Task Performance Research Articles

Servant leadership’s impact on trust, commitment, and performance in higher education

<span lang="EN-US">This research examined the relationship between servant leadership (SL), organizational trust (OT), organizational commitment (OC), and task performance (TP). It employed a quantitative research design with partial least squares-structural equation modeling (PLS-SEM) and bootstrap estimation. Data were collected through a survey of 111 lecturers from 12 universities in Eastern Indonesia. It found that SL does not affect TP and OC. Meanwhile, OT affects OC but not TP. Further, OC does not affect TP. Therefore, OT does not affect TP through OC. Likewise, SL does not affect TP through OC. It practically implies that universities should be concerned the selection and placement of professional leaders. Successful professional leaders support and enhance the OC and TP of lecturers with SL. This study enriches leadership literature empirically examining SL, TP, OT, and OC in religious higher education. Besides, it provides evidence that SL in tertiary institutions does not exhibit a positive impact on lecturer performance. Institutional limitations lead to difficult situations and allow lecturers to accept reality. Furthermore, it uncovers challenges and demands the need for in-depth investigation of SL studies, which are paradoxical and contradictory to the previous SL theories and findings.</span>

Comparing hybrid models for recognising objects in thermal images at nighttime

This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.

Speeded classification of visual events is sensitive to crossmodal intensity correspondence.

Crossmodal correspondences refer to systematic associations between stimulus attributes encountered in different sensory modalities. These correspondences can be probed in the speeded classification task where they tend to produce congruency effects. This study aimed to replicate and extend previous work conducted by Marks (1987, Experiment 3, Journal of Experimental Psychology: Human Perception and Performance, Vol. 13, No. 3, 384-394) which demonstrated a crossmodal correspondence between auditory and visual intensity attributes. Experiment 1 successfully replicates Marks' original finding that performance in a brightness classification task is affected by whether the loudness of a concurrently presented auditory distractor matches the brightness of the visual target. Furthermore, in line with the original study, we found that this effect was absent in a lightness classification task. In Experiment 2, we demonstrate that loudness-brightness correspondence is robust even when the exact stimulus input changes. This finding suggests that there is a context-dependent mapping between loudness and brightness levels, rather than an absolute mapping between any particular intensity levels. Finally, exploratory analysis using the diffusion model for conflict tasks indicated that evidence from the task-irrelevant modality generates a burst of weak, short-lived automatic activation that can bias decision-making in difficult tasks, but not in easy tasks. Our results provide further evidence for the existence of a flexible crossmodal correspondence between brightness and loudness, which might be helpful in determining one's distance to a stimulus source during the early stages of multisensory integration. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Getting value out of working memory through strategic prioritisation; implications for storage and control.

Working memory is an active system responsible for "the temporary maintenance and processing of information in the support of cognition and action" (Baddeley et al., 2021). In keeping with this, a growing body of research has explored the close links between working memory and attention, and how these might be harnessed to impact performance and possibly improve working memory efficiency. This is theoretically and practically important, given that working memory is a central hub in complex cognition yet is extremely capacity- and resource-limited. We review work carried out over the last ten years or so looking at how high 'value' items in working memory can be strategically prioritised through selective attention, drawing principally from visual working memory paradigms with young adult participants, while also discussing how the core effects extend to different task domains and populations. A consistent set of core findings emerges, with improved memory for items that are allocated higher 'value' but no change in overall task performance, and a recency advantage regardless of point allocation when items are encountered sequentially. Value-directed prioritisation is effortful, under top-down strategic control, and appears to vary with perceptual distraction and executive load. It is driven by processes operating during encoding, maintenance, and retrieval, though the extent to which these are influenced by different features of the task context remain to be mapped out. We discuss implications for working memory, attention, and strategic control, and note some possible future directions of travel for this promising line of research.

Oculomotor behavior can be adjusted on the basis of artificial feedback signals indicating externally caused errors.

