional neural networks (CNNs) have achieved promising performance in various visual recognition tasks. Training such networks usually requires large amount of labeled data, which is often challenging for medical applications. In this work, we address the segmentation problem by applying transfer learning to downstream segmentation tasks. Specifically, we explore how knowledge acquired from a large preoperative dataset can be transferred to postoperative tumor segmentation on a smaller dataset. To this end, we have developed a 3D CNN for brain tumor segmentation, and fine-tuned the pretrained models on the target domain data. To better exploit the inter-channel and spatial information, triplet attention has been incorporated and extended into existing segmentation network. Extensive experiments on our dataset demonstrate the effectiveness of transfer learning and attention modules for improved postoperative tumor segmentation performance when only limited amount of annotated data is available.

Effects of anti-fatigue lenses on performance, muscle activity and subjective discomfort responses during a seated computer task

Anti-fatigue lenses (AFL) intend to provide health benefits in association with computer work. Their effects on visual and muscle discomfort mechanisms and task performance remain unclear. Twenty-three computer users (n = 12 males) underwent two 90-min computer sessions with AFL or placebo lenses. Eye strain, body discomfort, typing performance, upper trapezius (UT) activation amplitude and variability, and neck posture were analyzed for time, condition, and sex effects. No significant effects of condition were observed on the dependent measures evaluated. Discomfort increased over time (neck/shoulder: p < .001), more so in females (eye strain: p < .001). Females’ UT activation amplitude was negatively correlated to eye strain in the placebo condition (p = .05).

A Robust Feature Downsampling Module for Remote-Sensing Visual Tasks

Remote sensing (RS) images present unique challenges for computer vision due to lower resolution, smaller objects, and fewer features. Mainstream backbone networks show promising results for traditional visual tasks. However, they use convolution to reduce feature map dimensionality, which can result in information loss for small objects in RS images and decreased performance. To address this problem, we propose a new and universal downsampling module named Robust Feature Downsampling (RFD). RFD fuses multiple feature maps extracted by different downsampling techniques, creating a more robust feature map with a complementary set of features. Leveraging this, we overcome the limitations of conventional convolutional downsampling, resulting in more accurate and robust analysis of RS images. We develop two versions of RFD module, Shallow RFD (SRFD) and Deep RFD (DRFD), tailored to adapt to different stages of feature capture and improve feature robustness. We replace the downsampling layers of existing mainstream backbones with RFD module and conduct comparative experiments on several public RS image datasets. The results show significant improvements compared to baseline approaches in RS image classification, object detection, and semantic segmentation. Specifically, our RFD module achieved an average performance gain of 1.5% on NWPU-RESISC45 classification dataset without utilizing any additional pretraining data, resulting in state-of-the-art performance on this dataset. Moreover, in detection and segmentation tasks on DOTA and iSAID datasets, our RFD module outperforms the baseline approaches by 2-7% when utilizing pretraining data from NWPU-RESISC45. These results highlight the value of RFD module in enhancing the performance of RS visual tasks.

Advanced RetinexNet: A fully convolutional network for low-light image enhancement

Capturing images in weak illumination environments seriously degrades image quality, such as low visibility, low contrast, artifacts, and noise. Solving a series of degradation of low-light images can effectively improve the visual quality of the image and enhance the performance of high-level visual tasks. Based on the Retinex theory, we propose a novel fully convolutional network to simultaneously solve these degradations and effectively improve the visual quality of the image. The entire network contains two sub-networks, the Decom-Net and the Enhance-Net, the image is decomposed into illumination and reflectance through Decom-Net, while noise in the reflectance is suppressed through our proposed frequency-domain noise suppression loss, and the Enhance-Net is proposed to improve the contrast of the illumination map. Our method can properly improve the contrast and suppress noise at the same time. Extensive experiments demonstrate that our method outperforms the existing state-of-the-art methods by a large margin both quantitatively and visually. In addition, our results showed that the performance of the high-level visual task (i.e., face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions.

