Improved Task Performance Research Articles

Addressing the Need for a Specialized Disconnection Device in Catheter Connection Management: A Case Study of User-Centered Medical Device Innovation

Background/Objectives: Improvements in catheter connection design intended to increase safety have resulted in connections that are difficult to release manually. No medical device exists to safely disconnect catheter connections. Nurses and other users have developed workarounds including use of hemostats, tourniquets, and wrenches. These workarounds are not always successful for performing this task and can break catheters and catheter connections. This study aimed to evaluate a disconnection device to safely disconnect catheter connections. Methods: This is a mixed-methods study using a user-centered design approach with triangulation of quantitative and qualitative data mapped to Valdez’s sociotechnical framework. Nurses (N = 139) from units across two academic medical centers encompassing diverse patient populations engaged in usability testing and surveys. Data about users’ past catheter disconnection experiences and usability of the specialized disconnection device were collected and analyzed. Triangulation of quantitative data and qualitative themes was mapped using Valdez’s socio-technical framework to complement and strengthen the final design generated for nurses’ user requirements. Results: Ninety-five percent of nurses reported previous difficulty with disconnecting luer connections; 93% of those reporting difficulty improvised with readily available medical devices or products to better grip the connected parts. Over 85% of nurses reported positive experiences using the specialized disconnection device; others suggested design improvements for better performance. Conclusions: The nurses who tested the developed disconnection device reported high acceptability, accessibility, ease of use, and improved task performance. Moreover, as workarounds develop at points of practice where no systematic solution exists, aiming product development activities at these points help close gaps in achieving and maintaining patient safety. This study was not registered.

Cost-benefit tradeoff mediates the transition from rule-based to memory-based processing during practice.

Practice not only improves task performance but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We test the hypothesis that strategy transitions in task learning can result from decision-making guided by cost-benefit analysis. Participants learn 2 task sequences and are then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis and the model-predicted strategy transition points align with the observed behavioral slowing. Model comparisons using behavioral data show that strategy transitions are better explained by a cost-benefit analysis across alternative strategies rather than solely on memory strength. Model-guided fMRI findings suggest that the brain encodes a decision variable reflecting the cost-benefit analysis and that different strategy representations are double-dissociated. Further analyses reveal that strategy transitions are associated with activation patterns in the dorsolateral prefrontal cortex and increased pattern separation in the ventromedial prefrontal cortex. Together, these findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

RAMIE: retrieval-augmented multi-task information extraction with large language models on dietary supplements.

To develop an advanced multi-task large language model (LLM) framework for extracting diverse types of information about dietary supplements (DSs) from clinical records. We focused on 4 core DS information extraction tasks: named entity recognition (2949 clinical sentences), relation extraction (4892 sentences), triple extraction (2949 sentences), and usage classification (2460 sentences). To address these tasks, we introduced the retrieval-augmented multi-task information extraction (RAMIE) framework, which incorporates: (1) instruction fine-tuning with task-specific prompts; (2) multi-task training of LLMs to enhance storage efficiency and reduce training costs; and (3) retrieval-augmented generation, which retrieves similar examples from the training set to improve task performance. We compared the performance of RAMIE to LLMs with instruction fine-tuning alone and conducted an ablation study to evaluate the individual contributions of multi-task learning and retrieval-augmented generation to overall performance improvements. Using the RAMIE framework, Llama2-13B achieved an F1 score of 87.39 on the named entity recognition task, reflecting a 3.51% improvement. It also excelled in the relation extraction task with an F1 score of 93.74, a 1.15% improvement. For the triple extraction task, Llama2-7B achieved an F1 score of 79.45, representing a significant 14.26% improvement. MedAlpaca-7B delivered the highest F1 score of 93.45 on the usage classification task, with a 0.94% improvement. The ablation study highlighted that while multi-task learning improved efficiency with a minor trade-off in performance, the inclusion of retrieval-augmented generation significantly enhanced overall accuracy across tasks. The RAMIE framework demonstrates substantial improvements in multi-task information extraction for DS-related data from clinical records.

Explainable AI improves task performance in human–AI collaboration

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human–AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, ) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, ) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.

Ergonomics in Laparoscopic Box Training and Virtual Reality (VR) Simulations: A Systematic Review.

