Human Performance Research Articles

The effect of e-cigarettes on cognitive function: a scoping review.

Much research has been conducted on the acute effects of nicotine on human cognitive performance, demonstrating both enhancing and impairing cognitive effects. With the relatively recent introduction of electronic cigarettes ('e-cigarettes') as a smoking cessation device, little is known about the cognitive effects of e-cigarettes specifically, either as a nicotine replacement device or in the absence of nicotine. The purpose of this review was to present an overview of evidence from empirical studies on the effect of e-cigarettes on cognitive function. Guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines (PRISMA-ScR), SCOPUS, PubMed, and EBSCOhost were searched from 2006, the year e-cigarettes were introduced, to June 2023 for relevant papers, along with reference lists checked for additional papers. Seven experimental and four cross-sectional survey studies were identified and included. The majority of the studies only include regular and current cigarette smokers and primarily assessed the acute cognitive effect of e-cigarettes relative to nicotine. While the findings primarily suggest either no or positive effect of e-cigarettes on cognition in cigarette smokers, associations between e-cigarettes and cognitive impairments in memory, concentration and decision making were reported in both cigarette smokers and never-smokers. The acute cognitive effect of e-cigarettes on regular cigarette smokers appears minimal. However, long-term cognitive effect and their effects on never-smokers are unclear. Given that the increased numbers of e-cigarette users are non-smokers and/or adolescents, research with those naïve to nicotine and a developmentally vulnerable adolescent population on its long-term effect is needed.

New task-new results? How the gaze cone is influenced by the method of measurement.

Perceiving direct gaze-the perception of being looked at-is important in everyday life. The gaze cone is a concept to define the area in which observers perceive gaze as direct. The most frequently used methods to measure direct gaze threshold fall into two broad groups: First, a variant of the method of constant stimuli, firstly introduced by Gibson and Pick (The American Journal of Psychology, 76, 386-394, 1963). Second, a variant of the method of adjustment, firstly introduced by Gamer and Hecht (Journal of Experimental Psychology: Human Perception and Performance, 33, 705-715, 2007). Previous studies found a considerable range of thresholds, and although some influences on thresholds are already known (uncertainty, clinical groups), thresholds often vary for no apparent reason. Another important method is a triadic gaze-perception task, which usually finds triadic gaze direction judgments to be overestimated. In two experiments, we compare the method of adjustment with the method of constant stimuli. Experiment 1additionally examines the influence of the overestimation effect found in the triadic task. Results indicate that thresholds are larger when measured by the method of adjustment than by constant stimuli. Furthermore, Experiment 1finds a nonlinear overestimation factor, indicating that gaze directions near 0° are less overestimated than larger eccentricities. Correcting the thresholds with individually obtained overestimation factors widens the gaze cone but does not eliminate the average difference between the methods of adjustments and constant stimuli.

Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties. We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images. The performance of the ARD-Net framework was evaluated for numerous low count realizations using fidelity-based qualitative metrics, task-based segmentation, and quantitative metrics. Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat-pad were subjected to preclinical [18F]-Fluorodeoxyglucose (FDG)-PET/CT imaging. SC-PET images were derived from a 10min static FDG-PET acquisition, 50min post administration of FDG, and were resampled to generate four distinct LC-PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC-PET count-level. ARD-Net was trained and optimized using 48 preclinical FDG-PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD-Net was benchmarked against two leading DL-based methods (Residual UNet, RU-Net; and Dilated Network, D-Net) and non-DL methods (Non-Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity-based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer-based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics-derived features were utilized as a measure of quality assurance (QA) in comparison to true SC-PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity-based and task-based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non-parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD-Net against benchmarked methods with significance at adjusted p-value ≤0.01. ARD-Net-generated SC-PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon-count levels compared to benchmarked DL and non-DL methods. In terms of task-based quantitative accuracy evaluated by SUVMeanand SUVPeak, ARD-Net exhibited less than 5% median absolute bias for SUVMean compared to true SC-PET and lower degree of variability compared to benchmarked DL and non-DL based methods in generating SC-PET. Additionally, ARD-Net-generated SC-PET images displayed higher degree of concordance to SC-PET images in terms of radiomics features compared to non-DL and other DL approaches. Finally, the ensemble score suggested that ARD-Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction). ARD-Net provides a robust framework to generate SC-PET from LC-PET images. ARD-Net generated SC-PET images exhibited superior performance compared other DL and non-DL approaches in terms of image-fidelity based metrics, task-based segmentation metrics, and minimal bias in terms of task-based quantification performance for preclinical PET imaging.

