The Impact of Dual-Tasks and Disease Severity on Posture, Gait, and Functional Mobility among People Living with Dementia in Residential Care Facilities: A Pilot Study.

Gait speed and timed-up-and-go (TUG) predict cognitive decline, falls, and mortality. Dual-tasks may be useful in cognitive screening among people living with dementia (PWD), but more evidence is needed. This cross-sectional study aimed to compare single- and dual-task performance and determine the influence of dementia severity on dual-task performance and interference. Thirty PWD in two residential care facilities (Age: 81.3 ± 7.1 years; Montreal Cognitive Assessment: 10.4 ± 6.0 points) completed two trials of single- (feet apart) and dual-task posture (feet apart while counting backward), single- (walk 4 m) and dual-task gait (walk 4m while naming words), and single- (timed-up-and-go (TUG)), and dual-task functional mobility (TUG while completing a category task) with APDM inertial sensors. Dual-tasks resulted in greater sway frequency, jerk, and sway area; slower gait speed; greater double limb support; shorter stride length; reduced mid-swing elevation; longer TUG duration; reduced turn angle; and slower turn velocity than single-tasks (ps < 0.05). Dual-task performance was impacted (reduced double limb support, greater mid-swing elevation), and dual-task interference (greater jerk, faster gait speed) was related to moderate-to-severe compared to mild PWD. Moderate-to-severe PWD had poorer dynamic stability and a reduced ability to appropriately select a cautious gait during dual-tasks than those with mild PWD, indicating the usefulness of dual-tasks for cognitive screening.

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.

Effects of noise exposure on stress hormone changes during task performance in young Korean men: quasi-experimental study.

Numerous studies have suggested that noise exposure might be associated with changes in stress hormone levels. However, quantitative evidence for these effects in humans is rare and remains controversial. This study aimed to investigate the acute effects of exposure to noise and its different levels on stress hormone changes in task performance. Quasi-experimental noise exposure environment was established for 90 male university student volunteers in their twenties, and each was exposed to different noise levels during task performance. The stress hormones tested included cortisol, adrenocorticotropic hormone (ACTH), adrenaline, and noradrenaline. A one-way ANOVA was performed to investigate differences in hormone levels measured in the three groups according to the noise exposure levels (35, 45, or 75 dB). Analysis of covariance (ANCOVA) was used to adjust for confounding factors that might affect hormone levels. After adjusting for confounders, significant exposure-dependent differences were found in hormone levels in salivary cortisol, serum cortisol, serum ACTH, and serum adrenaline. The amount of hormonal increase in 75 dB exposure group compared to 35 or 45 dB groups was detected. Similar results were also seen in the rate of change analysis. Our findings indicate that short-term noise exposure during task performance elevates stress hormone levels. Further, the extent of stress hormone alterations varies with noise exposure levels. Changes in hormone levels are an objective measure that may be used to identify health effects and stress responses in various noise environments.

Early Eye Disengagement Is Regulated by Task Complexity and Task Repetition in Visual Tracking Task.

Understanding human actions often requires in-depth detection and interpretation of bio-signals. Early eye disengagement from the target (EEDT) represents a significant eye behavior that involves the proactive disengagement of the gazes from the target to gather information on the anticipated pathway, thereby enabling rapid reactions to the environment. It remains unknown how task difficulty and task repetition affect EEDT. We aim to provide direct evidence of how these factors influence EEDT. We developed a visual tracking task in which participants viewed arrow movement videos while their eye movements were tracked. The task complexity was increased by increasing movement steps. Every movement pattern was performed twice to assess the effect of repetition on eye movement. Participants were required to recall the movement patterns for recall accuracy evaluation and complete cognitive load assessment. EEDT was quantified by the fixation duration and frequency within the areas of eye before arrow. When task difficulty increased, we found the recall accuracy score decreased, the cognitive load increased, and EEDT decreased significantly. The EEDT was higher in the second trial, but significance only existed in tasks with lower complexity. EEDT was positively correlated with recall accuracy and negatively correlated with cognitive load. Performing EEDT was reduced by task complexity and increased by task repetition. EEDT may be a promising sensory measure for assessing task performance and cognitive load and can be used for the future development of eye-tracking-based sensors.