A laboratory based study on the effect of peripheral flickering LED sources on reaction time of drivers for object recognition

The effects of peripheral source on On-axis visual task performance by human observers under different lighting conditions are very much identified. The effect of Color Temperature of the main and peripheral light sources also influences the driver’s performance in a real road. There arises a necessity to investigate the effect of peripheral glaring LED sources on driver’s task performance. In this paper a laboratory-based experiment is explained, which was carried out, to understand the effect of varying frequencies of peripheral flickering LED sources with varying chromaticity of main LED source on the reaction times of static human observers for test object recognition and to find the correlation among observers’ reaction times, main source’s chromaticity and flickering frequency. CCT is a suitable lighting parameter to experiment with as it can be directly correlated with chromaticity, a fundamental mark for a light source. It is found from the experiment that reaction time decreases as the CCT of main source increases irrespective of the increment of flickering frequency. Besides, it is also found that the introduction of flicker and change of flickering frequency have profound effects on human observers’ reaction time and flickering of peripheral glare sources increases the reaction time for object recognition. Moreover, it is also seen from the additional behavioral studies done on the participants in the same experimental setup that, the behavioral study supports the findings from reaction time measurement study.

Effects of a maximal cycling all-out anaerobic test on visual performance

ABSTRACT Clinical relevance All-out exercise may not impair all central nervous system processes, such as those related to visual-motor abilities, and may actually prove stimulatory to such tasks allowing athletes and sports conditioning specialists to develop strategies to take advantage of/mitigate the effects of such exercise on athletic performance. Background Despite research indicating that visual-motor abilities play a critical role in athletic performance, research has primarily focused on the effect of all-out exercise on processes along the motor pathway, such as resultant force production or simple cognitive tasks. Such research has neglected to investigate the effect of all-out exercise on visual tasks. When investigations on visual tasks are forthcoming, they focus on prolonged aerobic exercise, which is not the primary metabolic pathway for all, or even the majority of sports. Methods Sixty untrained males (experimental group; N = 30, control group; N = 30) completed a standardised six-item baseline sports vision test battery and one week later, the experimental participants returned to undertake a 30-second Wingate anaerobic test (30-WAnT) immediately followed by the same test battery. Results Significant (P < 0.05) improvements were found in accommodation facility, saccadic eye movement, speed of recognition, peripheral awareness and hand-eye coordination (P < 0.001 for all), but not visual memory (P = 0.242) following the 30-WAnT. Conclusions Although the mechanisms underlying these improvements in visual task performance have not yet been studied, this study suggests that simple anaerobic all-out exercise does not cause central- or brain-based fatigue impairing the oculomotor system but may rather provide “excitability” of the underlying motor cortex, motoneurons and/or corticofugal connections utilised in visual task response. It appears that the sweeping improvements in visual task performance elucidate the need for an intense anaerobic warm-up when training visual skills and when visual skills form an integral part of athletic performance.

R2RNet: Low-light image enhancement via Real-low to Real-normal Network

Images captured in weak illumination conditions could seriously degrade the image quality. Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual tasks. In this study, a novel Retinex-based Real-low to Real-normal Network (R2RNet) is proposed for low-light image enhancement, which includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net. These three subnets are used for decomposing, denoising, contrast enhancement and detail preservation, respectively. Our R2RNet not only uses the spatial information of the image to improve the contrast but also uses the frequency information to preserve the details. Therefore, our model achieved more robust results for all degraded images. Unlike most previous methods that were trained on synthetic images, we collected the first Large-Scale Real-World paired low/normal-light images dataset (LSRW dataset) to satisfy the training requirements and make our model have better generalization performance in real-world scenes. Extensive experiments on publicly available datasets demonstrated that our method outperforms the existing state-of-the-art methods both quantitatively and visually. In addition, our results showed that the performance of the high-level visual task (i.e., face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions. Our codes and the LSRW dataset are available at: https://github.com/JianghaiSCU/R2RNet.

