Task Performance Research Articles

Gray matter matters: Cognitive stability and flexibility in schizophrenia spectrum disorder.

Cognitive dysfunction constitutes a core characteristic of schizophrenia spectrum disorders (SZ). Specifically, deficits in updating generative models (i.e., cognitive flexibility) and shielding against distractions (i.e., cognitive stability) are considered critical contributors to cognitive impairment in these patients. Here, we examined the structural integrity of frontostriatal networks and their associations with reduced cognitive stability and flexibility in SZ patients. In a sample of 21 patients diagnosed with SZ and 22 healthy controls, we measured gray matter volume (GMV) using structural MRI. Further, cognitive stability and flexibility were assessed using a switch-drift paradigm, quantifying the successful ignoring of distracters and detection of rule switches. Compared to controls, patients showed significantly smaller GMV in the whole brain and three predefined regions of interest: the medial prefrontal cortex (mPFC), inferior frontal gyrus (IFG), and caudate nucleus (CN). Notably, GMV in these areas positively correlated with correct rule-switch detection but not with ignoring rule-compatible drifts. Further, the volumetric differences between SZ patients and controls were statistically explainable by considering the behavioral performance in the switch-drift task. Our results indicate that morphological abnormalities in frontostriatal networks are associated with deficient flexibility in SZ patients and highlight the necessity of minimizing neurodevelopmental and progressive brain atrophy in this population.

Trajectory tracking control based on deep reinforcement learning and ensemble random network distillation for robotic manipulator

Abstract In general, the trajectory tracking of robotic manipulator is exceptionally challenging due to the complex and strongly coupled mechanical architecture. In this paper, precise track control of the robotic manipulator is formulated as a dense reward problem for reinforcement learning(RL). A deep RL(DRL) approach combining the soft actor-critic (SAC) algorithm and ensemble random network distillation (ERND) is proposed to address the tracking control problem for robotic manipulator. Firstly, an ERND model is designed, consisting of a module list of multiple RND models. Each RND model obtains the error by learning the target features and the predicted features of the environment. The resulting error serves as internal rewards that drive the robotic agent to explore unknown and unpredictable environmental states. The ensemble model obtains the total internal reward by summing the internal rewards of each RND model, thereby obtaining more accurately reflecting the characteristics of the manipulator in tracking control tasks and improving control performance. Secondly, combining the SAC algorithm with ERND facilitates more robust exploration capabilities in environments with input saturation and joint angle constraints, thereby enabling faster learning of effective policies and enhancing the performance and efficiency of robotic manipulator tracking control tasks. Finally, the simulation results demonstrate that the robotic manipulator tracking control task is effectively completed in dense reward problems through the combination of the SAC algorithm and ERND.

SPNet: A Serial and Parallel Convolutional Neural Network algorithm for the cross-language coreference resolution

Current models of coreference resolution always neglect the importance of hidden feature extraction, accurate scoring framework design, and the long-term influence of preceding potential antecedents on future decision-making. However, these aspects play vital roles in scoring the likelihood of coreference between an anaphora and its’ real antecedent. In this paper, we present a novel model named Serial and Parallel Convolutional Neural Network (SPNet). Based on the SPNet, two kinds of resolvers are proposed. Given the characteristics of reinforcement learning, we joint the reinforcement learning framework and the SPNet to solve the problem of Chinese zero pronoun resolution. What’s more, we make some fine-tuning on the SPNet and propose a new resolver combined with the end-to-end framework to solve the problem of coreference resolution. The experiments are conducted on the CoNLL-2012 dataset and the results show that our model is effective. Our model achieves excellent performance in the Chinese zero pronoun resolution task. On the other hand, compared with our baseline, our model also has an improvement of 0.3% in coreference resolution task.