Multitasking across the lifespan in different task contexts.

We assessed lifespan development of multitasking in a sample of 187 individuals aged 8-82 years. Participants performed a visuo-spatial working memory (VSWM) task together with either postural control or reaction time (RT) tasks. Using criterion-referenced testing we individually adjusted difficulty levels for the VSWM task to control for single-task differences. Age-differences in single-task performances followed U-shaped patterns with young adults outperforming children and older adults. Multitasking manipulations yielded robust performance decrements in VSWM, postural control and RT tasks. Presumably due to our adjustment of VSWM challenges, costs in this task were small and similar across age groups suggesting that age-differential costs found in earlier studies largely reflected differences already present during single-task performance. Age-differences in multitasking costs for concurrent tasks depended on specific combinations. For VSWM and RT task combinations increases in RT were the smallest for children but pronounced in adults highlighting the role of cognitive control processes. Stabilogram diffusion analysis of postural control demonstrated that long-term control mechanisms were affected by concurrent VSWM demands. This interference was pronounced in older adults supporting concepts of compensation or increased cognitive involvement in sensorimotor processes at older age. Our study demonstrates how a lifespan approach can delineate the explanatory scope of models of human multitasking.

Effect of laparoscopic handle size on surgical performance: A randomized crossover trial.

Laparoscopic instrument handles design and dimensions are crucial to determine the configuration of surgeons' hand grip and, therefore, can have a deleterious effect on overall surgical efficiency and surgeons' comfort. The aim of this study is to investigate the impact of laparoscopic handle size and hand surface area on surgical task performance. A single-blind, randomized crossover trial was carried out with 29 novice medical students. Participants performed three simulated tasks in "black box" simulators using two scissor-type handles of different sizes. Surgical performance was assessed by the number of errors and time required to complete each task. Hand anthropometric data were measured using a 3D scanner. Execution time was significantly higher when cutting and suturing tasks were performed with the smaller handle. In addition, hand surface area was positively correlated with peg transfer task time when performed with the standard handle and was correlated with cutting task time in small and standard handle groups. We also found positive correlations between execution time and the number of errors executed by larger-handed participants. Our findings indicate that laparoscopic handle size and hand area influence surgical performance, highlighting the importance of considering hand anthropometry variances in surgical instrument design.

Automation function and malfunction: effects on human performance in accident handling tasks.

By conducting a mixed-design experiment using simplified accident handling tasks performed by two-person teams, this study examined the effects of automation function and condition (before, during, and after malfunction) on human performance. Five different and non-overlapping functions related to human information processing model were considered and their malfunctions were set in a first-failure way. The results showed that while the automation malfunction impaired task performance, the performance degradation for information analysis was more severe than response planning. Contrary to other functions, the situation awareness for response planning and response implementation tended to increase during malfunctioning and decrease after. In addition, decreased task performance reduced trust in automation, and malfunctions in earlier stages of information processing resulted in lower trust. Suggestions provided for the design and training related to automation emphasise the importance of high-level cognitive support and the benefit of involving automation error handling in training.

The effects of automation function and malfunction on human performance are important for design and training. The experimental results in this study revealed the significance of high-level cognitive support. Also, introducing automation error handling in training can be helpful in improving situation awareness of the teams.

Wagers for work: Decomposing the costs of cognitive effort.

Some aspects of cognition are more taxing than others. Accordingly, many people will avoid cognitively demanding tasks in favor of simpler alternatives. Which components of these tasks are costly, and how much, remains unknown. Here, we use a novel task design in which subjects request wages for completing cognitive tasks and a computational modeling procedure that decomposes their wages into the costs driving them. Using working memory as a test case, our approach revealed that gating new information into memory and protecting against interference are costly. Critically, other factors, like memory load, appeared less costly. Other key factors which may drive effort costs, such as error avoidance, had minimal influence on wage requests. Our approach is sensitive to individual differences, and could be used in psychiatric populations to understand the true underlying nature of apparent cognitive deficits.

Emergent neural dynamics and geometry for generalization in a transitive inference task.