Whether a saccade is accurate and has reached the target cannot be evaluated during its execution, but relies on post-saccadic feedback. If the eye has missed the target object, a secondary corrective saccade has to be made to align the fovea with the target. If a systematic post-saccadic error occurs, adaptive changes to the oculomotor behavior are made, such as shortening or lengthening the saccade amplitude. Systematic post-saccadic errors are typically attributed internally to erroneous motor commands. The corresponding adaptive changes to the motor command reduce the error and the need for secondary corrective saccades, and, in doing so, restore accuracy and efficiency. However, adaptive changes to the oculomotor behavior also occur if a change in saccade amplitude is beneficial for task performance, or if it is rewarded. Oculomotor learning thus is more complex than reducing a post-saccadic position error. In the current study, we used a novel oculomotor learning paradigm and investigated whether human participants are able to adapt their oculomotor behavior to improve task performance even when they attribute the error externally. The task was to indicate the intended target object among several objects to a simulated human-machine interface by making eye movements. The participants were informed that the system itself could make errors. The decoding process depended on a distorted landing point of the saccade, resulting in decoding errors. Two different types of visual feedback were added to the post-saccadic scene and we compared how participants used the different feedback types to adjust their oculomotor behavior to avoid errors. We found that task performance improved over time, regardless of the type of feedback. Thus, error feedback from the simulated human-machine interface was used for post-saccadic error evaluation. This indicates that 1) artificial visual feedback signals and 2) externally caused errors might drive adaptive changes to oculomotor behavior.

The effect of VR on fine motor performance by older adults: a comparison between real and virtual tasks

Virtual Reality (VR) technology has the potential to support the aging population and improve testing of daily abilities to detect functional decline. In multiple research studies, VR performance of participants has been assessed by measuring time to complete test, but the effect of learning how to use the VR system and differences between real and virtual environments have been understudied, especially for fine motor tasks. In this study, 20 older adults ages 65–84 performed a task that required fine motor skills in real-life and then in a VR replica of the same task. All participants completed the task in each setting with no difficulties. A clear learning effect was observed in VR, which was attributed to learning how to use the device itself. Still, participants could not reach the same level of performance (time) in VR as in real-life. Participants rated the VR task more mentally and physically demanding than in real-life, as well as more stressful, but with an overall low cognitive demand. In an exploratory cluster analysis, participants with an average age of 69 years old had more technological devices, found the VR system more usable and realistic than participants in the group with an average of 76 years old. This study demonstrated that VR influences time to complete a fine motor task, and that learning effects related to the system could be confounded with actual task performance if not properly considered in VR studies with older adults.

Sensory integration for neuroprostheses: from functional benefits to neural correlates.

In the field of sensory neuroprostheses, one ultimate goal is for individuals to perceive artificial somatosensory information and use the prosthesis with high complexity that resembles an intact system. To this end, research has shown that stimulation-elicited somatosensory information improves prosthesis perception and task performance. While studies strive to achieve sensory integration, a crucial phenomenon that entails naturalistic interaction with the environment, this topic has not been commensurately reviewed. Therefore, here we present a perspective for understanding sensory integration in neuroprostheses. First, we review the engineering aspects and functional outcomes in sensory neuroprosthesis studies. In this context, we summarize studies that have suggested sensory integration. We focus on how they have used stimulation-elicited percepts to maximize and improve the reliability of somatosensory information. Next, we review studies that have suggested multisensory integration. These works have demonstrated that congruent and simultaneous multisensory inputs provided cognitive benefits such that an individual experiences a greater sense of authority over prosthesis movements (i.e., agency) and perceives the prosthesis as part of their own (i.e., ownership). Thereafter, we present the theoretical and neuroscience framework of sensory integration. We investigate how behavioral models and neural recordings have been applied in the context of sensory integration. Sensory integration models developed from intact-limb individuals have led the way to sensory neuroprosthesis studies to demonstrate multisensory integration. Neural recordings have been used to show how multisensory inputs are processed across cortical areas. Lastly, we discuss some ongoing research and challenges in achieving and understanding sensory integration in sensory neuroprostheses. Resolving these challenges would help to develop future strategies to improve the sensory feedback of a neuroprosthetic system.

