Task Demands Research Articles

The role of task on the human brain's responses to, and representation of, visual regularity defined by reflection and rotation

Identifying and segmenting objects in an image is generally achieved effortlessly and is facilitated by the presence of symmetry: a principle of perceptual organisation used to interpret sensory inputs from the retina into meaningful representations. However, while imaging studies show evidence of symmetry selective responses across extrastriate visual areas in the human brain, whether symmetry is processed automatically is still under debate. We used functional Magnetic Resonance Imaging (fMRI) to study the response to and representation of two types of symmetry: reflection and rotation. Dot pattern stimuli were presented to 15 human participants (10 female) under stimulus-relevant (symmetry) and stimulus-irrelevant (luminance) task conditions. Our results show that symmetry-selective responses emerge from area V3 and extend throughout extrastriate visual areas. This response is largely maintained when participants engage in the stimulus irrelevant task, suggesting an automaticity to processing visual symmetry. Our multi-voxel pattern analysis (MVPA) results extend these findings by suggesting that not only spatial organisation of responses to symmetrical patterns can be distinguished from that of non-symmetrical (random) patterns, but also that representation of reflection and rotation symmetry can be differentiated in extrastriate and object-selective visual areas. Moreover, task demands did not affect the neural representation of the symmetry information. Intriguingly, our MVPA results show an interesting dissociation: representation of luminance (stimulus irrelevant feature) is maintained in visual cortex only when task relevant, while information of the spatial configuration of the stimuli is available across task conditions. This speaks in favour of the automaticity for processing perceptual organisation: extrastriate visual areas compute and represent global, spatial properties irrespective of the task at hand.

An integrated model of semantics and control.

Understanding the mechanisms enabling the learning and flexible use of knowledge in context-appropriate ways has been a major focus of research in the study of both semantic cognition and cognitive control. We present a unified model of semantics and control that addresses these questions from both perspectives. The model provides a coherent view of how semantic knowledge, and the ability to flexibly access and deploy that knowledge to meet current task demands, arises from end-to-end learning of the statistics of the environment. We show that the model addresses unresolved issues from both literatures, including how control operates over features that covary with one another and how control representations themselves are structured and emerge through learning, through a series of behavioral experiments and simulations. We conclude by discussing the implications of our approach to other fundamental questions in cognitive science, machine learning, and artificial intelligence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cyclic tensor singular value decomposition with applications in low-rank high-order tensor recovery

The rapid advancements in emerging technologies have increased the demand for recovery tasks involving high-dimensional data with complex structures. Effectively utilizing tensor decomposition techniques to capture the low-rank structure of such data is crucial. Recently, the high-order t-SVD has demonstrated strong adaptability. However, this decomposition approach can only capture the low-rank correlation of two modes along other modes individually, while disregarding the structural correlation between different modes. In this paper, we propose a novel cyclic tensor singular value decomposition (CTSVD) method that effectively characterizes the low-rank structures of high-order tensors along all modes. Specifically, our method decomposes an order-N tensor into N factor tensors and one core tensor, connecting them using a defined mode-k tensor-tensor product (t-product). Building upon this, we establish the corresponding tensor rank and its convex relaxation. To address the issue of dimensional imbalance between adjacent modes in high-dimensional data, we propose and integrate a square reshaping strategy into the recovery models for tensor completion (TC) and tensor principal component analysis (TRPCA) tasks. Effective alternating direction method of multipliers (ADMM)-based algorithms are designed to these tasks. Extensive experiments on both synthetic and real data demonstrate that our methods outperform state-of-the-art approaches.

Predictor Selection for Case Complexity Based on Routine Data for Needs-Based and Competence-Based Staff Planning.

Due to nursing staff shortage and growing nursing care demand, resource allocation and optimal task distribution have become primary concerns of nursing management. Grade mix analysis based on nursing interventions and nurse qualifications from routine patient documentation can support this. Case complexity is a key linking factor of nursing interventions, workload, and grade mix. This study determined case complexity predictors based on one year of routine patient documentation (n = 3,373 cases) from a Swiss hospital and predicted the patient clinical complexity level via weighted cumulative logistic regression models. Significant predictors were sex, age, pre-admission residence, admission type, self- care index, pneumonia risk, and number of nursing interventions. The models' accuracy is limited yet appropriate for applications such as needs- and competence- based staff-planning. After calibration via in-hospital data it could support nursing management in these tasks. The next step is now to test the model in a clinical setting.