Human resource challenges in health systems: evidence from ten African countries.

Sub-Saharan Africa has fewer medical workers per capita than any region of the world, and that shortage has been highlighted consistently as a critical constraint to improving health outcomes in the region. This paper draws on newly available, systematic, comparable data from ten countries in the region to explore the dimensions of this shortage. We find wide variation in human resources performance metrics, both within and across countries. Many facilities are barely staffed, and effective staffing levels fall further when adjusted for health worker absences. However, caseloads-while also varying widely within and across countries-are also low in many settings, suggesting that even within countries, deployment rather than shortages, together with barriers to demand, may be the principal challenges. Beyond raw numbers, we observe significant proportions of health workers with very low levels of clinical knowledge on standard maternal and child health conditions. This work demonstrates that countries may need to invest broadly in health workforce deployment, improvements in capacity and performance of the health workforce, and on addressing demand constraints, rather than focusing narrowly on increases in staffing numbers.

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

BackgroundThe detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article.Results2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating.ConclusionThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

Exploring characteristics of and transitions between mental states within the Multi‐Action Plan model in high‐level judo

AbstractThe Multi‐Action Plan (MAP) model presents an idiosyncratic framework for human performance. MAP research has categorised four Performance Types (PTs; T1–T4) using self‐paced activities. Specifically, T1–4 present four distinct mental states experienced by athletes during performance, differing, among other aspects, in the level of effort and perceived control. However, transitions between PTs are yet to be empirically investigated. This study aimed to examine MAP in the hyperdynamic open‐skill combat context of judo. We explored how judoka experienced PTs and the transition processes between these mental states. Six British, high‐level judoka (n = 6; Mdnage = 19) were interviewed using a retrospective semi‐structured interview schedule. Interpretative phenomenological analysis indicated that judo presents a hyperdynamic difficult‐to‐predict performance context due to the opponent and fighting rhythm. Second, participants strongly related to T2 and T3 and less to T1 and T4. They also reported experiences ‘between’ T2 and T3. Lastly, participants reported common transition pathways (e.g., T2–T3), typical transition cues, and specific trainable strategies to optimise performance. Findings are discussed in relation to existing literature. Lastly, we present limitations, applied, and research considerations.

Shades of gravity - effects of planetary gravity levels on electrocortical activity and neurocognitive performance.

The plans of international space agencies to return to the Moon and explore deep space, including Mars, highlight the challenges of human adaptation and stress the need for a thorough analysis of the factors that facilitate, limit and modify human performance under extreme environments. This study investigates the influence of partial gravity on behavioural (error rate and reaction time) and neuronal parameters (event-related potentials) through parabolic flights. Brain cortical activity was assessed using EEG from 18 participants who solved a neurocognitive task, consisting of a mental arithmetic task and an auditory oddball paradigm, during Earth (1G), Lunar (0.16G + 0.25G) and Martian gravity (0.38G + 0.5G) for 15 consecutive parabolas. Data shows higher electrocortical activity in Earth gravity compared to Lunar and Martian gravity in the parietal lobe. No differences in participants' performance were found among the gravity levels. Event-related potentials displayed gravity-dependent variations, though limited stimuli recording suggests caution in interpretation. Data suggests a threshold between Earth and Martian gravity within the different gravities responsible for physiological changes, but it seems to vary greatly between individuals. The altered neuronal communication could be explained with a model developed by Kohn and Ritzmann in 2018. The increasing intracranial pressure in weightlessness changes the properties of the cell membrane of neurons and leads to a depolarisation of the resting membrane potential. The findings underscore the individuality of physiological changes in response to gravity alterations, signalling the need for further investigations in future studies.