Relational cognition-the ability to infer relationships that generalize to novel combinations of objects-is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought to be essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) expressed different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

Impacts of decision support systems on cognition and performance for intelligence-gathering path planning.

Decision Support Systems (DSS) are tools designed to help operators make effective choices in workplace environments where discernment and critical thinking are required for effective performance. Path planning in military operations and general logistics both require individuals to make complex and time-sensitive decisions. However, these decisions can be complex and involve the synthesis of numerous tradeoffs for various paths with dynamically changing conditions. Intelligence collection can vary in difficulty, specifically in terms of the disparity between locations of interest and timing restrictions for when and how information can be collected. Furthermore, plans may need to be changed adaptively mid-operation, as new collection requirements appear, increasing task difficulty. We tested participants in a path planning decision-making exercise with scenarios of varying difficulty in a series of two experiments. In the first experiment, each map displayed two paths simultaneously, relating to two possible routes for the two available trucks. Participants selected the optimal path plan, representing the best solution across multiple routes. In the second experiment, each map displayed a single path, and participants selected the best two paths sequentially. In the first experiment, utilizing the DSS was predictive of adoption of more heuristic decision strategies, and that strategic approach yielded more optimal route selection. In the second experiment, there was a direct effect of the DSS on increased decision performance and a decrease in perceived task workload.

Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task.

Despite the rise of decision support systems enabled by artificial intelligence (AI) in personnel selection, their impact on decision-making processes is largely unknown. Consequently, we conducted five experiments (N = 1403 students and Human Resource Management (HRM) employees) investigating how people interact with AI-generated advice in a personnel selection task. In all pre-registered experiments, we presented correct and incorrect advice. In Experiments 1a and 1b, we manipulated the source of the advice (human vs. AI). In Experiments 2a, 2b, and 2c, we further manipulated the type of explainability of AI advice (2a and 2b: heatmaps and 2c: charts). We hypothesized that accurate and explainable advice improves decision-making. The independent variables were regressed on task performance, perceived advice quality and confidence ratings. The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it (i.e., overreliance). Additionally, we found that the effects of source and explainability of advice on the dependent variables were limited. The lack of reduction in participants' overreliance on inaccurate advice when the systems' predictions were made more explainable highlights the complexity of human-AI interaction and the need for regulation and quality standards in HRM.

Contextual design requirements for decision-support tools involved in weaning patients from mechanical ventilation in intensive care units.

Weaning patients from ventilation in intensive care units (ICU) is a complex task. There is a growing desire to build decision-support tools to help clinicians during this process, especially those employing Artificial Intelligence (AI). However, tools built for this purpose should fit within and ideally improve the current work environment, to ensure they can successfully integrate into clinical practice. To do so, it is important to identify areas where decision-support tools may aid clinicians, and associated design requirements for such tools. This study analysed the work context surrounding the weaning process from mechanical ventilation in ICU environments, via cognitive task and work domain analyses. In doing so, both what cognitive processes clinicians perform during weaning, and the constraints and affordances of the work environment itself, were described. This study found a number of weaning process tasks where decision-support tools may prove beneficial, and from these a set of contextual design requirements were created. This work benefits researchers interested in creating human-centred decision-support tools for mechanical ventilation that are sensitive to the wider work system.

AI and the transformation of industrial work: Hybrid intelligence vs double-black box effect.

It is uncertain how the application of artificial intelligence (AI) technology transforms industrial work. We address this question from the perspective of cognitive systems, which, in this case, includes considerations of AI and process transparency, resilience, division of labor, and worker skills. We draw from a case study on glass tempering that includes a machine-vision-based quality control system and an advanced automation process control system. Based on task analysis and background literature, we develop the concept of hybrid intelligence that implies balanced AI transparency that supports upskilling and resilience. So-called fragmented intelligence, in turn, may result from the combination of the complexity of advanced automation along with the complexity of the process physics that places critical emphasis on expert knowledge. This combination can result in the so-called "double black box effect", given that designing for understandability for the line workers might not be feasible: expert networks are needed for resilience.

Investigating the impact of greenery elements in office environments on cognitive performance, visual attention and distraction: An eye-tracking pilot-study in virtual reality.

