Task Performance Research Articles

Spatial thinking skills used by hydrogeology practitioners and students while completing a hydrogeology task

AbstractA typical classroom exercise in hydrogeology is to develop a conceptual model of a contaminated site, identify groundwater flow direction(s), and predict the location and mass of a contaminant plume. This requires knowledge of key hydrogeological concepts and is highly visuospatial in nature. Among multiple discrete spatial thinking skills identified by cognitive science, the combination of visual penetrative ability and working in multiple frames of reference were identified to significantly predict performance on a hydrogeology task and showed that together with hydrogeology knowledge, these spatial thinking skills account for 49% of the variability on task performance. Seventy-two hydrogeology practitioners and students with varying levels of expertise were administered multiple spatial thinking tests and an assessment of hydrogeology knowledge before completing a hydrogeology task that was developed for the study. Using spatial thinking and knowledge test scores as predictor variables, a hierarchical regression analysis was conducted with performance on the hydrogeology task as the outcome variable. The resulting model predicts that at low levels of hydrogeology knowledge, the identified spatial thinking skills account for more than a 25% difference on the hydrogeology task. This study provides empirical evidence that visual penetrative ability and working in multiple frames of reference are important skills in hydrogeology; thus, instructors are encouraged to recognize that underdeveloped spatial thinking skills could present hurdles for students and that targeted spatial thinking training may yield positive results for both weak and strong spatial thinkers.

Modulation of memory by prism adaptation in healthy subjects

Recent findings suggest that prism adaptation can extend its effects beyond spatial attention, modulating the performance of different cognitive tasks by acting on cerebellar, parietal and temporal-frontal networks. We tested groups of healthy subjects to investigate the effects of rightward vs. leftward prism adaptation vs. neutral lenses exposure in a series of memory tasks, probing either short-term (Digit span, Corsi span) or long-term memory (Supraspan verbal and spatial learning). In the short-term memory tasks, leftward prism adaptation selectively increased verbal span, while rightward prism adaptation increased spatial span. In the long-term memory tasks, leftward prism adaptation selectively increased verbal supraspan, i.e., increased the number of digits in the correct sequence reproduced and reduced the number of repetitions needed to learn the supraspan sequence. On the other hand, rightward prism adaptation selectively increased spatial supraspan, i.e. it increased the number of spatial positions in the correct sequence reproduced and reduced the number of repetitions needed to learn the supraspan sequence. Moreover, rightward, but not leftward, prism adaptation selectively increased supraspan recall after a delay interval, regardless of the stimulus material, i.e., it increased the number of digits or spatial positions recalled after a delay interval. Neutral lenses exposure did not influence any memory task. These findings suggest that prism adaptation can induce both modality/hemispheric-specific and process-specific effects on short-term and long-term explicit memory.

Enhancement algorithm for surface electromyographic-based gesture recognition based on real-time fusion of muscle fatigue features

This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.

Physics-Driven Image Dehazing from the Perspective of Unmanned Aerial Vehicles

Drone vision is widely used in change detection, disaster response, and military reconnaissance due to its wide field of view and flexibility. However, under haze and thin cloud conditions, image quality is usually degraded due to atmospheric scattering. This results in issues like color distortion, reduced contrast, and lower clarity, which negatively impact the performance of subsequent advanced visual tasks. To improve the quality of unmanned aerial vehicle (UAV) images, we propose a dehazing method based on calibration of the atmospheric scattering model. We designed two specialized neural network structures to estimate the two unknown parameters in the atmospheric scattering model: the atmospheric light intensity A and medium transmission t. However, calculation errors always occur in both processes for estimating the two unknown parameters. The error accumulation for atmospheric light and medium transmission will cause the deviation in color fidelity and brightness. Therefore, we designed an encoder-decoder structure for irradiance guidance, which not only eliminates error accumulation but also enhances the detail in the restored image, achieving higher-quality dehazing results. Quantitative and qualitative evaluations indicate that our dehazing method outperforms existing techniques, effectively eliminating haze from drone images and significantly enhancing image clarity and quality in hazy conditions. Specifically, the compared experiment on the R100 dataset demonstrates that the proposed method improved the peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics by 6.9 dB and 0.08 over the second-best method, respectively. On the N100 dataset, the method improved the PSNR and SSIM metrics by 8.7 dB and 0.05 over the second-best method, respectively.