Keeping balance during head-free smooth pursuit: The role of aging

Standing balance is often more unstable when visually pursuing a moving target than when fixating on a stationary one. These effects are common in both young and older adults when the head is restrained during visual task performance. The present study focused on the role of head motion on standing balance during smooth pursuit as a function of age. Three predictions were tested: a) standing balance is compromised to a greater extent in older than young adults by gaze target pursuit compared to fixation, b) older adults pursue a moving target with greater and more variable head rotation than young adults, and c) greater and more variable head rotation during the smooth pursuit task is associated with greater Center of Pressure (CoP) sway. Twenty-two (22) older (age: 71.7 ± 8.1, 12 M / 10 F) and twenty-three (23) young adults (age: 23.6 ± 2.5, 12 M / 11 F) stood on a force plate while either fixating a stationary or smoothly pursuing a horizontally moving target (31.9° peak-to-peak visual angle). CoP (Bertec Balance Plate), head kinematics (Vicon Motion Analysis) and head-unconstrained gaze (Pupil Labs Invisible) were synchronously recorded. The root means square (RMS) of CoP velocity increased during smooth pursuit compared to fixation regardless of age (p < .05), while the interquartile CoP range increased only in older and not in young participants (p < .05). We also calculated the head rotation range (peak to peak cycle amplitude) of motion and variability (SD of range of motion) across the cycles of the smooth pursuit task. Older adults pursued the moving target employing more variable (p = .022) head yaw rotation than young participants although the mean range of head rotation was similar between groups (p =. 077). The amplitude and variability of head yaw rotation did not correlate with CoP sway measures. Results suggest that head-free pursuing of a moving target decreased balance to a greater extent in old than young individuals when compared to fixation. Nevertheless, postural sway during head-free smooth pursuit was not associated with the extent or variability of head rotation.

Benefits associated with the standing position during visual search tasks.

The literature on postural control highlights that task performance should be worse in challenging dual tasks than in a single task, because the brain has limited attentional resources. Instead, in the context of visual tasks, we assumed that (i) performance in a visual search task should be better when standing than when sitting and (ii) when standing, postural control should be better when searching than performing the control task. 32 and 16 young adults participated in studies 1 and 2, respectively. They performed three visual tasks (searching to locate targets, free-viewing and fixating a stationary cross) displayed in small images (visual angle: 22°) either when standing or when sitting. Task performance, eye, head, upper back, lower back and center of pressure displacements were recorded. In both studies, task performance in searching was as good (and clearly not worse) when standing as when sitting. Sway magnitude was smaller during the search task (vs. other tasks) when standing but not when sitting. Hence, only when standing, postural control was adapted to perform the challenging search task. When exploring images, and especially so in the search task, participants rotated their head instead of their eyes as if they used an eye-centered strategy. Remarkably in Study 2, head rotation was greater when sitting than when standing. Overall, we consider that variability in postural control was not detrimental but instead useful to facilitate visual task performance. When sitting, this variability may be lacking, thus requiring compensatory movements.

Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data.

Electroencephalogram (EEG) is a popular tool for studying brain activity. Numerous statistical techniques exist to enhance understanding of the complex dynamics underlying the EEG recordings. Inferring the functional network connectivity between EEG channels is of interest, and non-parametric inference methods are typically applied. We propose a fully parametric model-based approach via cointegration analysis. It not only estimates the network but also provides further insight through cointegration vectors, which characterize equilibrium states, and the corresponding loadings, which describe the mechanism of how the EEG dynamics is drawn to the equilibrium. We outline the estimation procedure in the context of EEG data, which faces specific challenges compared with the common econometric problems, for which cointegration analysis was originally conceived. In particular, the dimension is higher, typically around 64; there is usually access to repeated trials; and the data are artificially linearly dependent through the normalization done in EEG recordings. Finally, we illustrate the method on EEG data from a visual task experiment and show how brain states identified via cointegration analysis can be utilized in further investigations of determinants playing roles in sensory identifications.