The human-nature connection is one of the main aspects determining supportive and comfortable office environments. In this context, the application of eye-tracking-equipped Virtual Reality (VR) devices to support an evaluation on the effect of greenery elements indoors on individuals' efficiency and engagement is limited. A new approach to investigate visual attention, distraction, cognitive load and performance in this field is carried out via a pilot-study comparing three virtual office layouts (Indoor Green, Outdoor Green and Non-Biophilic). 63 participants completed cognitive tasks and surveys while measuring gaze behaviour. Sense of presence, immersivity and cybersickness results supported the ecological validity of VR. Visual attention was positively influenced by the proximity of users to the greenery element, while visual distraction from tasks was negatively influenced by the dimension of the greenery. In the presence of greenery elements, lower cognitive loads and more efficient information searching, resulting in improved performance, were also highlighted.

Impact of repetitive mouse clicking on forearm muscle fatigue and mouse aiming performance.

Exercise induced performance fatigue has been shown to impair many aspects of fine motor function in the distal upper limb. However, most fatiguing protocols do not reflect the conditions experienced with computer use. The purpose of this study was to determine how a prolonged, low-force mouse clicking fatigue protocol impacts performance fatigue of the distal upper limb for gamers and non-gamers. Participants completed a total of 1 h of mouse clicking at 5 clicks per second. Muscle fatigue and performance were intermittently assessed. RMS amplitude increased for the forearm flexors throughout the fatigue protocol. Accuracy decreased following the first bout of clicking and returned to baseline values after 40-min. EDC and ECU displayed the greatest muscle activity while aiming, producing 11.4% and 12.9% of MVC, respectively. These findings indicate that mouse clicking may not result in performance fatigue, however, high levels of extensor activity may explain common injuries among gamers.

Equivalent weight: Application of the assessment method on real task conducted by railway workers wearing a back support exoskeleton.

Commonly used risk indexes, such as the NIOSH Lifting Index, do not capture the effect of exoskeletons. This makes it difficult for Health and Safety professionals to rigorously assess the benefit of such devices. The community requires a simple method to assess the effectiveness of back-support exoskeleton's (BSE) in possibly reducing ergonomic risk. The method introduced in this work is termed "Equivalent Weight" (EqW) and it proposes an interpretation of the effect built on the benefit delivered through reduced activation of the erector spinae (ES). This manifests itself as an apparent reduction of the lifted load perceived by the wearer. This work presents a pilot study where a practical application of the EqW method is used to assess the ergonomic risk in manual material handling (MMH) when using a back support exoskeleton (StreamEXO). The results are assessed by combining observational measurements from on-site testing with five different workers and quantitative measures of the muscle activity reduction achieved during laboratory evaluation with ten workers. These results will show that when lifting, lowering, and carrying a 19 kg load the StreamEXO can reduce risk by up to two levels (from "high" to "low") in the target sub-tasks. The Lifting index (LI) was reduced up to 64% when examining specific sub-tasks and the worker's movement conduction.

Analysis of driver behavior at grade-separated intersections to support design.

Understanding driver behaviors in varied traffic scenarios is critical to the design of safe and efficient roadways and traffic control device. This research presents an analysis of driver cognitive workload, situation awareness (SA) and performance for three different scenarios, including a standard intersection and contraflow grade-separated intersections (C-GSI) and quadrant GSI (Q-GSI) with lane assignment sign manipulations. The study used a simulator-based driving experiment with application of the NASA Task Load Index and Situation Awareness Global Assessment Technique to assess the influence of the scenarios on driver behavioral responses. The findings reveal challenges for drivers navigating the C-GSI, characterized by diminished SA and elevated workload. These states were associated with behaviors such as delayed lane changes, missed opportunities for appropriate lane changes, heightened acceleration behavior within deceleration segments, and frequent speeding. In contrast, while drivers in the Q-GSI scenario faced elevated workloads, their SA remained steady, largely due to lane-specific signs facilitating early lane changes. Although the Q-GSI led to increased speed variability and slight increases in deceleration, the use of supplementary speed signage revealed a promising alternative to the S-intersection. Correlation analysis highlighted a significant relationship between mental workload and acceleration responses, indicating that increased acceleration was associated with higher mental workload. In addition, a significant negative correlation between driver perceived performance and absolute lane deviations indicated that drivers with higher self-assessed performance were more accurate in lane-keeping. The study underscores the need for GSIs and signage designs that support driver SA, manage cognitive workload to improve driver performance and increase road safety.