Laparoscopic surgery has now become the gold standard in managing most surgical cases. Despite its advantages, working hours and in-theatre training restrictions have prompted trainees to explore alternatives like virtual reality (VR) simulations and box training. Furthermore, given the increased frequency of minimally invasive surgery and the prevalence of musculoskeletal issues among surgeons, there's a growing effort to optimize ergonomics. There is currently a lack of focus on ergonomics in training, prompting our study to inspect VR and box training's ergonomic outcomes and their impact on task performance. This systematic review was conducted at Barts Cancer Institute, Queen Mary University, from January 27, 2024, to April 22, 2024. Multiple databases were searched electronically from January 1980 to March 2024, including only articles that utilized electromyography (EMG) or the NASA Task Load Index (NASA-TLX) to measure ergonomics. Two reviewers independently conducted eligibility assessment, data extraction, and risk of bias evaluation, with discrepancies resolved through consensus discussions. Ultimately, 12 studies were selected. EMG results indicated a higher %MVC (maximum voluntary contraction) in box training in poor ergonomic settings and a negative correlation between skills and muscle activity. Both modalities showed a significant decrease in NASA-TLX scores after training on the simulators and when comparing novice and experienced surgeons. Furthermore, a reduction in physical demand and improved task performance was observed, with significant differences found between experts and novices in a VR appendicectomy scenario. This systematic review reveals reduced muscle activity and physical demand among trained individuals in laparoscopic surgery with both box trainers and VR simulators, emphasizing the importance of addressing ergonomic considerations. To advance our understanding of surgical ergonomics, the standardization of measurement methods and higher-quality evidence, particularly through randomized controlled trials, are recommended.

Human Activity Recognition in an Open World

Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current stateof-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released.

Rivals reloaded: Adapting to sample-based speed-accuracy trade-offs through competitive pressure.

Collecting an adequate amount of information for a decision is an important skill. However, previous experiments on speed-accuracy trade-offs in sample-based decisions revealed marked oversampling that was impervious to various interventions (Fiedler, McCaughey, et al., 2021). When faced with the threat of being preempted by a rival in making decisions, participants seem to reduce information search substantially (Phillips et al., 2014). Such a decrease provides unique opportunities for metareasoning, which should advance people's understanding of the task and improve their performance. To test this possibility, in the present research (N = 101), participants had to compete with a fast (computer-simulated) rival and indeed substantially reduced self-determined sample size compared to a control condition. This speed increase also carried over to a subsequent decision block without rival, albeit participants regressed to a slower strategy. Mere exposure to a teammate using small samples either in an equivalent competitive version of the task or the standard solitary version led to similar reductions in sample size. This demonstrates that competition is not a necessary requirement for participants to make use of the metareasoning opportunity to improve task performance. Further research is needed to uncover the metacognitive underpinnings of improving performance and facilitate people taking full advantage of such opportunities for metareasoning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Emotion perception and online awareness following alcohol-intoxication: investigating possible deficits using the complex audio visual emotion assessment task.

Alcohol-intoxication is implicated in negative social behaviours, however the mechanisms underlying this relationship are poorly understood. Impaired emotion perception following alcohol consumption may partially account for this link, however limited methodology in prior studies undermines the efficacy of this explanation. The current study investigated the effect of acute moderate-dose alcohol-intoxication on basic and compound emotion perception abilities using contextualised video vignettes. Self-appraisals of performance accuracy were also investigated. Sixty-eight participants consumed a beverage containing either (a) an alcohol dose calculated to achieve a BrAC of 0.08%, or (b) a placebo. The Complex Audio-Visual Emotion Assessment Task (CAVEAT) was used to assess emotion perception ability. Anticipatory performance accuracy and emergent confidence judgements were made on the CAVEAT. There were no significant between-group differences on emotion perception ability and emergent confidence judgements. However, anticipatory performance accuracy was more aligned to actual performance in the alcohol intoxication group compared to the placebo group. Overall, these results suggest that (1) deficits in perception of facial emotional expressions following alcohol intoxication may not be as pronounced as originally suspected; and (2) the questioning of performance accuracy may prompt intoxicated individuals to anticipate poorer emotion perception performance, which may lead to better monitoring of-and improvements in-task performance.

Internal states emerge early during learning of a perceptual decision-making task.

Recent work has shown that during perceptual decision-making tasks, animals frequently alternate between different internal states or strategies. However, the question of how or when these emerge during learning remains an important open problem. Does an animal alternate between multiple strategies from the very start of training, or only after extensive exposure to a task? Here we address this question by developing a dynamic latent state model, which we applied to training data from mice learning to perform a visual decision-making task. Remarkably, we found that mice exhibited distinct "engaged" and "biased" states even during early training, with multiple states apparent from the second training session onward. Moreover, our model revealed that the gradual improvement in task performance over the course of training arose from a combination of two factors: (1) increased sensitivity to stimuli across all states; and (2) increased proportion of time spent in a higher-accuracy "engaged" state relative to biased or disengaged states. These findings highlight the power of our approach for characterizing the temporal evolution of multiple strategies across learning.