Neural dynamics underlying minute-timescale persistent behavior in the human brain.

The ability to pursue long-term goals relies on a representations of task context that can both be maintained over long periods of time and switched flexibly when goals change. Little is known about the neural substrate for such minute-scale maintenance of task sets. Utilizing recordings in neurosurgical patients, we examined how groups of neurons in the human medial frontal cortex and hippocampus represent task contexts. When cued explicitly, task context was encoded in both brain areas and changed rapidly at task boundaries. Hippocampus exhibited a temporally dynamic code with fast decorrelation over time, preventing cross-temporal generalization. Medial frontal cortex exhibited a static code that decorrelated slowly, allowing generalization across minutes of time. When task context needed to be inferred as a latent variable, hippocampus encoded task context with a static code. These findings reveal two possible regimes for encoding minute-scale task-context representations that were engaged differently based on task demands.

The Effects of Mental Fatigue on Anaerobic Power and Power Endurance Performance.

Mental fatigue has been studied extensively in relation to its impact on aerobic-, strength-, and motor-based tasks, but anaerobic power-based tasks have received limited attention. Interdisciplinary research investigating the underlying mechanisms by which mental fatigue influences physical performance has been called for. In two studies, the effects of mental fatigue on maximal power jump and endurance jump performance as well as kinetics and kinematics during jump performance were examined. Samples of collegiate volleyball players (Study 1; N = 14) and recreationally active students (Study 2; N = 27) completed two 30 min experimental manipulations (high vs. low cognitive control exertion) before performing three maximal power squat jumps followed by 15 repeated countermovement jumps, with measurements of kinetics and kinematics. For Study 1, the maximal power performance was significantly lower under a mentally fatigued condition, but no differences were observed for repeated jump performance, which may have been attributable to alterations in jump mechanics. For Study 2, no between-condition differences were observed for the maximal power performance, although repeated jump performance was significantly lower under a mentally fatigued condition. Collectively, these findings suggest that the impacts of mental fatigue on power-based performance tasks may depend on the task demands as well as the training status of the individual.

A resource prediction method for air traffic cyber-physical-social system

Air traffic is exhibiting the characteristics of large flow, strong coupling, and high time variation. Therefore, the complex network of air traffic is more vulnerable to disturbances. When it is disturbed, the failure of some nodes spreads through dependency relationships in the network, resulting in cascade failure. In the event of a cascade failure, the network may quickly collapse until it is paralyzed, with widespread delays and flight cancellations. The current flow management and deployment methods still remain in the control-oriented stage, which is mainly completed by air traffic controls (ATCs), and lack of accurate flow adjustment and effective utilization of capacity. The whole air traffic system and its peripheral factors are intricate, so human and social factors must be integrated into the control and decision-making of the system. Considering engineering and social factors such as operation environment, social environment, personnel, rules, equipment, and information processing, we analyse the air traffic in a cyber-physical-social system (CPSS). To reflect the actual system behaviour rules, dynamic response, limit state, and so on, the corresponding computational experiment and comprehensive evaluation system are established. Based on neural networks and other technologies, a resource prediction scheme based on task demand is proposed for multi-dimensional resources such as airports, air routes, and ATC, to reduce the cost of system resource scheduling and improve resource utilization through resource prediction and adjustment. Finally, the accuracy of the proposed resource prediction algorithm is verified by theoretical analysis and simulation.