Operator selection for human-automation teaming: The role of manual task skill in predicting automation failure intervention.

Humans working in modern work systems are increasingly required to supervise task automation. We examined whether manual aircraft conflict detection skill predicted participants' ability to respond to conflict detection automation failures in simulated air traffic control. In a conflict discrimination task (to assess manual skill), participants determined whether pairs of aircraft were in conflict or not by judging their relative-arrival time at common intersection points. Then in a simulated air traffic control task, participants supervised automation which either partially or fully detected and resolved conflicts on their behalf. Automation supervision required participants to detect when automation may have failed and effectively intervene. When automation failed, participants who had better manual conflict detection skill were faster and more accurate to intervene. However, a substantial proportion of variance in failure intervention was not explained by manual conflict detection skill, potentially reflecting that future research should consider other cognitive skills underlying automation supervision.

Exposure to neighborhood violence, and laboratory-based and ambulatory cognitive task performance in adulthood.

OBJECTIVE: Exposure to neighborhood violence may have negative implications for adults' cognitive functioning, but the ecological sensitivity of these effects has yet to be determined. We first evaluated the link between exposure to neighborhood violence and two latent constructs of cognitive function that incorporated laboratory-based and ambulatory, smartphone-based, cognitive assessments. Second, we examined whether the effect of exposure to violence was stronger for ambulatory assessments compared to in-lab assessments. METHODS: We used data from 256 urban-dwelling adults between 25 and 65 years old (M = 46.26, SD = 11.07); 63.18% non-Hispanic Black, 9.21% non-Hispanic White, 18.41% Hispanic White, 5.02% Hispanic Black, and 4.18% other. Participants completed baseline surveys on neighborhood exposures, cognitive assessments in a laboratory/research office, and ambulatory smartphone-based cognitive assessments five-times a day for 14 days. RESULTS: Exposure to neighborhood violence was associated with poorer performance in a latent working memory construct that incorporated in-lab and ambulatory assessments, but was not associated with the perceptual speed construct. The effect of exposure to neighborhood violence on the working memory construct was explained by its effect on the ambulatory working memory task and not by the in-lab cognitive assessments. CONCLUSION: This study shows the negative effect that exposure to neighborhood violence may have on everyday working memory performance in urban-dwelling adults in midlife. Results highlight the need for more research to determine the sensitivity of ambulatory assessments to quantify the effects of neighborhood violence on cognitive function.

Investigating underlying brain structures and influence of mild and subjective cognitive impairment on dual-task performance in people with Parkinson's disease.

Cognitive impairment can affect dual-task abilities in Parkinson's disease (PD), but it remains unclear whether this is also driven by gray matter alterations across different cognitive classifications. Therefore, we investigated associations between dual-task performance during gait and functional mobility and gray matter alterations and explored whether these associations differed according to the degree of cognitive impairment. Participants with PD were classified according to their cognitive function with 22 as mild cognitive impairment (PD-MCI), 14 as subjective cognitive impairment (PD-SCI), and 20 as normal cognition (PD-NC). Multiple regression models associated dual-task absolute and interference values of gait speed, step-time variability, and reaction time, as well as dual-task absolute and difference values for Timed Up and Go (TUG) with PD cognitive classification. We repeated these regressions including the nucleus basalis of Meynert, dorsolateral prefrontal cortex, and hippocampus. We additionally explored whole-brain regressions with dual-task measures to identify dual-task-related regions. There was a trend that cerebellar alterations were associated with worse TUG dual-task in PD-SCI, but also with higher dual-task gait speed and higher dual-task step-time variability in PD-NC. After multiple comparison corrections, no effects of interest were significant. In summary, no clear set of variables associated with dual-task performance was found that distinguished between PD cognitive classifications in our cohort. Promising but non-significant trends, in particular regarding the TUG dual-task, do however warrant further investigation in future large-scale studies.