Longitudinal markers of cognitive procedural learning in fronto-striatal circuits and putative effects of a BDNF plasticity-related variant

Procedural learning and automatization have widely been studied in behavioral psychology and typically involves a rapid improvement, followed by a plateau in performance throughout repeated training. More recently, brain imaging studies have implicated frontal-striatal brain circuits in skill learning. However, it is largely unknown whether frontal-striatal activation during skill learning and behavioral changes follow a similar learning curve pattern. To address this gap in knowledge, we performed a longitudinal brain imaging study using a procedural working memory (pWM) task with repeated measurements across two weeks to map the temporal dynamics of skill learning. We additionally explored the effect of the BDNF Val66Met polymorphism, a common genetic polymorphism impacting neural plasticity, to further inform the relevance of the identified neural markers. We used linear and exponential modeling to characterize procedural learning by means of learning curves on the behavioral and brain functional level. We show that repeated training led to an exponential decay in a distributed set of brain regions including fronto-striatal circuits, which paralleled the exponential improvement in task performance. In addition, we show that both behavioral and neurofunctional readouts were sensitive to the BDNF Val66Met polymorphism, suggesting less efficient learning in 66Met-allele carriers along with protracted signal decay in frontal and striatal brain regions. Our results extend existing literature by showing the temporal relationship between procedural learning and frontal-striatal brain function and suggest a role of BDNF in mediating neural plasticity for establishing automatized behavior.

Navigating Uncertainty: The Role of Mood and Confidence in Decision-Making Flexibility and Performance.

Dealing with uncertainty is a pivotal skill for adaptive decision-making across various real-life contexts. Cognitive models suggest that individuals continuously update their knowledge based on past choices and outcomes. Traditionally, uncertainty has been linked to negative states such as fear and anxiety. Recent evidence, however, highlights that uncertainty can also evoke positive emotions, such as surprise, interest, excitement, and enthusiasm, depending on one's task expectations. Despite this, the interplay between mood, confidence, and learning remains underexplored. Some studies indicate that self-reported mood does not always align with confidence, as these constructs evolve on different timescales. We propose that mood influences confidence, thereby enhancing decision flexibility-defined as the ability to switch effectively between exploration and exploitation. This increased flexibility is expected to improve task performance by increasing accuracy. Our findings support this hypothesis, revealing that confidence modulates exploration/exploitation strategies and learning rates, while mood affects reward perception and confidence levels. These findings indicate that metacognition entails a dynamic balance between exploration and exploitation, integrating mood states with high-level cognitive processes.

Grounding with Structure: Exploring Design Variations of Grounded Human-AI Collaboration in a Natural Language Interface

Selecting an effective utterance among countless possibilities that match a user's intention poses a challenge when using natural language interfaces. To address the challenge, we leveraged the principle of least collaborative effort in communication grounding theory and designed three grounded conversational interactions: 1) a grounding interface allows users to start with a provisional input and then invite a conversational agent to complete their input, 2) a multiple grounding interface presents multiple inputs for the user to select from, and 3) a structured grounding interface guides users to write inputs in a structure best understood by the system. We compared our three grounding interfaces to an ungrounded control interface in a crowdsourced study (N=80) using a natural language system that generates small programs. We found that the grounding interfaces reduced cognitive load and improved task performance. The structured grounding interface further reduced speaker change costs and improved technology acceptance, without sacrificing the perception of control. We discuss the implications of designing grounded conversational interactions in natural language systems.

Should I Evaluate My Augmented Reality System in an Industrial Environment? Investigating the Effects of Classroom and Shop Floor Settings on Guided Assembly.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Processing Transition during Practice.

Practice not only improves task performance but also changes task execution from rule-to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We test the hypothesis that strategy transitions in task learning can result from decision-making guided by cost-benefit analysis. Participants learn two task sequences and are then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis, and the model-predicted strategy transition points align with the observed behavioral slowing. Model comparisons using behavioral data show that strategy transitions are better explained by a cost-benefit analysis across alternative strategies rather than solely on memory strength. Model-guided fMRI findings suggest that the brain encodes a decision variable reflecting the cost-benefit analysis and that different strategy representations are double-dissociated. Further analyses reveal that strategy transitions are associated with activation patterns in the dorsolateral prefrontal cortex and increased pattern separation in the ventromedial prefrontal cortex. Together, these findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

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).

The interplay of resilience, stress, mental wellbeing and task performance: Implications for sustainable workplaces

Understanding relationships between stress, resilience, mental wellbeing, and task-performance is critical for success in today’s sustainable workplaces. Thus, we aimed to analyse and develop a management framework to deal with this criticality. Inspired by Salutogenesis theory – prioritizing positive variables over the absence of negative ones, our emphasis was on resilience and mental wellbeing for stress management and improving task performance. Data from 445 employees was collected by a survey instrument employing standardised scales. Reliability and validity of constructs were established through the measurement model, while the structural model tested the strength of the relationships. Low stress and high resilience were identified as having a strong effect on mental wellbeing, which in-turn improved task-performance. This study highlights that resilience and mental wellbeing, in addition to stress management, significantly improves task performance for sustainable workplaces.