When Young Scholars Cooperate with LLMs in Academic Tasks: The Influence of Individual Differences and Task Complexities

As a novel AI-powered conversational system, large language models (LLMs) have the potential to be used in various applications. Recent advances in LLMs like ChatGPT have made LLM-based academic tools possible. However, most of the existing studies on the adoption of LLM for academic tasks were based on theoretical or qualitative analyses, which failed to provide empirical evidence on the effects of LLMs on users’ behaviors. Additionally, although previous work has investigated users’ acceptance of conventional conversational systems, little is known about how scholars evaluate LLMs when they are used for academic tasks. Hence, we conducted an empirical field experiment to assess the performance of 48 early-stage scholars on two core academic activities (paper reading and literature reviews) under varying time constraints. Prior to the tasks, participants underwent different training programs about LLM capabilities and limitations. Then, we built a hierarchy dependency network using the Bayesian network. Statistical regression analyses were further conducted to quantify relationships among influential factors of task performance and users’ attitudes toward the LLMs. It was found that young scholars have upheld relatively high academic integrity when using LLMs for academic tasks, and user-LLM performance varied with the task type and time pressure but not with the type of training we used. Further, scholars’ traits can also affect their performance in academic tasks and attitudes towards the LLMs. This work can inspire the future development of LLM-related user training and guide the optimization of LLMs.

An event-scene cooperative analysis network with dual-stream attention convolution module and soft parameter-sharing

Sound event detection (SED) and acoustic scene classification (ASC) are critical tasks in computational environmental sound scene analysis. Both exhibit significant potential across diverse applications, including urban environment detection, smart home integration, and anomaly monitoring. Considering the intricate relationship between acoustic scenes and sound events, researchers have proposed a joint analysis method for SED and ASC tasks using multi-task learning (MTL). However, conventional MTL-based methods rely mainly on a hard parameter-sharing mechanism, which restricts information flow and collaborative learning among tasks during training. To address this issue, this paper proposes a novel MTL method for SED that employs a soft parameter-sharing mechanism to explore the intrinsic correlation between acoustic scenes and sound events. A cross-stitch mechanism is introduced in this study to facilitate soft parameter-sharing between the SED and ASC tasks by employing scene information to improve the performance of the SED tasks and ensure training efficiency. Furthermore, a dual-stream attention convolution module (DACM) is designed to capture global and local contextual information through parallel branches of attention and convolution. To evaluate the performance of the proposed method in SED tasks, we tested two datasets: the TUT Acoustic Scenes 2016/2017 and TUT Sound Events 2016/2017 datasets and the synthetic sound scene datasets. Experimental results indicate that the proposed MTL method outperforms state-of-the-art approaches, demonstrating that acoustic scene information can improve the performance of SED tasks.

Neuromodulatory effects of parietal high-definition transcranial direct-current stimulation on network-level activity serving fluid intelligence.

Fluid intelligence (Gf) involves rational thinking skills and requires the integration of information from different cortical regions to resolve novel complex problems. The effects of non-invasive brain stimulation on Gf have been studied in attempts to improve Gf, but such studies are rare and the few existing have reached conflicting conclusions. The parieto-frontal integration theory of intelligence (P-FIT) postulates that the parietal and frontal lobes play a critical role in Gf. To investigate the suggested role of parietal cortices, we applied high-definition transcranial direct current stimulation (HD-tDCS) to the left and right parietal cortices of 39 healthy adults (age 19-33years) for 20min in three separate sessions (left active, right active and sham). After completing the stimulation session, the participants completed a logical reasoning task based on Raven's Progressive Matrices during magnetoencephalography. Significant neural responses at the sensor level across all stimulation conditions were imaged using a beamformer. Whole-brain, spectrally constrained functional connectivity was then computed to examine the network-level activity. Behaviourally, we found that participants were significantly more accurate following left compared to right parietal stimulation. Regarding neural findings, we found significant HD-tDCS montage-related effects in brain networks thought to be critical for P-FIT, including parieto-occipital, fronto-occipital, fronto-parietal and occipito-cerebellar connectivity during task performance. In conclusion, our findings showed that left parietal stimulation improved abstract reasoning abilities relative to right parietal stimulation and support both P-FIT and the neural efficiency hypothesis. KEY POINTS: Abstract reasoning is a critical component of fluid intelligence and is known to be served by multispectral oscillatory activity in the fronto-parietal cortices. Recent studies have aimed to improve abstract reasoning abilities and fluid intelligence overall through behavioural training, but the results have been mixed. High-definition transcranial direct-current stimulation (HD-tDCS) applied to the parietal cortices modulated task performance and neural oscillations during abstract reasoning. Left parietal stimulation resulted in increased accuracy and decreased functional connectivity between occipital regions and frontal, parietal, and cerebellar regions. Future studies should investigate whether HD-tDCS alters abstract reasoning abilities in those who exhibit declines in performance, such as healthy ageing populations.