Behavioural interference at event boundaries reduces long-term memory performance in the virtual water maze task without affecting working memory performance

Narrative episodic memory of movie clips can be retroactively impaired by presenting unrelated stimuli coinciding with event boundaries. This effect has been linked with rapid hippocampal processes triggered by the offset of the event, that are alternatively related either to memory consolidation or with working memory processes. Here we tested whether this effect extended to spatial memory, the temporal specificity and extent of the interference, and its effect on working- vs long-term memory. In three computerized adaptations of the Morris Water Maze, participants learned the location of an invisible target over three trials each. A second spatial navigation task was presented either immediately after finding the target, after a 10-s delay, or no second task was presented (control condition). A recall session, in which participants indicated the learned target location with 10 ‘pin-drop’ trials for each condition, was performed after a 1-h or a 24-h break. Spatial memory was measured by the mean distance between pins and the true location. Results indicated that the immediate presentation of the second task led to worse memory performance, for both break durations, compared to the delayed condition. There was no difference in performance between the delayed presentation and the control condition. Despite this long-term memory effect, we found no difference in the rate of performance improvement during the learning session, indicating no effect of the second task on working memory. Our findings are in line with a rapid process, linked to the offset of an event, that is involved in the early stages of memory consolidation.

HAC-SAGIN: High-altitude computing enabled space–air–ground integrated networks for 6G

The space–air–ground integrated networks (SAGINs) provide a new paradigm for the development of the Internet of Things (IoT) networks by enhancing coverage and deploying computing resources near IoT devices, especially in emergency situations and disaster-hit regions. In the context of the IoT networks, aerial platforms such as unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) present in the air layer of SAGINs with access and aerial computing (AC) capabilities have the potential to significantly expand coverage, enhance performance, reduce delay and handle complex computation tasks for IoT devices. Seeking the stated prospect, we propose a high-altitude computing (HAC)-enabled SAGIN leveraging millimeter waves (mmWave) frequency range in which the IoT devices are provided access services by low-earth orbit satellites (LEO-SATs) and HAPs while the HAPs offer AC facility as well. Non-orthogonal multiple access (NOMA) is used as a multiple-access technique with different clustering mechanisms in uplink (UL) and downlink (DL) communication. We aim to establish high-rate data transmission in DL along with minimizing the execution time of IoT devices offloading their data to the HAPs in UL communication. The mmWaves range is targeted to have high-rate data transmissions and NOMA implementation further enhances the bandwidth available for an individual IoT device. For efficient offloading in UL communication, we formulate an optimization problem aiming to minimize the execution time by using the Lagrangian function-based approach. Execution time is minimized by reducing the transmission and computation time, which is attained by the optimization of allocated power and computation resources. Simulation results demonstrate that the proposed HAC-SAGIN is able to establish high-rate transmissions in DL and exhibits a significant decrease in execution time in UL in contrast to the no optimization case. Optimum power assignment improves the achievable rate, leading to reduced transmission times, while optimum core assignment efficiently reduces the computation time. In addition, the offloaded data size-driven NOMA implementation in UL prominently improves the system effective throughput.

Domain-selective and sex-dependent regulation of learning and memory in mice by GIRK channel activity in CA1 pyramidal neurons of the dorsal hippocampus.

G protein-gated inwardly rectifying K+ (GIRK) channels mediate the postsynaptic inhibitory effect of many neurotransmitters in the hippocampus and are implicated in neurological disorders characterized by cognitive deficits. Here, we show that enhancement or suppression of GIRK channel activity in dorsal CA1 pyramidal neurons disrupted novel object recognition in mice, without impacting open field activity or avoidance behavior. Contextual fear learning was also unaffected, but extinction of contextual fear was disrupted by suppression of GIRK channel activity in male mice. Thus, the strength of GIRK channel activity in dorsal CA1 pyramidal neurons regulates select cognitive task performance in mice.

Approach to Aircraft Handling Qualities Prediction

For the decades that followed the publication of the Cooper–Harper report that formalized a standard and universally recognized handling qualities pilot rating scale, researchers have sought to correlate pilot compensation—as well as physical and mental workload—with the assigned rating. A quantitative correlation remains elusive. In recent years, new physiological measurement devices have been developed that, together with software processing tools, can provide accurate measures of psychophysiological measures, including cognitive workload, distraction, and high/low engagement, based on electroencephalogram and electrocardiogram measures (i.e., brain waves and heart rate variability). The pilot compensation referred to in the Cooper–Harper scale is also a function of task performance measures that reflect aircraft characteristics and inceptor activity that reflects upon physical workload. Using a new piloted simulation test database generated in Manned Flight Simulator’s containerized rotary-wing simulator with 10 experienced test pilots, a machine-learning-based software algorithm that integrates a disparate mix of pilot–vehicle system, physiological, and task performance measures was used to further develop an approach to predict handling qualities levels and ratings.