Debt Collection Model for Mass Receivables Based on Decision Rules—A Path to Efficiency and Sustainability

Debt collection companies buy overdue debts on the market in order to collect them and recover the highest possible amount of a debt. The pursuit of debt recovery by employees of collection agencies is a very demanding task. The aim of the article is to propose a rule-based model for managing the process of mass debt collection in a debt collection company, which will make the debt collection process more efficient. To achieve this, we have chosen a decision tree as a machine learning technique best suited for creating rules based on extensive data from the debt collection company. The classification accuracy of the decision tree, regardless of the possibility of acquiring rule-based knowledge, proved to be the highest among the tested machine learning methods, with an accuracy rate of 85.5%. Through experiments, we generated 16 stable rules to assist in the debt collection process. The proposed approach allows for the elimination of debts that are difficult to recover at the initial stage of the recovery process and to decide whether to pursue amicable debt collection or to escalate the debt recovery process to legal action. Our approach also enables the determination of specific actions during each stage of the proceedings. Abandoning certain actions or reducing their frequency will alleviate the burden on collection agency employees and help to avoid the typical burnout associated with this line of work. This is the path to making the organizational culture of a collection agency more sustainable. Our model also confirms the possibility of using data from debt collection companies to automatically generate procedural rules and automate the process of purchasing and collecting debts. However, this would require a larger set of attributes than what we currently possess.

Towards enhanced performance: an integrated framework of emotional valence, arousal, and task demand

Extensive evaluations exist concerning the linkage between objective task demands and subsequent effects on user performance. However, the human user also experiences a range of emotions related to external task demands. Problematically, little is known about the associations between emotional valence, and arousal associated with the task demand–performance axis. In this paper, we advance a theoretical model concerning such interactive influences using three dimensions: (1) emotional valence, (2) arousal, and (3) task demand. The model evaluates the impact of these dimensions on user performance. It also identifies critical emotional user states, particularly those resulting in negative performance effects, as well as non-critical emotional states that can positively impact performance. Finally, we discuss the implications for affect-adaptive systems that can mitigate the impact of critical emotional states while leveraging the benefits of non-critical ones.

Vertical slice equal sampling and transformer network for point cloud ground filtering

ABSTRACT Ground filtering is a critical yet challenging aspect of Digital Elevation Model (DEM) generation from LiDAR point clouds. Existing deep learning approaches for point cloud processing encounter difficulties in addressing the conflict between the demands of large-scale object filtering tasks and GPU memory. In response to this challenge, this study presents a novel ground filtering method utilizing deep learning. The proposed method incorporates Vertical Slicing Equally Sampling (VSES) to locally sample the original point cloud, effectively organizing the unordered sequence of points and reducing their number while maintaining the representation of the terrain. Subsequently, the resampled point cloud is fed into the TransGF network for classification processing. TransGF is a network model that leverages Transformer as its backbone. Initially, TransGF employs a linear layer to project the input data from a low-dimensional space of (n, 3) to a high-dimensional space of (n, 32). It then extracts features using a module comprising six consecutive Transformer blocks. Finally, TransGF outputs the probability of each point in the samples belonging to either ground or non-ground points. This method is evaluated using the publicly accessible OpenGF ground filtering dataset, showcasing its superior performance in testing scenarios. Noteworthy advantages include structural conciseness, ease of training, and a promising potential for practical applications. The results affirm the effectiveness of TransGF in overcoming challenges associated with ground filtering in LiDAR point cloud processing, positioning it as a valuable contribution to the advancement of DEM generation techniques.

Unveiling the dynamics and impact of emotional presence in collaborative learning

This study aims to understand the dynamics and impact of emotional presence in a collaborative learning environment and its effects on the learning process and outcomes. Emotional presence, defined as the experience of emotion arising from cognitive appraisals in learner-environment interactions, encompasses four dimensions: interest-curiosity, emotional regulation, expression management, and emotional awareness. Using a mixed-methods approach, we surveyed 33 Japanese college students engaged in collaborative learning activities and conducted in-depth interviews with 10 participants to gather qualitative insights. The study revealed that emotional presence evolves throughout the collaborative process, with increased emotional regulation in response to complex tasks, reflecting learners’ adaptations to varying task demands. It highlighted the importance of emotional regulation in shaping the learning process and improving outcomes in collaborative learning. Our findings suggest that heightened emotional presence, with increased emotional awareness and regulation, fosters cognitive development and learner well-being, supporting more effective and fulfilling collaborative learning experiences. The study also emphasizes the crucial role of cognitive appraisal in shaping emotional experiences, significantly influencing outcomes in technology-enhanced environments. The study concludes by proposing future research directions, such as examining the longitudinal impacts of emotional presence and exploring interventions to enhance emotional regulation skills in diverse learning contexts.