The Effects of Eye Illuminance Distribution in the Horizontal Field of View on Human Performance in a Home Paper-Based Learning Situation

Previous studies have focused on task/ambient illumination for visual effects and eye illumination for non-visual effects. In this context, eye illumination within the non-visual realm was defined as vertical non-visual eye illuminance. Considering the functional specificity of central vision and peripheral vision, this study aims to explore whether the distribution of eye illuminance in the horizontal field of view (FOV) affects human performance in home paper-based learning settings. In this study, a within-subject design was used to investigate the effects of eye illuminance distribution on mental perception, task performance, and physiological health while maintaining constant task illuminance and correlated color temperature (CCT). The findings revealed that eye illuminance and its distribution in the horizontal FOV had complex effects on visual fatigue, Landolt ring performance, heart rate variability, and luminous environment appraisal. A relatively optimal lighting configuration was suggested—Scene 4, which was characterized by an eye illuminance level in central FOV of 186 lx and an “m” shaped eye illuminance distribution pattern. This indicates the importance of considering eye illuminance distribution in the horizontal FOV, rather than solely focusing on vertical eye illuminance.

Task repetition versus task rehearsal: Understanding effects of task-readiness factors and elemental genres on L2 writing task performance

In the literature on task-based language teaching (TBLT), task repetition and task rehearsal have frequently been used interchangeably. However, this distinction, identified as potentially significant (Ellis, 2019), is noteworthy due to the potential impact of participants’ awareness of future repetitions on second language (L2) task performance. Given the lack of empirical exploration regarding task repetition and task rehearsal distinction in the context of L2 writing, this study investigates how task rehearsal and task repetition affect the narrative and argumentative writing performances of 120 students of English as a second language (ESL) from a U.S. university. They were randomly assigned to four groups. Groups 1 and 3 wrote the narrative text initially, repeated the same task, and then proceeded to write the argumentative text, which was also repeated. The intervals between these performances were one week apart. However, Group 3 knew in advance that they were going to repeat the task, whereas Group 1 lacked this knowledge. Conversely, Groups 2 and 4 began by writing the argumentative text, repeated that task, and then proceeded to complete the narrative text, which was also repeated within the same time intervals. Group 4 had forewarning of their future performance, while Group 2 did not. We analysed the essays using the complexity, accuracy, lexis, and fluency (CALF) measures. The results indicated that time significantly influenced almost all CALF measures in narrative and argumentative writing, irrespective of the forewarning condition. Additionally, task readiness and essay order had a significant impact on most CALF measures, particularly in narrative writing. Specifically, task readiness positively affected the majority of CALF measures in narratives, whereas essay order exhibited sporadic influence.

Professional Scepticism, Auditor Experience, and Self-efficacy on Audit Judgement

In the performance of all audit tasks, auditors must adhere to pre-determined standards. In Indonesia, there have been cases of fraud related to auditors, including the case of fraud at SNP Finance in 2018. Such situations can arise due to incorrect audit judgment. Factors that may impact audit judgment include professional skepticism, auditor experience, and self-efficacy. A quantitative approach is used in this research. The study population consisted of auditors at public accountant firms in Bandar Lampung. During data collection, questionnaires were administered directly to external auditors. The data were analyzed using SmartPLS. The study revealed new evidence suggesting that professional skepticism only has a limited effect on aiding auditors in their audit judgments, even when combined with extensive auditor experience. This limitation can occur when the auditor has a short working period. Conversely, self-efficacy has been shown as a beneficial factor in helping auditors make well-informed audit decisions. Keywords: professional skepticism, auditor experience, self-efficacy, audit judgment

Individual differences in emotion-induced blindness: Are they reliable and what do they measure?