Self-Determination Components as Predictors of Job Performance Among Secondary School Teachers in Ebonyi State

The study examined self-determination components as predictors of job performance among secondary school teachers in Ebonyi State. The study used the correlation research design. Three research questions and three corresponding hypotheses guided the study. The population of the study comprised all 6, 121 teachers in the 226 public secondary schools in Ebonyi State. The simple random sampling technique was used to draw 399 teachers as sample for the study. Two self-designed instrument (questionnaire) titled Self-Determination Scale (SDS), and Teachers’ Tasks Performance Scale (TTPS) were used to collect data for the study. The Cronbach Alpha was used to determine the reliability of the instruments; the reliability coefficients obtained were .92 and .88 respectively. The research question was answered using simple regression while hypotheses were tested with regression analysis. The finding of the study revealed that self-determination components (competence, autonomy and relatedness,) significantly predict job performance of teachers in public secondary schools in Ebonyi State. Based on the findings of the study, it was recommended among others that teachers should take their career direction very seriously by being committed and dedicated in other to achieve high aspiration. Keywords: Self-determination, Competence, Autonomy, Relatedness and Teachers’ Job Performance.

Impact of Experimentally Induced Pain on Logical Reasoning and Underlying Attention-Related Psychophysiological Mechanisms

Background. Pain is known to negatively impact attention, but its influence on more complex cognitive abilities, such as logical reasoning, remains inconsistent. This may be due to compensatory mechanisms (e.g., investing additional resources), which might not be detectable at the behavioral level but can be observed through psychophysiological measures. In this study, we investigated whether experimentally induced pain affects logical reasoning and underlying attentional mechanisms, using both behavioral and electroencephalographic (EEG) measures. Methods. A total of 98 female participants were divided into a pain-free control group (N = 47) and a pain group (N = 51). Both groups completed the Advanced Progressive Matrices (APM) task, with EEG recordings capturing task-related power (TRP) changes in the upper alpha frequency band (10–12 Hz). We used a mixed design where all participants completed half of the APM task in a pain-free state (control condition); the second half was completed under pain induction by the pain group but not the pain-free group (experimental condition). Results. Logical reasoning performance, as measured by APM scores and response times, declined during the experimental condition, compared to the control condition for both groups, indicating that the second part of the APM was more difficult than the first part. However, no significant differences were found between the pain and pain-free groups, suggesting that pain did not impair cognitive performance at the behavioral level. In contrast, EEG measures revealed significant differences in upper alpha band power, particularly at fronto-central sites. In the pain group, the decrease in TRP during the experimental condition was significantly smaller compared to both the control condition and the pain-free group. Conclusions. Pain did not impair task performance at the behavioral level but reduced attentional resources, as reflected by changes in upper alpha band activity. This underscores the importance of incorporating more sensitive psychophysiological measures alongside behavioral measures to better understand the impact of pain on cognitive processes.

Supplemental Material for Verbal and Visual Short-Term Memory Predict Performance in a Multiplication Production Task: Evidence From a Malaysian Sample

Supplemental Material for Verbal and Visual Short-Term Memory Predict Performance in a Multiplication Production Task: Evidence From a Malaysian Sample

Competition during verbal creative processes influences on ERS/ERD

ABSTRACT Humans are social creatures, and many tasks in our daily lives are solved together. The two main forms of social interaction in problem solving could be defined as competition and cooperation. In our study, we compared the ERS/ERD when performing a creative task (Alternative Uses Test, AUT) and a control task (“naming the objects from the presented category”) under competitive conditions in dyads (22 dyads, m-m, f-f, 18–23 years old) compared to the performance of tasks individually. The number of answers given by subjects under competitive conditions was significantly lower than during the execution of the tasks individually. The solving of the creative task in competition versus individual performance was accompanied by EEG synchronization (9–30 hz) clusters: 140–1220 ms and 900–1780 ms after stimulus presentation; 13.5–30 hz (1800–1980 ms), reflecting the creative thinking mode, and expected cognitive, emotional answers’ assessment. The control task under competitive conditions was accompanied by pronounced synchronization of low frequencies in the frontal areas (2–7 hz, 0–1980 ms), due to a greater working memory load; synchronization clusters in broadband (10–30 hz, 100–320 ms, 400–860 ms) and in the beta EEG band (17–30 hz, 1140–1980 ms). The competitive conditions significantly modulated the brain activity underlying creative and non-creative cognitive task performance, and resulted in greater induced EEG synchronization.

Motion-mimicking Robotic Finger Prosthesis for Burn-induced Partial Hand Amputee: A Case Report.