An examination of the motivation to manage distraction

Individuals exhibit limited awareness of when their attention is captured by salient but irrelevant stimuli, and it has long been argued that involuntary attentional capture by such stimuli is minimally disruptive to information processing. Yet, robust mechanisms of distractor suppression are hypothesized to support the control of attention, which presumably serve in the interest of managing distraction. In the present study, I examine whether participants are aware of the cost of distraction with respect to task performance, and whether they are motivated to manage this cost even when it is effortful to do so. Across three experiments, participants were willing to exert physical effort in order to reduce the frequency with which they encountered physically salient distractors, and in a fourth experiment tended to prefer trials with fewer distractors when given a choice over distractor frequency. Importantly, the amount of physical effort exerted varied as a function of the degree to which task-irrelevant distractors impaired search performance, suggesting that people are sensitive to the cost of distraction.

Advancing geological image segmentation: Deep learning approaches for rock type identification and classification

This study aims to tackle the obstacles linked with geological image segmentation by employing sophisticated deep learning techniques. Geological formations, characterized by diverse forms, sizes, textures, and colors, present a complex landscape for traditional image processing techniques. Drawing inspiration from recent advancements in image segmentation, particularly in medical imaging and object recognition, this research proposed a comprehensive methodology tailored to the specific requirements of geological image datasets. To establish the dataset, a minimum of 50 images per rock type was deemed essential, with the majority captured at the University of Las Palmas de Gran Canaria and during a field expedition to La Isla de La Palma, Spain. This dual-source approach ensures diversity in geological formations, enriching the dataset with a comprehensive range of visual characteristics. The study involves the identification of 19 distinct rock types, each documented with 50 samples, resulting in a comprehensive database containing 950 images. The methodology involves two crucial phases: initial preprocessing of the dataset, focusing on formatting and optimization, and subsequent application of deep learning models—ResNets, Inception V3, DenseNets, MobileNets V3, and EfficientNet V2 large. Preparing the dataset is crucial for improving both the quality and relevance, thereby to ensure the optimal performance of deep learning models, the dataset was preprocessed. Following this, transfer learning or more specifically fine-tuning is applied in the subsequent phase with ResNets, Inception V3, DenseNets, MobileNets V3, and EfficientNet V2 large, leveraging pre-trained models to enhance classification task performance. After fine-tuning eight deep learning models with optimal hyperparameters, including ResNet101, ResNet152, Inception-v3, DenseNet169, DenseNet201, MobileNet-v3-small, MobileNet-v3-large, and EfficientNet-v2-large, comprehensive evaluation revealed exceptional performance metrics. DenseNet201 and InceptionV3 attained the highest accuracy of 98.49% when tested on the original dataset, leading in precision, sensitivity, specificity, and F-score. Incorporating preprocessing steps further improved results, with all models exceeding 97.5% accuracy on the preprocessed dataset. In K-Fold cross-validation (k = 5), MobileNet V3 large excelled with the highest accuracy of 99.15%, followed by ResNet101 at 99.08%. Despite varying training times, models on the preprocessed dataset showed faster convergence without overfitting. Minimal misclassifications were observed, mainly among specific classes. Overall, the study's methodologies yielded remarkable results, surpassing 99% accuracy on the preprocessed dataset and in K-Fold cross-validation, affirming the efficacy in advancing rock type understanding.

Connection of social anxiety to impaired pattern of cognitive control and underlying motivational deficiencies: Evidence from event-related potentials.