The effect of non-uniform urban illumination upon pedestrians’ alertness and sense of safety

The influence of urban lighting on the visual tasks of pedestrians are well known and, likewise, studies have explored the relationship between urban lighting and pedestrians’ feelings of safety. But there are other, less investigated, possible effects of light in mesopic situations. Research on street lighting for pedestrians has traditionally focused on minimal required illuminance for various visual performance tasks (e.g., obstacle detection and face recognition). However, it is not only pedestrians’ visual performance that is important for safety, but also their general attentiveness to the environment. Despite this, the psychological concepts related to the individuals’ attention, such as alertness, arousal and anxiety, have not been considered in street lighting research to date even though they too can play an important role in pedestrian’s attention and safety. Since proper metrics for these concepts are needed, we conducted a pilot study to test their sensitivity. We decided for a conceptual replication of a field experiment from 1916 and managed to partly replicate the findings. More importantly, we learned meaningful lessons from this endeavour. Implications for future research are discussed.

Aging and feature binding in visual working memory.

Older adults have reduced performance in visual working memory tasks in comparison to young adults, but the precipitators of the age-related impairment are not fully understood. The most common interpretation of this difference is that older adults are incapable of maintaining the same amount of object representations as young adults over short intervals (in line with the fixed-slot model of working memory). However, it has remained largely unexplored whether the age-related decline is only due to the number of representations that older individuals can retain in visual working memory, or whether the content of the representation(s) may have an effect as well (in line with the flexible-resource model of working memory). Feature binding studies represent an interesting research line to examine the content of older adults' representations. In this mini-review, we present the main results across feature binding studies in aging, as well as highlight the importance of manipulating both the representation content and number to have a stress test of the various models of working memory and their contribution to aging. Overall, feature binding studies, together with the simultaneous manipulation of set size, will allow us to better understand the nature of the age-related decline of visual working memory.

Indoor lighting standards and their role in lighting practice

Indoor lighting standards were first introduced as a means to ensure the efficient performance of visual tasks in workplaces. Their scope soon expanded to cover a wider range of applications as well as visual discomfort. They thus became a means to eliminate bad lighting, and in this they have been successful. However, now they are often seen as a restriction on the freedom of lighting practitioners to develop alternative designs. This is partly because standards have been issued for applications where there are no critical visual tasks, and partly because the conventional design procedure ensuring compliance with standards assumes a regular array of luminaires. Three steps are proposed for overcoming this problem. The first is to separate lighting recommendations into standards and guidance. Standards should be legally enforceable and devoted to eliminating bad lighting. Promotion of good lighting should be achieved by applications-specific guidance documents. The second is to change the metric used in standards from task illuminance to ambient illuminance, from lighting the task to lighting the space. The third is to change the design method used from the lumen method to the lighting design objectives procedure.

Progressive Rain Removal Based on the Combination Network of CNN and Transformer.

The rain removal method based on CNN develops rapidly. However, convolution operation has the disadvantages of limited receptive field and inadaptability to the input content. Recently, another neural network structure Transformer has shown excellent performance in natural language processing and advanced visual tasks by modeling global relationships, but Transformer has limitations in capturing local dependencies. To address the above limitations, we propose the combination network of CNN and Transformer, which fully combines the advantages of CNN and Transformer structure to complete the task of image restoration. We use CNN to provide preliminary output and adopt Transformer architecture to further optimize the output of CNN. In addition, by using some key designs in module connection, our model strengthens feature propagation and encourages feature reuse, allowing better information and gradient flow. The experimental results show that compared with the existing methods, our method can remove the rain lines more comprehensively and achieve the state-of-the-art results. Besides, the experimental results also demonstrate that the CNN structure can be effectively combined with Transformer to fully utilize the superiority of different structures.