Re-evaluating the Need for Routine Maximal Aerobic Capacity Testing within Fighter Pilots.

INTRODUCTION: There is a current belief in aviation suggesting that aerobic training may reduce G-tolerance due to potential negative impacts on arterial pressure response. Studies indicate that increasing maximal aerobic capacity (<mml:math id="ie1" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂ max) through aerobic training does not hinder G-tolerance. Moreover, sustained centrifuge training programs revealed no instances where excessive aerobic exercise compromised a trainee's ability to complete target profiles. The purpose of this review article is to examine the current research in the hope of establishing the need for routine <mml:math id="ie2" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂-max testing in air force pilot protocols.METHODS: A systematic search of electronic databases including Google Scholar, PubMed, the Aerospace Medical Association, and Military Medicine was conducted. Keywords related to "human performance," "Air Force fighter pilots," "aerobic function," and "maximal aerobic capacity" were used in various combinations. Articles addressing exercise physiology, G-tolerance, physical training, and fighter pilot maneuvers related to human performance were considered. No primary data collection involving human subjects was conducted; therefore, ethical approval was not required.RESULTS: The <mml:math id="ie3" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂-max test provides essential information regarding a pilot's ability to handle increased Gz-load. It assists in predicting G-induced loss of consciousness by assessing anti-G straining maneuver performance and heart rate variables during increased G-load.DISCUSSION: <mml:math id="ie4" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂-max testing guides tailored exercise plans, optimizes cardiovascular health, and disproves the notion that aerobic training hampers G-tolerance. Its inclusion in air force protocols could boost readiness, reduce health risks, and refine training for fighter pilots' safety and performance. This evidence-backed approach supports integrating <mml:math id="ie5" display="inline"><mml:mrow><mml:mover><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:mrow></mml:math>o₂-max testing for insights into fitness, risks, and tailored exercise.Zeigler Z, Acevedo AM. Re-evaluating the need for routine maximal aerobic capacity testing within fighter pilots. Aerosp Med Hum Perform. 2024; 95(5):273-277.

Towards Decision-Making Support for Complex Socio-Technical System Safety Assessment: A Hybrid Model Combining FRAM and Dynamic Bayesian Networks

Effectively managing system safety and resilience in critical infrastructures requires addressing emergent risks and critical resonances. This study introduces a quantitative model that merges Monte Carlo Simulation (MCS) of the Functional Resonance Analysis Method (FRAM) and Dynamic Bayesian Network (DBN) to assess technological, organizational, and human performance variability in complex social-technical systems. FRAM identifies system taxonomy and functional variability, while MCS pinpoints critical coupling, supplying prior probabilities for functional resonance. This information feeds into the DBN, facilitating the modeling of causal relationships and probabilistic inferences regarding risk uncertainties and system resonances. The model underwent rigorous testing and validation on a preheater cyclone system within the cement industry. This process involved the utilization of historical field data gathered from six prominent cement industries and input from thirty subject matter experts. The integrated approach deeply analyzes emerging risk indicators, unveiling interactions within organizational, human, and technical subsets, alongside performance variability. Furthermore, the model incorporates system learning parameters, aiding decision-making under uncertainty. These findings advance system safety and resilience management, offering insights for risk assessment and accident prevention across diverse scenarios in complex socio-technical systems.