Development of means for suppressing the negative effects of interaction between the pilot and reentry and other high-speed vehicles

Several novel active and passive means for the suppression of pilot-induced oscillations are considered in the paper. The potential of the proposed means in eliminating Category I, II, and III pilot-induced oscillations was studied in ground-based simulation for two controlled element dynamics corresponding to an aerospace vehicle and a second-generation supersonic transport. The experiments demonstrated that the highest effectiveness in suppressing pilot-induced oscillations and improving task performance is provided by integrating both proposed active suppressors with one of the passive prefilters.

Robotic-Assisted Navigation in Single-Level Transforaminal Lumbar Interbody Fusion Reduces Surgeons' Mental Workload Compared With Fluoroscopic and Computed Tomographic Techniques: A Nonrandomized Prospective Controlled Trial.

Pedicle screw placement during spine fusion is physically and mentally demanding for surgeons. Consequently, spine surgeons can become fatigued, which has implications for both patient safety and surgeon well-being. We sought to assess the cognitive workload of surgeons placing pedicle screws using robotic-assisted navigation compared with fluoroscopic and computed tomography (CT)-assisted placement. We performed a nonrandomized prospective controlled trial to compare the cognitive workload of 3 surgeons performing single-level minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) using robotic, CT, or fluoroscopic navigation on 25 patients (15 in the robotic navigation group and 10 in the nonrobotic group). Immediately after each procedure, surgeons submitted the National Aeronautics and Space Administration-Task Load Index (NASA-TLX), which has 6 subscales: mental demands, physical demands, temporal demands, performance, effort, and frustration. Four tasks associated with pedicle screw placement were assessed independently: (1) screw planning, (2) calibrating robot/obtaining imaging/registration, (3) pedicle cannulation, and (4) screw placement. Patient demographics and surgical characteristics were obtained and reviewed. Surgeons' self-reported cognitive workload was significantly reduced when using robotic-assisted navigation versus CT/fluoroscopic navigation. Workload was reduced for screw planning, pedicle cannulation, and screw placement. In addition, there were significant reductions in each subdomain for these 3 tasks, encompassing mental demand, physical demand, temporal demand, effort, and frustration with improved task performance. This study found significant reductions in mental workload with improved perceived performance for robotic-assisted pedicle screw placement compared with fluoroscopic and CT-navigation techniques. Lowering the cognitive burden associated with screw placement may allow surgeons to address the remainder of the operative case with less decision fatigue, prevent complications, and increase surgeon wellness.

Reducing discomfort by integrating unpleasant environmental sounds with cognitive sound control

We respond to unpleasant sounds in our daily lives by blocking out sound environments that we find unpleasant. This may be because of our helplessness in interfering with these sounds. However, blocking limits the range of sound selection. To reduce discomfort from unpleasant sound, rather than blocking the sound environment, this study aimed to make unpleasant sounds less bothersome by integrating them into music. To achieve this, we expect that participants will intervene in the sound environment where there is unpleasant sound, recognize that the environment is controllable, and reduce the unpleasantness of the sound. If participants can consider unpleasant sounds as part of the environment they can control, they can create an environment in which unpleasant sounds do not bother them. In this study, melody generated based on the rhythm of the unpleasant sound was played, and experiments were conducted to cognitively mask the unpleasant sound based on the perception that it can interfere with the environment as the melody can be adjusted. Results showed that manipulating the environment altered the perception of unpleasant sounds and improved task performance by reducing their unpleasantness. Improved performance may be due to task-specific self-efficacy.

Investigations of motor performance with neuromodulation and exoskeleton using leader-follower modality: a tDCS study.

This study investigates how the combination of robot-mediated haptic interaction and cerebellar neuromodulation can improve task performance and promote motor skill development in healthy individuals using a robotic exoskeleton worn on the index finger. The authors propose a leader-follower type of mirror game where participants can follow a leader in a two-dimensional virtual reality environment while the exoskeleton tracks the index finger motion using an admittance filter. The game requires two primary learning phases: the initial phase focuses on mastering the pinching interface, while the second phase centers on predicting the leader's movements. Cerebral transcranial direct current stimulation (tDCS) with anodal polarity is applied to the subjects during the game. It is shown that the subjects' performance improves as they play the game. The combination of tDCS with finger exoskeleton significantly enhances task performance. Our research indicates that modulation of the cerebellum during the mirror game improves the motor skills of healthy individuals. The results also indicate potential uses for motor neurorehabilitation in hemiplegia patients.