Burn injuries often result in severe hand complications, including joint contractures and nerve damage, sometimes leading to amputation. Despite early treatment, hypertrophic scarring frequently hampers hand function recovery, and the thick raised scar blocks electromyography (EMG) sensing. A promising solution involves motion-mimicking robotic finger prostheses tailored to individual patient requirements. By utilizing the versatility of motion-capturing technology on a sound finger, a robotic finger prosthesis can mimic the movement of a sound finger simultaneously with less latency than EMG-based sensory mechanisms through hypertrophic scars. This case study evaluated the clinical efficacy of a customized three-dimensional printed robotic finger prosthesis in a 24-year-old man who sustained left second finger loss due to electrical burns. Despite undergoing reconstructive surgery, the patient struggled with manual dexterity. Following the adoption of a personalized robotic finger prosthesis with a finger motion-capturing device, significant improvements in grip strength and daily task performance were observed. This innovative approach has advantages such as customization, reduced latency time for finger movements, and affordability from low-cost manufacturing, suggesting its potential for broader adoption among burn-induced amputees.

A Task-driven Adversarial Channel Selection Method for Binary Classification Based on Magnetocardiography.

As the number of sensors in magnetocardiography (MCG) arrays increases to capture detailed cardiac activity, some channels contribute minimally to task performance, resulting in data redundancy and resource consumption. Although existing methods can reduce the number of channels required to meet task demands, they often struggle to balance computational time and the accuracy of the selected channels and overlook the scalability of the selected channels. This limitation means that when environmental conditions change, or when sensors malfunction, redesigning channel configurations becomes necessary, which increases experimental uncertainties. This study introduces a task-driven adversarial channel selection method tailored for binary classification of MCG signals. The optimal channel combination is determined through a group-wise search using a heuristic algorithm, whose objective function is designed to maximize the difference between the classification accuracy and cosine similarity of the selected channel. In evaluations using an MCG dataset from Qilu Hospital of Shandong University, the proposed method successfully reduced the number of channels from 36 to 5 without compromising classification performance. Furthermore, it outperforms existing hybrid sequential forward search method by achieving comparable accuracy with fewer channels, while also demonstrating superior scalability compared to both hybrid sequential forward search and pearson-rank methods. This approach strikes a balance between computational consumption and accuracy, while improving the scalability of the selected channel combinations, enhancing the efficiency and practicality of the MCG system.

Increasing transparency of computer-aided detection impairs decision-making in visual search.

Recent developments in artificial intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy and how AI systems work. However, increased transparency could lead to negative outcomes if humans become overreliant on the technology. This study investigated how changes in AI transparency affected human decision-making in a medical-screening visual search task. Transparency was manipulated by either giving or withholding knowledge about the accuracy of an 'AI system'. We tested performance in seven simulated lab mammography tasks, in which observers searched for a cancer which could be correctly or incorrectly flagged by computer-aided detection (CAD) 'AI prompts'. Across tasks, the CAD systems varied in accuracy. In the 'transparent' condition, participants were told the accuracy of the CAD system, in the 'not transparent' condition, they were not. The results showed that increasing CAD transparency impaired task performance, producing an increase in false alarms, decreased sensitivity, an increase in recall rate, and a decrease in positive predictive value. Along with increasing investment in AI, this research shows that it is important to investigate how transparency of AI systems affect human decision-making. Increased transparency may lead to overtrust in AI systems, which can impact clinical outcomes.

Assessing the Effects of Sensory Modality Conditions on Object Retention across Virtual Reality and Projected Surface Display Environments

Haptic feedback reportedly enhances human interaction with 3D data, particularly improving the retention of mental representations of digital objects in immersive settings. However, the effectiveness of visuohaptic integration in promoting object retention across different display environments remains underexplored. Our study extends previous research on the retention effects of haptics from virtual reality to a projected surface display to assess whether earlier findings generalize to 2D environments. Participants performed a delayed match-to-sample task incorporating visual, haptic, and visuohaptic sensory feedback within a projected surface display environment. We compared error rates and response times across these sensory modalities and display environments. Our results reveal that visuohaptic integration significantly enhances object retention on projected surfaces, benefiting task performance across display environments. Our findings suggest that haptics can improve object retention without requiring fully immersive setups, offering insights for the design of interactive systems that assist professionals who rely on precise mental representations of digital objects.

Exploring the influence of music on cognitive performance in female assembly line workers at a medical device manufacturing unit.