دور استراتيجيات إدارة الموارد البشرية في التميز التنظيمي دراسة تطبيقية على العاملين في صندوق تنمية الموارد البشرية بالمملكة العربية السعودية

This study aimed to examine the impact of human resource management strategies, including job analysis, human resource planning, training, performance evaluation, and compensation, on achieving organizational excellence in its various dimensions such as leadership excellence, strategic excellence, organizational structure excellence, individual excellence, and organizational culture excellence at the Human Resources Development Fund in the Kingdom of Saudi Arabia. The study sample consisted of 262 employees out of a total of 578, and the study followed a descriptive-analytical approach, utilizing the SPSS program for data analysis. The study found statistically significant evidence of the impact of human resource management strategies on achieving organizational excellence at the Human Resources Development Fund in the Kingdom of Saudi Arabia. The study recommended the continuous development of effective and well-thought-out human resource management strategies, covering all stages of the employee lifecycle, to optimize the utilization of human resources and achieve organizational performance and objectives.

Enhancing entrepreneurial strategy skills in Saudi universities employees: The impact of psychological capital

Psychological capital is recognized as a positive and unique factor that plays a crucial role in human resource development and performance management. It has the potential to increase employees’ efforts towards achieving organizational goals and improving their entrepreneurial strategy skills. The objective of this study was to examine the contribution of psychological capital in enhancing the entrepreneurial strategy skills of employees in Saudi universities. The study employed a descriptive approach, specifically utilizing the survey study method. The study sample was intentionally selected from different categories within the study population. Data was collected from 530 participants using two questionnaires. The findings revealed that employees exhibited an average level of psychological capital, while their practice of entrepreneurial strategy skills was rated as poor. The study also demonstrated that psychological capital significantly contributes to enhancing employees’ entrepreneurial strategy skills. Furthermore, statistically significant differences were observed in the psychological capital of employees across certain variables, such as personal and functional aspects. The average level of psychological capital among employees indicates the need for further development in this area. By focusing on enhancing psychological capital, organizations can effectively improve the entrepreneurial strategy skills of their employees. It is clear that investing in the psychological capital of employees can lead to significant improvements in their entrepreneurial strategy skills. This highlights the potential for organizations to foster a more entrepreneurial mindset and approach among their staff members. Additionally, the study’s findings underscore the need to tailor interventions and development programs to address specific aspects of psychological capital that may vary across different employees. Overall, the study emphasizes that psychological capital is a valuable resource that should be nurtured and developed within the organizational context. By doing so, organizations can not only enhance the entrepreneurial strategy skills of their employees but also cultivate a more resilient, motivated, and engaged workforce. This has the potential to contribute to the overall success and innovation of Saudi universities and similar institutions.

Emotional Intelligence: Catalyst for enhanced human resource management performance

This study investigates the pivotal role of emotional intelligence (EI) in optimizing human resource management (HRM) outcomes. Through a mixed-method approach combining quantitative surveys and qualitative interviews, the research explores the correlation between EI competencies and various HRM effectiveness metrics. Findings reveal a significant positive relationship between EI levels among HR professionals and key performance indicators such as employee engagement, leadership effectiveness, and conflict resolution. Furthermore, qualitative insights underscore the nuanced ways in which EI influences HRM practices, emphasizing its role in fostering empathetic communication, facilitating constructive feedback, and mitigating workplace tensions. These results have profound implications for organizational HR strategies, suggesting the integration of EI development programs into HRM training initiatives. By cultivating EI competencies among HR practitioners, organizations can enhance their capacity to nurture a positive work environment, foster stronger employee relationships, and ultimately achieve greater organizational success.

The Role of Leadership and Discipline to Increase Commitment to Loudry Harfia Employees

This study aims to understand the important role of leadership and discipline in increasing employee commitment at Loudry Harfia. Effective leadership and consistent discipline are key factors in creating a productive and committed work environment. In the context of the laundry industry, where quality of service and consistency in operations are critical, this role is becoming increasingly vital. This research approach includes an analysis of the relationship between leadership styles applied in organizations, applied disciplinary practices, and employee commitment levels. Through a deep understanding of these dynamics, this study aims to provide valuable insights for Loudry Harfia's management in improving the performance and commitment of their employees. The results of this study are expected to provide a better understanding of how strong leadership and consistent application of discipline can affect employee commitment, as well as provide practical guidance for management in optimizing their human resources to achieve organizational goals effectively. Thus, this research is expected to significantly contribute significantly to human resource development and performance management in service industries such as Loudry Harfia.