Numerous studies have established a correlation between social anxiety and poor cognitive control. However, little is known about the cognitive control pattern of individuals with high social anxiety (HSAs) and the underlying mechanisms. Based on the Dual Mechanisms of Control framework and the Expected Value of Control theory, this study explored whether HSAs have an impaired cognitive control pattern (Experiment 1) and whether motivational deficiencies underlie the impaired control pattern (Experiment 2). In Experiment 1, 21 individuals with low social anxiety (LSAs) and 21 HSAs completed an AX-Continuous Performance Task. Results showed that HSAs had a smaller P3b amplitude than LSAs, indicating their weakened proactive control in the cue processing stage, but a larger contingent negative variation (CNV) on cue B as compensation for the negative effects of anxiety in the response preparation stage. No group difference was found in N2 and P3a amplitude on probes, suggesting that reactive control in HSAs was not affected compared to LSAs. In Experiment 2, 21 LSAs and 21 HSAs completed a cued-flanker task, where the likelihood of proactive control engagement was manipulated. The results revealed that HSAs exhibited motivation deficiencies in engaging in proactive control, as evidenced by P3b, CNV amplitude, and response times. These findings shed light on the impaired cognitive control pattern of HSAs and suggest that motivational deficiencies may be the crucial underlying factor.

A framework for the emergence and analysis of language in social learning agents

Neural systems have evolved not only to solve environmental challenges through internal representations but also, under social constraints, to communicate these to conspecifics. In this work, we aim to understand the structure of these internal representations and how they may be optimized to transmit pertinent information from one individual to another. Thus, we build on previous teacher-student communication protocols to analyze the formation of individual and shared abstractions and their impact on task performance. We use reinforcement learning in grid-world mazes where a teacher network passes a message to a student to improve task performance. This framework allows us to relate environmental variables with individual and shared representations. We compress high-dimensional task information within a low-dimensional representational space to mimic natural language features. In coherence with previous results, we find that providing teacher information to the student leads to a higher task completion rate and an ability to generalize tasks it has not seen before. Further, optimizing message content to maximize student reward improves information encoding, suggesting that an accurate representation in the space of messages requires bi-directional input. These results highlight the role of language as a common representation among agents and its implications on generalization capabilities.

Spiritual roots of adaptability: unveiling the impact of workplace spirituality on task performance post-covid-19

Spiritual roots of adaptability: unveiling the impact of workplace spirituality on task performance post-covid-19

Differential Effects of Continuous Theta Burst Stimulation over the Bilateral and Unilateral Cerebellum on Working Memory.

Recent functional MRI studies have implicated the cerebellum in working memory (WM) alongside the prefrontal cortex. Some findings indicate that the right cerebellum is activated during verbal tasks, while the left is engaged during visuospatial tasks, suggesting cerebellar lateralization in WM function. The cerebellum could be a potential target for non-invasive brain stimulation (NIBS) to enhance WM function in cognitive disorders. However, the comprehensive influence of cerebellar lateralization on different types of WM and the effect of stimulation over the unilateral or bilateral cerebellum remain uncertain. This study was to investigate the cerebellum's functional lateralization and its specific impact on various aspects of WM in a causal manner using unilateral or bilateral cerebellar continuous theta burst stimulation (cTBS), a form of inhibitroy NIBS. Twenty-four healthy participants underwent four sessions of cTBS targeting the left, right, or bilateral Crus I of the cerebellum, or a sham condition, in a controlled cross-over design. WM performance was assessed pre- and post-stimulation using neuropsychological tests, including the 3-back task, spatial WM task, and digit span task. Results indicated that cTBS over the bilateral and right cerebellum both led to a greater improvement in 3-back task performance compared to sham stimulation. Additionally, active cTBS over the bilateral cerebellum yielded better performance in the spatial WM task than sham stimulation. However, no significant differences were observed between stimulation conditions for the auditory digit span task. This study may provide novel causal evidence highlighting the specific involvement of the right and bilateral cerebellum in various types of WM. Specifically, the right cerebellum appears crucial for updating and tracking 3-back WM content, while spatial WM processes require the coordinated engagement of both cerebellar hemispheres.

Improvement in Body's Dynamic Adaptation during Walking with Vestibular Rehabilitation Therapy in Patients with Cerebellopontine Angle Tumor Resection.