The emotion-induced-blindness (EIB) paradigm has been extensively used to investigate attentional biases to emotionally salient stimuli. However, the low reliability of EIB scores (the difference in performance between the neutral and emotionally salient condition) limits the effectiveness of the paradigm for investigating individual differences. Here, across two studies, we investigated whether we could improve the reliability of EIB scores. In Experiment 1, we introduced a mid-intensity emotionally salient stimuli condition, with the goal of obtaining a wider range of EIB magnitudes to promote reliability. In Experiment 2, we sought to reduce the attentional oddball effect, so we created a modified EIB paradigm by removing the filler images. Neither of these approaches improved the reliability of the EIB scores. Reliability for the high- and mid-intensity EIB difference scores were low, while reliability of the scores for absolute performance (neutral, high-, and mid-intensity) were high and the scores werealso highly correlated, even though overall performance in the emotionally salient conditions were significantly worse than in the neutral conditions. Given these results, we can conclude that while emotionally salient stimuli impair performance in the EIB task compared with the neutral condition, the strong correlation between the emotionally salient and neutral conditions means that while EIB can be used to investigate individual differences in attentional control, it is not selective to individual differences in attentional biases to emotionally salient stimuli.

Enhancing touch sensibility with sensory electrical stimulation and sensory retraining

A large proportion of stroke survivors suffer from sensory loss, negatively impacting their independence, quality of life, and neurorehabilitation prognosis. Despite the high prevalence of somatosensory impairments, our understanding of somatosensory interventions such as sensory electrical stimulation (SES) in neurorehabilitation is limited. We aimed to study the effectiveness of SES combined with a sensory discrimination task in a well-controlled virtual environment in healthy participants, setting a foundation for its potential application in stroke rehabilitation. We employed electroencephalography (EEG) to gain a better understanding of the underlying neural mechanisms and dynamics associated with sensory training and SES. We conducted a single-session experiment with 26 healthy participants who explored a set of three visually identical virtual textures—haptically rendered by a robotic device and that differed in their spatial period—while physically guided by the robot to identify the odd texture. The experiment consisted of three phases: pre-intervention, intervention, and post-intervention. Half the participants received subthreshold whole-hand SES during the intervention, while the other half received sham stimulation. We evaluated changes in task performance—assessed by the probability of correct responses—before and after intervention and between groups. We also evaluated differences in the exploration behavior, e.g., scanning speed. EEG was employed to examine the effects of the intervention on brain activity, particularly in the alpha frequency band (8–13 Hz) associated with sensory processing. We found that participants in the SES group improved their task performance after intervention and their scanning speed during and after intervention, while the sham group did not improve their task performance. However, the differences in task performance improvements between groups only approached significance. Furthermore, we found that alpha power was sensitive to the effects of SES; participants in the stimulation group exhibited enhanced brain signals associated with improved touch sensitivity likely due to the effects of SES on the central nervous system, while the increase in alpha power for the sham group was less pronounced. Our findings suggest that SES enhances texture discrimination after training and has a positive effect on sensory-related brain areas. Further research involving brain-injured patients is needed to confirm the potential benefit of our solution in neurorehabilitation.

The arousal level of consciousness required for working memory performance: An anaesthesia study.