Exercise Physiology: A Review of Established Concepts and Current Questions

Exercise physiology is the science that studies the processes of physical activity and their impact on the body. It usually requires a multidisciplinary approach with a team of experts because of the multiple physiological systems involved. At the cellular level, exercise can trigger a cascade of events that involve metabolic pathways, muscle recruitment and conditioning, neuromuscular adaptations, and optimization of energy production. On a systematic level, this has involved the cardiovascular system, the respiratory system, the musculoskeletal system, the peripheral and central nervous systems, and even the endocrine system to improve adaptation to meet the demands of exercise, improving strength and endurance with the goal of maximizing performance. This field, which began largely with a focus on the kinetics of oxygen consumption and the relationship between aerobic and anaerobic metabolism, has expanded with technological advancements such as wearable devices, advanced imaging techniques, and genomics, which has allowed for a greater appreciation of the physiologic responses of exercise at the level of molecular and cellular interactions. Recently, this science has evolved into the study of overall health promotion and disease prevention to meet individual fitness needs and goals. This philosophy, which is captured by the motto “Exercise is Medicine”, has been adopted as a Global Health Initiative by the World Health Organization (WHO). Despite the advancements in technology and impactful, large-scale studies, a variety of questions, such as how physical activity interacts with aging to affect physical and cognitive function, how inactivity influences acute and chronic disease states, and how we maximize human performance in extreme conditions like microgravity environments, remain. The purpose of this review is to present and discuss established concepts, controversial topics, and unanswered questions within exercise physiology that have allowed this field to become a science that embraces the overall well-being of individuals.

Management Policy, Strategic Planning, and Human Resource Performance in Business Organisations: Unravelling the Overlapping Correlations for Effective Decision-Making

The objective of the study is to ascertain how organisational policies and strategies affect human resource performance in business enterprises. The study is anchored on decision theory and adopted a review of the literature to justify its findings. The study found that there is an intricate correlation between policy and strategy concerning organisational management and human resource performance. Also, it is revealed that worker's oriented policies and strategies affect their performance and the overall growth of the organisation. The study concludes that business policies and strategies are veritable and dynamic decision-making tools applied by managers in responding to changes in the business world.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

Artificial Creativity? Evaluating AI Against Human Performance in Creative Interpretation of Visual Stimuli

This study investigates the creative capacities of artificial intelligence (AI), exemplified by ChatGPT-4, in comparison with human creativity, utilizing the Figural Interpretation Quest (FIQ) as the evaluative tool. The primary objective is to assess whether AI can surpass human performance in creative tasks, particularly in terms of flexibility and subjectively perceived creativity. Participants, including both ChatGPT-4 and humans, were asked to provide creative interpretations for a set of ambiguous, abstract figures. The study measured the flexibility of these interpretations and gathered subjective ratings of creativity from independent evaluators. Results indicated that while AI, on average, demonstrated higher flexibility in generating creative interpretations, human participants excelled in terms of subjectively perceived creativity. Furthermore, the most creative human responses were higher than those of AI in both flexibility and subjective creativity. These findings suggest that in these types of complex multimodal interpretation tasks, AI shows excellent potential and the ability to generate semantically diverse ideas, yet human subjectively perceived creativity remains for now unmatched. The study highlights the limitations of AI in replicating the complexity of human creativity and points to possible theoretical interpretations of the reported findings.

Neural correlates of crowding in macaque area V4.