Our study aimed to investigate the effects of vestibular rehabilitation therapy on functional gait performance in patients with balance disorders. A total of 40 post-operative patients with balance disorders were included in the study. They were divided into two groups and participated in a vestibular rehabilitation program during their hospital stay. After discharge, the intervention group performed vestibular exercises at home, while the control group did not. Balance was assessed using the Functional Gait Assessment Scale at discharge and three months after surgery. The intervention group included 15 women and 5 men with an average age of 45 years, while the control group included 7 women and 13 men with an average age of 50 years. Three months after surgery, the change in Functional Gait Assessment (FGA) scores exceeded the clinically significant threshold of 5 points in 17 patients in the intervention group and 14 in the control group. There was a statistically significant difference in FGA progression between the groups (p = 0.034). After three months post-surgery, 7 patients in the intervention group experienced falls compared to 12 in the control group. Three months after surgery, we observed a significant improvement in the performance of balance tasks while walking and a lower risk of falls in the intervention group.

Greek-Cypriot learners’ perception and production of L2 English vowels

Acquiring L2 segments is particularly challenging for L2 learners, especially when the L1 and L2 inventories involve different contrasts and acoustic cues. The present research investigated the perception and production of L2 English vowels by adult Greek-Cypriot learners and examined the effects of orthographic cues and consonantal context in their performance. Perceptual performance was assessed through a forced-choice identification task and production performance through a wordlist-reading and an elicitation task, both analyzed acoustically and through intelligibility ratings. The findings showed the influence of the L1 on both the perception and production of L2 segments, supporting the assumptions of current models of speech perception and production. Learners faced challenges in perceiving the members of L2 contrasts and mostly used their L1 articulatory routines in their productions of L2 vowels. Orthographic cues or consonantal context did not significantly affect learners’ productions or overall perception, although strong contextual effects were observed in individual target vowels. This study is the first to provide an in-depth comparison of CYG and English vowels as well as an examination of the acquisition of L2 English vowels by this population, which can guide EFL teachers and course developers in developing appropriate EFL curricula that incorporate pronunciation instruction.

Assessing driver distraction from in-vehicle information system: an on-road study exploring the effects of input modalities and secondary task types

In-vehicle information system (IVIS) use is prevalent among young adults. However, their interaction with IVIS needs to be better understood. Therefore, an on-road study aims to explore the effects of input modalities and secondary task types on young drivers' secondary task performance, driving performance, and visual glance behavior. A 2 × 4 within-subject design was undertaken. The independent variables are input modalities (auditory-speech and visual-manual) and secondary task types (calls, music, navigation, and radio). The dependent variables include secondary task performance (task completion time, number of errors, and SUS), driving performance (average speed, number of lane departure warnings, and NASA-TLX), and visual glance behavior (average glance duration, number of glances, total glance duration, and number of glances over 1.6 s). The statistical analysis result showed that the main effect of input modalities is significant, with more distraction during visual-manual than auditory-speech. The main impact of secondary task types was also substantial across most metrics, aside from average speed and average glance duration. Navigation and music were the most distracting, followed by calls, and radio came in last. The distracting effect of input modalities is relatively stable and generally not moderated by the secondary task types, except radio tasks. The findings practically benefit the driver-friendly human–machine interface design, preventing IVIS-related distraction.

Combined effects of abusive supervision, willpower and waypower on employees’ task performance and helping behavior, through quality of work life

PurposeExtending the efforts of previous scholars, this study examines how abusive supervision undermines employees’ ability to meet performance expectations and propensity to engage in helping behavior. Specifically, we investigate a hitherto unexplored mediating role of quality of work life (QWL) in this relationship. We further suggest that employees’ psychological resources, namely willpower and waypower, act as protective shields against this harmful process.Design/methodology/approachWe tested the proposed hypotheses using multisource (self- and supervisor-rated) three-wave time-lagged data (N = 185) collected from employees and their supervisors in eight organizations that operate in the service sector of Pakistan.FindingsThe findings corroborate our predicted hypotheses. The results indicate that employees' exposure to abusive supervision deteriorates their quality of work life (QWL), hindering their ability to deliver expected performance and tendency to help other colleagues. However, this negative process is less pronounced for employees who possess sufficient psychological resources of willpower and waypower.Practical implicationsThis study provides valuable insights to organizations by explicating the process that undermines employees’ ability to channel their energies into performance-enhancing activities when faced with humiliation from their supervisors.Originality/valueThis study details three previously unexplored factors that explain how and when abusive behavior steers service sector employees away from meeting performance expectations and assisting colleagues, via thwarting their quality of work life.