Regarding the stage of arousal level required for working memory to function properly, limited studies have been conducted on changes in working memory performance when the arousal level of consciousness decreases. This study aimed to experimentally clarify the stages of consciousness necessary for optimal working memory function. In this experiment, the sedation levels were changed step-by-step using anaesthesia, and the performance accuracy during the execution of working memory was assessed using a dual-task paradigm. Participants were required to categorize and remember words in a specific target category. Categorization performance was measured across four different sedative phases: before anaesthesia (baseline), and deep, moderate and light stages of sedation. Short-delay recognition tasks were performed under these four sedative stages, followed by long-delay recognition tasks after participants recovered from sedation. The results of the short-delay recognition task showed that the performance was lowest at the deep stage. The performance of the moderate stage was lower than the baseline. In the long-delay recognition task, the performance under moderate sedation was lower than that under baseline and light sedation. In addition, the performance under light sedation was lower than that under baseline. These results suggest that task performance becomes difficult under half sedation and that transferring information to long-term memory is difficult even under one-quarter sedation.

Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates

Deep neural feedforward networks are effective models for a wide array of problems, but training and deploying such networks presents a significant energy cost. Spiking neural networks (SNNs), which are modeled after biologically realistic neurons, offer a potential solution when deployed correctly on neuromorphic computing hardware. Still, many applications train SNNs offline, and running network training directly on neuromorphic hardware is an ongoing research problem. The primary hurdle is that back-propagation, which makes training such artificial deep networks possible, is biologically implausible. Neuroscientists are uncertain about how the brain would propagate a precise error signal backward through a network of neurons. Recent progress addresses part of this question, e.g., the weight transport problem, but a complete solution remains intangible. In contrast, novel learning rules based on the information bottleneck (IB) train each layer of a network independently, circumventing the need to propagate errors across layers. Instead, propagation is implicit due the layers' feedforward connectivity. These rules take the form of a three-factor Hebbian update a global error signal modulates local synaptic updates within each layer. Unfortunately, the global signal for a given layer requires processing multiple samples concurrently, and the brain only sees a single sample at a time. We propose a new three-factor update rule where the global signal correctly captures information across samples via an auxiliary memory network. The auxiliary network can be trained a priori independently of the dataset being used with the primary network. We demonstrate comparable performance to baselines on image classification tasks. Interestingly, unlike back-propagation-like schemes where there is no link between learning and memory, our rule presents a direct connection between working memory and synaptic updates. To the best of our knowledge, this is the first rule to make this link explicit. We explore these implications in initial experiments examining the effect of memory capacity on learning performance. Moving forward, this work suggests an alternate view of learning where each layer balances memory-informed compression against task performance. This view naturally encompasses several key aspects of neural computation, including memory, efficiency, and locality.

Performance on clinical outcomes, activities of daily living anduser experience on head-mounted displays for people withvision impairment.

To compare the objective performance, acceptance and usability of head-mounted displays (HMDs) to provide evidence-based data that could be used to increase the efficiency of device referrals based upon a person's vision loss and functional needs. A cross-sectional, counterbalanced, individually controlled crossover study was performed on 15 adults with various eye conditions. Performance was measured when using four HMDs: eSight4, Eyedaptic EYE3, Eyedaptic EYE4 and IrisVision Inspire. Performance on clinical visual acuity tests and contrast were assessed, as well as vision-related activities of daily living (ADL) which were divided into three categories: Reading, Searching & Identifying and Eye-hand Coordination. User-experience was also assessed. Logistic regression analyses, Friedman one-way repeated measure analyses of variance by ranks and multivariate permutation testing were used for analysis. There was a significant improvement in visual acuity when using all devices. For contrast tasks, only the eSight4 and Eyedaptic EYE3 improved performance relative to baseline. For most Reading and Searching & Identifying tasks, the odds of being able to perform the tasks were significantly higher while using the devices. However, the actual performance with most devices (e.g., number of words read or reading speed) did not improve significantly over baseline for most tasks. For the Eye-hand Coordination tasks, participants performed equivalent to or significantly poorer than baseline when using the devices. No demographic or clinical predictors of outcomes were identified. Participants expressed dissatisfaction with the devices' effectiveness, acceptability and usability. While performance on clinical tests was better when using the devices, performance on most real-world ADLs was equal to or worse than baseline. No single device improved performance on all tasks, and performance on any one task was not improved with all the devices. The overall dissatisfaction with the devices paralleled the lack of objective improvement in the performance of real-world tasks.