The significance of enhancing working conditions for the physical health and performance of workers, particularly female workers, underscores the need for research in this domain and the examination of interventions such as music. Previous studies have yielded diverse outcomes regarding the influence of music on individuals' performance; hence, further research in this area appears imperative. The aim of this study is to explore the impact of music on the cognitive and task performance of female assembly operation operators. This study is an interventional (quasi-experimental) study that involved 81 participants from the female workforce of the medical equipment assembly unit in Isfahan, Iran. The evaluation encompassed task performance, working memory using N-Back test, sustained attention using continues performance test (CPT), degree of sleepiness, and mental fatigue using Flicker Fusion test, along with physiological parameters such as heart rate and blood oxygen level. Participants underwent testing both in the presence of classical music and in a condition without music playback. The provision of background music enhanced the workers' sustained attention and working memory. It led to improved task performance and a reduction in drowsiness. Concerning physiological parameters, it resulted in a slight decrease in heart rate at the end of the work shift and a marginal increase in participants' blood oxygen levels. Background music enhanced working memory (p-value = 0.001), sustained attention (p-value = 0.001), and improved the task performance of workers(p-value = 0.005). Additionally, likely due to increased relaxation, it led to a decrease in heart rate (p-value = 0.001) and an increase in blood oxygen levels (p-value = 0.016). Music also played a role in reducing participants' sleepiness (p-value = 0.001).

Individual Differences in the Impact of Distracting Environmental Sounds on the Performance of a Continuous Visual Task in Older Adults

Background/Objectives: Vulnerability to sound distraction is commonly reported in older adults with dementia and tends to be associated with adverse impacts on daily activity. However, study outcome heterogeneity is increasingly evident, with preserved resistance to distraction also evident. Contributory factors may include individual differences in distractibility in older adulthood per se, and failure to consider the influence of how difficult a person found the test. Methods: We therefore measured distractibility in a group of older adults by comparing the performance of a primary visual task (Swansea Test of Attentional Control), which includes an adaptive algorithm to take into account how difficult a person finds the test under both no-sound and sound conditions. Results: Analysis revealed no significant difference in group mean performance between no-sound versus sound conditions [t (33) = 0.181, p = 0.858; Cohen’s effect size d = −0.028], but individual differences in performance both within and between sound and no-sound conditions were evident, indicating that for older adults, distracting sounds can be neutral, detrimental, or advantageous with respect to visual task performance. It was not possible to determine individual thresholds for whether sound versus no-sound conditions affected a person’s actual behaviour. Conclusions: Nevertheless, our findings indicate how variable such effects may be in older adults, which in turn may help to explain outcome heterogeneity in studies including people living with dementia. Furthermore, such within-group heterogeneity highlights the importance of considering a person’s individual performance in order to better understand their behaviour and initiate interventions as required.

Proactive control for conflict resolution is intact in subclinical obsessive-compulsive individuals.

BackgroundObsessive-compulsive (OC) traits (i.e., tendency to implement stereotyped behaviors to avoid negative consequences) are transversally observed in psychiatric disorders largely differing in terms of clinical manifestations and etiopathogenesis. Interestingly, OC traits were also extensively found in the prodromal phases of the full-blown psychopathology and in healthy relatives of affected individuals. Moreover, OC traits were found to be associated—and possibly underlain by—cognitive control impairments. Nonetheless, the role of such interplay in the onset of OC disorders is yet to be understood. We hypothesized that OC traits are associated with abnormalities in proactively implement cognitive control for solving conflict.MethodsWe administered healthy individuals (n = 104) with the perifoveal spatial Stroop task to measure their ability of solving conflict in a proactive fashion, and with Obsessive-Compulsive Inventory (OCI) to stratify population according to the severity of OC traits.ResultsAnalysis of response times by means of Linear Mixed-effect models revealed that proactive control performance was not associated with and the severity of OC traits. Furthermore, an equivalence test (Two One-Sided Test) revealed that the association between OCI scores and task performance was equivalent to zero.ConclusionThese results suggest that the interplay between OC traits and proactive control abnormalities might not contribute to the development of OC-related disorders. Therefore, the role of other cognitive endophenotypes should be scrutinized for exploiting alternative prevention and intervention strategies.