Reading Reshapes Stimulus Selectivity in the Visual Word Form Area.

Reading depends on a brain region known as the "visual word form area" (VWFA) in the left ventral occipitotemporal cortex. This region's function is debated because its stimulus selectivity is not absolute, it is modulated by a variety of task demands, and it is inconsistently localized. We used fMRI to characterize the combination of sensory and cognitive factors that activate word-responsive regions that we precisely localized in 16 adult humans (4 male). We then presented three types of character strings: English words, pseudowords, and unfamiliar characters with matched visual features. Participants performed three different tasks while viewing those stimuli: detecting real words, detecting color in the characters, and detecting color in the fixation mark. There were three primary findings about the VWFA's response: (1) It preferred letter strings over unfamiliar characters even when the stimuli were ignored during the fixation task. (2) Compared with those baseline responses, engaging in the word reading task enhanced the response to words but suppressed the response to unfamiliar characters. (3) Attending to the stimuli to judge their color had little effect on the response magnitudes. Thus, the VWFA is uniquely modulated by a cognitive signal that is specific to voluntary linguistic processing and is not additive. Functional connectivity analyses revealed that communication between the VWFA and a left frontal language area increased when the participant engaged in the linguistic task. We conclude that the VWFA is inherently selective for familiar orthography, but it falls under control of the language network when the task demands it.

Comparable cerebral cortex activity and gait performance in elderly hypertensive and healthy individuals during dual-task walking: A fNIRS study.

Hypertension increases the risk of cognitive impairment and related dementia, causing impaired executive function and unusual gait parameters. However, the mechanism of neural function illustrating this is unclear. Our research aimed to explore the differences of cerebral cortex activation, gait parameters, and working memory performance between healthy older adults (HA) and older hypertensive (HT) patients when performing cognitive and walking tasks. A total of 36 subjects, including 12 healthy older adults and 24 older hypertensive patients were asked to perform series conditions including single cognitive task (SC), single walking task (SW), and dual-task (DT), wearing functional near-infrared spectroscopy (fNIRS) equipment and Intelligent Device for Energy Expenditure and Activity equipment to record cortical hemodynamic reactions and various gait parameters. The left somatosensory cortex (L-S1) and bilateral supplementary motor area (SMA) showed higher cortical activation (p<.05) than HA when HT performed DT. The intragroup comparison showed that HT had higher cortical activation (p<.05) when performing DT as SW. The cognitive performance of HT was significantly worse (p<.05) than HA when executing SC. The activation of the L-S1, L-M1, and bilateral SMA in HT were significantly higher during SW (p<.05). Hypertension can lead to cognitive impairment in the elderly, including executive function and walking function decline. As a result of these functional declines, elderly patients with hypertension are unable to efficiently allocate brain resources to support more difficult cognitive interference tasks and need to meet more complex task demands by activating more brain regions.

NEW APPROACH TO MILITARY PILOT EDUCATION AND TRAINING IN THE NEW SECURITY ENVIRONMENT

Military pilot education, including flight training, is a long, expensive, and complex process. The main goal is to produce as many skilled professionals as possible ready to perform flight tasks on the front-line aircraft and push them to their workload limits with complete control. To achieve this goal, it is necessary to select and educate young people with the aptitude and abilities to perform the most demanding military flight tasks. The creation of a model for the education and training of military pilots depends on the requirements of the Czech Air Force Command and the teaching capacities of the University of Defence and the Aviation Training Centre in Pardubice. Rearmament of the Czech Air Force and the required number of pilots requires a change in the new military pilot training model. This contribution describes a new approach to Czech Air Force pilot education and training in the current security conditions in the world.