The Effect of Icon Color Combinations in Information Interfaces on Task Performance under Varying Levels of Cognitive Load

In human–computer interaction interfaces, icons serve as highly symbolic elements that convey information and significantly influence the performance of visual search and other tasks. Thus, the selection of appropriate colors plays a crucial role in the design of human–computer interfaces. This study aimed to investigate the effects of icon color combinations on human visual search task performance across various cognitive load conditions. The experiment was divided into two parts, involving a cognitive load test experiment and an icon search task, wherein the former required participants to select the target icon from 16 de-colored icons, and the latter involved selecting the target icon from 16 color combinations of 8 colors (background colors: black, red, blue, and purple; foreground colors: white, yellow, green, and turquoise). A total of 20 participants (11 females and 9 males, all aged between 18 and 24 years) were recruited for this experiment to perform both tasks sequentially. Through a comprehensive consideration of factors such as the NASA-TLX scale, retrieval time, etc., the experimental results revealed that cognitive load increased with time pressure, resulting in varying optimal color combinations for different cognitive load levels. Additionally, background colors did not affect task response time under different cognitive loads, whereas white foreground color was superior to turquoise foreground color in a medium cognitive load environment. Meanwhile, our results recommend prioritizing white-on-black as the preferred color combination, given that it demonstrated excellent cognitive performance in all three cognitive load environments. Conversely, white-on-blue is not recommended as a color combination for the design of high cognitive load environments. Concerning medium cognitive load environments, yellow-on-red or white-on-purple color combinations were preferred. Finally, in low cognitive load environments, white-on-purple is recommended as the primary color combination. Overall, this study provides a theoretical reference for the future design of interactive interface icons across various contexts.

Perturbation-based dual task assessment in older adults with mild cognitive impairment

BackgroundDual tasking (i.e., concurrent performance of motor and cognitive task) is significantly impaired in older adults with mild cognitive impairment (OAwMCI) compared to cognitively intact older adults (CIOA) and has been associated with increased fall risk. Dual task studies have primarily examined volitionally driven events, and the effects of mild cognitive impairment on reactive balance control (i.e., the ability to recover from unexpected balance threats) are unexplored. We examined the effect of cognitive tasks on reactive balance control in OAwMCI compared to CIOA.MethodsAdults >55 years were included and completed the Montreal Cognitive Assessment (MoCA) to categorize them as OAwMCI (MoCA: 18–24, n = 15) or CIOA (MoCA: ≥25, n = 15). Both OAwMCI [MoCA: 22.4 (2.2), 65.4 (6.1) years, 3 females] and CIOA [MoCA: 28.4 (1.3), 68.2 (5.5) years, 10 females] responded to large magnitude stance slip-like perturbations alone (single task) and while performing perceptual cognitive tasks targeting the visuomotor domain (target and tracking game). In these tasks, participants rotated their head horizontally to control a motion mouse and catch a falling target (target game) or track a moving object (track). Margin of stability (MOS) and fall outcome (harness load cell >30% body weight) were used to quantify reactive balance control. Cognitive performance was determined using performance error (target) and sum of errors (tracking). A 3 × 2 repeated measures ANOVA examined the effect of group and task on MOS, and generalized estimating equations (GEE) model was used to determine changes in fall outcome between groups and tasks. 2 × 2 repeated measures ANOVAs examined the effect of group and task on cognitive performance.ResultsCompared to CIOA, OAwMCI exhibited significantly deteriorated MOS and greater number of falls during both single task and dual task (p < 0.05), and lower dual task tracking performance (p < 0.01). Compared to single task, both OAwMCI and CIOA exhibited significantly deteriorated perceptual cognitive performance during dual task (p < 0.05); however, no change in MOS or fall outcome between single task and dual task was observed.ConclusionCognitive impairment may diminish the ability to compensate and provide attentional resources demanded by sensory systems to integrate perturbation specific information, resulting in deteriorated ability to recover balance control among OAwMCI.