A self-regulation perspective on L2 grit development and its impact on language achievement

Evidence from recent studies has shown that sustained perseverance and passion for long-term goals in the domain of second language (L2) learning – a personality trait called L2 grit – is associated with various aspects of language learning, including positive psychological attributes (e.g. motivation, beliefs, and emotions), actions (e.g. learning efforts and strategy use), and achievement (e.g. course and task performance). Meanwhile, relatively little has been written about the mechanism through which such relationships emerge. Expanding upon L2 grit’s association with self-regulated learning (SRL) behaviors and SRL-related psychological variables, this study explores the development of L2 grit and its role in language achievement using the SRL framework. One hundred and eight English majors in Japan responded to a questionnaire, granted permission to access their L2 class records, and completed a standardized L2 test. The main findings include: (1) the psychological attributes that facilitate SRL may play a critical role in increasing levels of L2 grit, (2) the relationship between L2 grit and achievement in language classes was mediated by behavioral self-regulation (intensified learning efforts), and (3) L2 grit may promote different types of self-regulatory behaviors depending on learners’ status (i.e. learning experience and proficiency levels) and learning situations (e.g. learning content and goals). The results provide one explanation for the superior performance of gritty learners in L2 classes observed in previous studies.

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.

EXPRESS: Cross-Linguistic Effects of the Speech-to-Song Illusion in Speakers of Bangla and English.

The speech-to-song illusion is a phenomenon in which the continuous repetition of a spoken utterance induces the listeners to perceive it as more song-like. Thus far, this perceptual transformation has been observed in mostly European languages, such as English; however, it is unclear whether the illusion is experienced by speakers of Bangla (Bengali), an Indo-Aryan language. The current study, therefore, investigates the illusion in 28 Bangla- and 31 English-speaking participants. The experiment consisted of a listening task in which participants were asked to rate their perception of repeating short speech stimuli on a scale from 1-5, where 1 = "sounds like speech" and 5 = "sounds like song". The stimuli were composed of English and Bangla utterances produced by two bilingual speakers. To account for possible group differences in music engagement, participants self-reported musical experience and also performed a rhythm discrimination task as an objective measure of non-verbal auditory sequence processing. Stimulus ratings were analysed with cumulative link mixed modelling. Overall, English- and Bangla-speaking participants rated the stimuli similarly and, in both groups, better performance in the rhythm discrimination task significantly predicted more song-like ratings beyond self-reported musical experience. An exploratory acoustic analysis revealed a role of harmonic ratio in the illusion for both language groups. These results demonstrate that the speech-to-song-illusion occurs for Bangla speakers to a similar extent as English speakers and that, across both groups, sensitivity to non-verbal auditory structure is positively correlated with susceptibility to this perceptual transformation.

“It's all about trust!” a multilevel model of the effect of servant leadership on firefighters' group task performance, adaptivity and emotional exhaustion

AbstractHow and why does servant leaders' behavior influence both performance (individual and collective) and emotional exhaustion within dynamic and extreme environments such as those of firefighters? We develop and test a multilevel model that integrates the principles of servant leadership with social exchange theory to explore how servant leadership positively influences collective task performance and how it strengthens adaptivity at the individual level and reduces emotional exhaustion. Our four‐wave and three‐source study sample comprised 303 firefighters nested in 45 fire stations. The results of multilevel structural equation model (MSEM) analyses indicate that at the individual level, servant leadership significantly predicts high adaptivity and low emotional exhaustion through the mediating influence of firefighters' felt trust and the trust climate. The implications of our results for theory and practice are discussed.

Is transcranial alternating current stimulation effective for improving working memory? A three-level meta-analysis.

Working memory, an essential component of cognitive function, can be improved through specific methods. This meta-analysis evaluates the effectiveness of transcranial alternating current stimulation (tACS), an emerging technique for enhancing working memory, and explores its efficacy, influencing factors, and underlying mechanisms. A PRISMA systematic search was conducted. Hedges's g was used to quantify effect sizes. We constructed a three-level meta-analytic model to account for all effect sizes and performed subgroup analyses to assess moderating factors. Recognizing the distinct neural underpinnings of various working memory processes, we separately assessed the effects on n-back tasks and traditional working memory tasks. A total of 39 studies with 405 effect sizes were included (170 from n-back tasks and 235 from other tasks). The overall analysis indicated a net benefit of g = 0.060 of tACS on working memory. Separate analyses showed that tACS had a small positive effect on n-back tasks (g = 0.102), but almost no effect on traditional working memory tasks (g = 0.045). Further analyses revealed mainly: A moderately positive effect of theta tACS (without anti-phase stimulation) on n-back tasks (g = 0.207); and a small effect of offline stimulation on working memory maintenance (g = 0.127). Overall, tACS has minimal impact on working memory improvement, but it shows potential under certain conditions. Specifically, both online and offline theta tACS can improve n-back task performance, while only offline stimulation enhances working memory maintenance. More research is needed to understand the mechanisms behind these effects to make tACS an effective method.