MAPRS: An intelligent approach for post-prescription review based on multi-label learning

Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way to improve appropriate prescription by the review and stewardship of antimicrobials, and Post-Prescription Review (PPR) is currently the main tool used in hospitals. Existing methods of PPR typically focus on the dichotomy of antimicrobial prescription based on binary classification which, however, is usually a multi-label classification problem. Moreover, previous research did not explain the causes beneath the inappropriate antimicrobial used in the clinical setting, which could be practically important for problem location and decision improvement. In this paper, we collected antimicrobial prescriptions and related data from clean surgery in a hospital in northeastern China, and proposed a Multi-label Antimicrobial Post-Prescription Review System (MAPRS). MAPRS first uses NLP techniques to process unstructured data in prescriptions and explores the value of clinical record text for solving medical problems. Then, Classifier Chains are used to deal with multi-label problems and fused with machine learning algorithms to construct a classifier. At last, a SHAP explanation module is introduced to explain the inappropriate prescriptions. The experimental results show that MAPRS could achieve great performance in a challenging six-category multi-label task, with a subset accuracy of 90.7 % and an average AUROC of 94.3 %. Our results can help hospitals to perform intelligent prescription review and improve the antimicrobial stewardship.

ANALISIS PENERAPAN SISTEM INFORMASI AKUNTANSI PENGGAJIAN NON ASN DAN PENGENDALIAN INTERNAL DALAM UPAYA MENINGKATKAN KINERJA PEGAWAI PADA DINAS PERDAGANGAN DAN PERINDUSTRIAN KABUPATEN SUKABUMI

Payroll Accounting Information System and Internal Control are important aspects in managing a government agency because a good payroll accounting information system and internal control can provide accurate information and avoid errors in payroll so that it can improve employee performance. The type of research used is a qualitative descriptive method, the data processed is the result of unstructured interviews and observations that will be analyzed qualitatively and described in descriptive form. The results of the study show that the implementation of the Non ASN payroll accounting information system and internal control has been running well in improving employee performance. However, the Sukabumi Regency Trade and Industry Service needs to improve the attendance recording system which is still done manually as one of the weaknesses that needs to be improved into a computerized system to avoid fraud. In addition, internal control related to organizational structure also needs to be improved to separate the performance of employee tasks and functions more effectively and add human resources in several parts to prevent a decline in performance

Learning to Move and Plan like the Knight: Sequential Decision Making with a Novel Motor Mapping.

Many skills that humans acquire throughout their lives, such as playing video games or sports, require substantial motor learning and multi-step planning. While both processes are typically studied separately, they are likely to interact during the acquisition of complex motor skills. In this work, we studied this interaction by assessing human performance in a sequential decision-making task that requires the learning of a non-trivial motor mapping. Participants were tasked to move a cursor from start to target locations in a grid world, using a standard keyboard. Notably, the specific keys were arbitrarily mapped to a movement rule resembling the Knight chess piece. In Experiment 1, we showed the learning of this mapping in the absence of planning, led to significant improvements in the task when presented with sequential decisions at a later stage. Computational modeling analysis revealed that such improvements resulted from an increased learning rate about the state transitions of the motor mapping, which also resulted in more flexible planning from trial to trial (less perseveration or habitual responses). In Experiment 2, we showed that incorporating mapping learning into the planning process, allows us to capture (1) differential task improvements for distinct planning horizons and (2) overall lower performance for longer horizons. Additionally, model analysis suggested that participants may limit their search to three steps ahead. We hypothesize that this limitation in planning horizon arises from capacity constraints in working memory, and may be the reason complex skills are often broken down into individual subroutines or components during learning.