Visual crowding refers to the phenomenon where a target object that is easily identifiable in isolation becomes difficult to recognize when surrounded by other stimuli (distractors). Many psychophysical studies have investigated this phenomenon and proposed alternative models for the underlying mechanisms. One prominent hypothesis, albeit with mixed psychophysical support, posits that crowding arises from the loss of information due to pooled encoding of features from target and distractor stimuli in the early stages of cortical visual processing. However, neurophysiological studies have not rigorously tested this hypothesis. We studied the responses of single neurons in macaque (one male, one female) area V4, an intermediate stage of the ventral, object-processing pathway, to parametrically designed crowded displays and their texture-statistics matched metameric counterparts. Our investigations reveal striking parallels between how crowding parameters, e.g., number, distance, and position of distractors, influence human psychophysical performance and V4 shape selectivity. Importantly, we also found that enhancing the salience of a target stimulus could alleviate crowding effects in highly cluttered scenes, and this could be temporally protracted reflecting a dynamical process. Thus, a pooled encoding of nearby stimuli cannot explain the observed responses and we propose an alternative model where V4 neurons preferentially encode salient stimuli in crowded displays. Overall, we conclude that the magnitude of crowding effects is determined not just by the number of distractors and target-distractor separation but also by the relative salience of targets versus distractors based on their feature attributes-the similarity of distractors, and the contrast between target and distractor stimuli.Significance Statement Psychophysicists have long studied the phenomena of visual crowding, but the underlying neural mechanisms are unknown. Our results reveal striking correlations between the responses of neurons in mid-level visual cortical area V4 and psychophysical demonstrations, revealing that crowding is influenced not only by the number and spatial arrangement of distractors but also by the similarity of features between target and distractors, as well as among the distractors themselves. Overall, our studies provide strong evidence that the visual system uses strategies to preferentially encode salient features in a visual scene presumably to process visual information efficiently. When multiple nearby stimuli are equally salient, the phenomenon of crowding ensues.

Can Artificial Intelligence Identify Reading Fluency and Level? Comparison of Human and Machine Performance

This study examined whether an artificial intelligence-based automatic speech recognition system can accurately assess students’ reading fluency and reading level. Participants were 120 fourth-grade students attending public schools in Türkiye. Students read a grade-level text out loud while their voice was recorded. Two experts and the artificial intelligence-based automatic speech recognition system analyzed the recordings for reading errors. Following the analysis, a word error rate was calculated for both the experts and the artificial intelligence-based automatic speech recognition system. Word error rates were converted into reading accuracy rate scores. Inter-rater agreement and linear regression analyses were used to compare the raters’ reading fluency scores, and logistic regression analyses were used to compare the classification of readers according to their reading levels. Results showed that the difference between the scores of the artificial intelligence-based automatic speech recognition system and the expert scores was minimal. This is because there was a very high level of agreement between the artificial intelligence-based automatic speech recognition system and the experts scores. Linear regression analyses showed that the artificial intelligence-based automatic speech recognition system significantly predicted the scores of experts. According to the logistic regression analysis results, the artificial intelligence-based automatic speech recognition system was at least 93% as successful as human raters in classifying readers as poor and good. These results give us hope that reading assessments at classroom, school, regional, national, and even international levels can be conducted more accurately and economically by using artificial intelligence-based systems in the coming years.

A novel mixture model for characterizing human aiming performance data

Fitts’ law is often employed as a predictive model for human movement, especially in the field of human-computer interaction. Models with an assumed Gaussian error structure are usually adequate when applied to data collected from controlled studies. However, observational data (often referred to as data gathered ‘in the wild’) typically display noticeable positive skewness relative to a mean trend as users do not routinely try to minimize their task completion time. As such, the exponentially modified Gaussian (EMG) regression model has been applied to aimed movements data. However, it is also of interest to reasonably characterize those regions where a user likely was not trying to minimize their task completion time. In this article, we propose a novel model with a two-component mixture structure—one Gaussian and one exponential—on the errors to identify such a region. An expectation-conditional-maximization (ECM) algorithm is developed for estimation of such a model and some properties of the algorithm are established. The efficacy of the proposed model, as well as its ability to inform model-based clustering, are addressed in this work through extensive simulations and an insightful analysis of a human aiming performance study.