Get ready! High urgency reduces beta band cortico-muscular coherence during motor preparation

Motor preparation is a dynamic process that is tuned to task demands such as urgency. This study examined the effect of urgency to move on cortico-muscular coherence (CMC) in the beta frequency band during motor preparation. Participants (n = 25) prepared for a rapid wrist flexion movement under two distinct scenarios: high (350 ms to prepare) and low (1400 ms to prepare) urgency. Before participants performed the ballistic actions, they were required to hold a light contraction of the flexor carpi radialis muscle for 3 s. During this holding time, we simultaneously obtained EEG and EMG signals to estimate their coherence —a measure of how much brain and muscle activity is synchronized at specific rhythms— over the last 1 s of the contraction interval.Contrary to our hypothesis, we found greater CMC in conditions of low urgency rather than high urgency. This finding suggests that participants prioritized attending to the visual stimuli, dividing their attention to capture the preparation go-signal, rather than preparing the motor system, leading to a reduction in CMC. This interpretation suggests a cognitive-motor trade-off, wherein attentional resources are allocated more to sensory processing that to motor preparedness in urgent situations.

Humans flexibly use visual priors to optimize their haptic exploratory behavior

Humans can use prior information to optimize their haptic exploratory behavior. Here, we investigated the usage of visual priors, which mechanisms enable their usage, and how the usage is affected by information quality. Participants explored different grating textures and discriminated their spatial frequency. Visual priors on texture orientation were given each trial, with qualities randomly varying from high to no informational value. Adjustments of initial exploratory movement direction orthogonal to the textures’ orientation served as an indicator of prior usage. Participants indeed used visual priors; the more so the higher the priors’ quality (Experiment 1). Higher task demands did not increase the direct usage of visual priors (Experiment 2), but possibly fostered the establishment of adjustment behavior. In Experiment 3, we decreased the proportion of high-quality priors presented during the session, hereby reducing the contingency between high-quality priors and haptic information. In consequence, even priors of high quality ceased to evoke movement adjustments. We conclude that the establishment of adjustment behavior results from a rather implicit contingency learning. Overall, it became evident that humans can autonomously learn to use rather abstract visual priors to optimize haptic exploration, with the learning process and direct usage substantially depending on the priors’ quality.

Researching the CNN Collaborative Inference Mechanism for Heterogeneous Edge Devices.

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs. This paper targets efficient collaborative execution of CNN inference tasks among heterogeneous and resource-constrained edge terminal devices. We propose a pre-partitioning deployment method for CNNs based on critical operator layers, and optimize the system bottleneck latency during pipeline parallelism using data compression, queuing, and "micro-shifting" techniques. Experimental results demonstrate that our method achieves significant acceleration in CNN inference within heterogeneous environments, improving performance by 71.6% compared to existing popular frameworks.

Test-retest reliability and validity of vagally-mediated heart rate variability to monitor internal training load in older adults: a within-subjects (repeated-measures) randomized study

BackgroundVagally-mediated heart rate variability (vm-HRV) shows promise as a biomarker of internal training load (ITL) during exergame-based training or motor-cognitive training in general. This study evaluated the test-retest reliability of vm-HRV during exergaming in healthy older adults (HOA) and its validity to monitor ITL.MethodsA within-subjects (repeated-measures) randomized study was conducted that included baseline assessments and 4 measurement sessions. Participants played 5 exergames at 3 standardized levels of external task demands (i.e., “easy”, “challenging”, and “excessive”) in random order for 90 s. Test-retest reliability was assessed on the basis of repeated-measures analyses of variance (ANOVA), intraclass correlation coefficients (ICC3,1), standard errors of measurement (SEM), and smallest detectable differences (SDD). Validity was determined by examining the effect of game level on vm-HRV in the ANOVA.ResultsFourty-three HOA (67.0 ± 7.0 years; 58.1% females (25 females, 18 males); body mass index = 23.7 ± 3.0 kg·m−2) were included. Mean R-R time intervals (mRR) and parasympathetic nervous system tone index (PNS-Index) exhibited mostly good to excellent relative test-retest reliability with no systematic error. Mean SEM% and SDD% were 36.4% and 100.7% for mRR, and 44.6% and 123.7% for PNS-Index, respectively. Significant differences in mRR and PNS-Index were observed between standardized levels of external task demands, with mostly large effect sizes (mean r = 0.847). These results persisted irrespective of the type of neurocognitive domain trained and when only motoric and cognitive demands were manipulated while physical intensity was kept constant. The remaining vm-HRV parameters showed inconsistent or poor reliability and validity.ConclusionOnly mRR and PNS-Index demonstrated reliable measurement and served as valid biomarkers for ITL during exergaming at a group level. Nonetheless, the presence of large SEMs hampers the detection of individual changes over time and suggests insufficient precision of these measurements at the individual level. Future research should further investigate the reliability and validity of vm-HRV with a specific focus on comparing different measurement methodologies and exercise conditions, particularly focusing on ultra-short-term HRV measurements, and investigate the potential implications (i.e., superiority to other markers of ITL or monitoring strategies?) of using vm-HRV as a biomarker of ITL.

Fatigue Is Associated With Perceived Effort and 2-Dimensional Reach Performance After Stroke.

Poststroke fatigue (PSF) is prevalent and often manifests as high perceived effort during activities. Little is known about how PSF influences goal-directed reaching after stroke. The purpose of this study was 2-fold (1) to evaluate how perceived effort changed when individuals with stroke performed a reaching task with various demands and (2) to determine whether PSF was associated with perceived effort during reaching and reach performance. Thirty-six individuals with chronic stroke performed 2-dimensional reach actions under varied conditions with the more and less affected arms. Perceived effort during reaching was assessed using rating of perceived exertion (RPE) and Paas Mental Effort Rating Scale (MERS). Derived reach kinematics were used to quantify reach performance. The Fatigue Severity Scale (FSS) was administered to assess fatigue severity. Perceived effort was higher when participants reached with the more affected arm, reached toward far and small targets, and performed memory-guided reaching. Both RPE and MERS significantly correlated with the FSS score (r =0.50 and 0.35, respectively, P <0.05). Further, FSS correlated with movement time during the more affected arm reaching (ρ=0.40, p <0.05) and reach performance discrepancy between the fast and self-selected speed conditions when participants performed with the less affected arm (ρ=0.36, P <0.05). Exploratory analysis revealed that the relationship between fatigue and reach control appeared to be modulated by task demand. PSF is associated with perceived effort during reaching and reach performance after stroke. These relationships might offer insights into arm performance in the real world after stroke. for more insights from the authors Supplemental Digital Content available at http://links.lww.com/JNPT/A476.

The effects of tunnel radius, turn direction, and zone characteristics on drivers’ visual performance

This study aimed to explore the influence of the tunnel radius, turn direction, and zone characteristics on drivers’ visual performance. The study used a field experiment, in which 30 participants drove real vehicles in different curved tunnels, and measured their fixation duration (FD), fixation frequency (FF), saccade duration (SD), and saccade amplitude (SA) as indicators of visual performance. The results showed that the tunnel radius, turn direction, and zone characteristics had significant effects on all four indicators of visual performance. As the tunnel radius decreases, drivers exhibit longer FD and larger SA, while FF and SD decrease correspondingly. Additionally, the directional aspect of turning significantly influences these eye movement parameters, with drivers demonstrating longer FD, lower FF, shorter SD, and smaller SA during left turns compared to right turns. Irrespective of turning direction, drivers allocate the longest average FD and highest FF in the entrance zone, followed by the middle zone, and the shortest duration in the exit zone under the same tunnel radius condition. Similarly, the mean SD and amplitude are highest in the exit zone, followed by the middle zone, and lowest in the entrance zone, regardless of the turning condition. These findings suggest that drivers adjust their visual behavior according to the dynamic and complex visual stimuli and the varying driving task demands in different scenarios of curved tunnel driving. These findings also imply that drivers face different perceptual and cognitive challenges and preferences when driving in curved tunnels with different radii, turn directions, and zone characteristics, which affect their visual adaptation and preparation processes, as well as their visual integration and differentiation processes. This study has some implications and suggestions for the design and management of curved tunnels, as well as for the education and training of drivers. This study also provides new theoretical and empirical evidence for the visual behavior and cognition of drivers in curved tunnels, and provides practical suggestions for enhancing their visual performance and improving their driving safety and efficiency in curved tunnels.