Selection Task Research Articles

On the localization of reward effects in overlapping dual tasks.

In dual-task (DT) situations, performance deteriorates compared with single-task situations. Such performance decrements are frequently explained with the serial scheduling of the response selection stages constituting a bottleneck. Proof of this assumption stems from the observation that response times for the second task (task 2; RT 2) increase with decreasing stimulus-onset asynchrony (SOA).In this study, we investigated how the reward prospect for task 1 performance affects task 1 and task 2 processing. For that purpose, we relied on the psychological refractory period paradigm (PRP) as a chronometric tool, to determine the locus of the reward effect in the processing chain of both tasks.We obtained improved task 1 and task 2 performance; as indicated by reduced RTs in the reward compared to the no reward condition of task 1 and task 2. Furthermore, the reward effect propagated at short SOA from task 1 onto task 2, suggesting that the locus of the reward effect can be pinpointed before or at the bottleneck of task 1. Importantly, the mean reward effect on task 1 was increased compared to task 2, thus indicating that parts of the reward effect were not propagated onto task 2, therefore affecting task 1 motor processes.In Experiment 2, we tested for the locus of the effect propagation to task 2. Therefore, we implemented a difficulty manipulation of the response selection of task 2. The results indicate that the reward effect is propagated from task 1 onto the response selection stage of task 2.

A common factor underlying individual differences in confirmation bias.

When they are asked to test a given hypothesis, individuals tend to be biased towards confirming evidence. This phenomenon has been documented on different cognitive components: information search, weighing of evidence, and memory recall. However, the interpretation of these observations has been debated, and it remains unclear whether they truly reflect a confirmation bias (as opposed to e.g., a bias towards positive information). In the present study we aimed at evaluating whether these biases might be subtended by a common factor. We adapted three classic experimental paradigms on hypothesis testing (Wason selection task, 2-4-6 task, and interviewee task) and examined the relation between these biases using an individual differences approach. Participants (N = 200) completed a total of nine behavioral tasks, in which each component of confirmation bias was measured in each of the three experimental paradigms. Correlations and factor analyses within a multitrait-multimethod framework indicated greater convergence of bias scores within each component across paradigms, than within experimental paradigms. This suggests that a common factor underlies the different measurements of confirmation bias across experimental paradigms, at least to some extent. In these paradigms, thus, biases towards confirming evidence may truly reflect a confirmation bias.

Fuzzy task assignment in heterogeneous distributed multi-robot system

This study addresses the problem of coordination in cooperative multi-robot systems performing complex tasks. An analysis of cooperative behavior in mobile multi-robot systems in terms of task execution accuracy by heterogeneous robots is carried out. In addition, we evaluate the capacity and compatibility of tasks assigned to robots to optimize task execution without using direct communication with the robots or a central decision-making unit. A model for task selection in heterogeneous distributed multi-robot systems is proposed. It is based on two processes: the first decomposes complex tasks into elementary tasks, and the second assigns elementary tasks to mobile robots for real-time execution. The distribution of elementary tasks is NP-hard, which leads us to recommend approximate solutions. A fuzzy system called Fuzzy Decision Making in Task Selection is proposed, which uses fuzzy logic to solve this problem. This system allows robots to choose to perform any task in the future. An approach is presented that uses two cascading fuzzy systems. The first calculates the utility of the robot and then activates the second fuzzy system to calculate the utility of the task. By using the output of the fuzzy decision system in our model, each robot will be able to decide for itself which tasks to perform. The results of a simulation of mobile robots transporting goods demonstrate the effectiveness of this fuzzy decision-maker.

Bias and Its Consequences : A Study of Machine Learning Performance

This paper addresses the concern about bias affecting the results of machine learning models. For this purpose, it uses the Adult Income dataset from OpenML for income classification. The conditions for bias are induced by underrepresenting people that earn <= $50K in training data, thus checking the behavior of different models when encountering such a skewed distribution. Key metrics, namely accuracy and specificity (True Negative Rate), were analyzed for unbiased and biased training scenarios. The results show that Naive Bayes and Random Forest models were resistant to bias, but others, including SVM and Logistic Regression, suffered major performance drops. This study throws light on the robustness of different classifiers when exposed to biased data, requiring further bias mitigation strategies in real-world applications. This paper actually examines critically how bias in training data can significantly affect the performance of prediction, fairness, and model selection in income classification tasks.

Children's beliefs about Black and White men's and women's scientific knowledge: An intersectional approach.

Children are sensitive to other's knowledge and social characteristics when seeking out information, but little is known about how adults' gender and race interact to influence children's beliefs about adults' knowledge. In two studies, 5-8-year-olds (N = 257; 127 girls; 130 boys; 73% White) saw photos of Black and White men and women and rated each adults' science knowledge. In Study 1, children then viewed four adult faces together (one from each gender and race) and chose who knew the most and second-most about the answer to a scientific question. In Study 2, the selection task was modified so that children saw two faces from different categories and chose one, and children were then asked to identify one of four individuals as a scientist. In both studies, children also chose which of four individuals they would want to learn about science from. Children gave similar knowledge ratings to men and women and to Black and White individuals when they rated one adult at a time. However, when children selected the most knowledgeable adult, they showed an ingroup gender-based preference whose strength varied with child age. In both studies, children also showed an ingroup gender-based learning preference, but showed no preferences based on adult race. Children referred to adults' appearance most often when justifying their learning preference and which individual they believed to be a scientist. Together, these findings suggest that, for primarily White American children, a potential adult informant's gender may be more salient than race when evaluating science knowledge. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Novel Snow Leopard Optimization for High-Dimensional Feature Selection Problems

To address the limitations of traditional optimization methods in achieving high accuracy in high-dimensional problems, this paper introduces the snow leopard optimization (SLO) algorithm. SLO is a novel meta-heuristic approach inspired by the territorial behaviors of snow leopards. By emulating strategies such as territory delineation, neighborhood relocation, and dispute mechanisms, SLO achieves a balance between exploration and exploitation, to navigate vast and complex search spaces. The algorithm’s performance was evaluated using the CEC2017 benchmark and high-dimensional genetic data feature selection tasks, demonstrating SLO’s competitive advantage in solving high-dimensional optimization problems. In the CEC2017 experiments, SLO ranked first in the Friedman test, outperforming several well-known algorithms, including ETBBPSO, ARBBPSO, HCOA, AVOA, WOA, SSA, and HHO. The effective application of SLO in high-dimensional genetic data feature selection further highlights its adaptability and practical utility, marking significant progress in the field of high-dimensional optimization and feature selection.

Mapping punishment avoidance learning deficits in non-suicidal self-injury in young adults with and without borderline personality disorder: An fMRI study

IntroductionNon-suicidal self-injury (NSSI) is a growing public health concern among young adults in both clinical and non-clinical settings. Despite evidence linking NSSI to alterations in learning from reward and punishment, this area remains understudied, especially in non-clinical populations without borderline personality disorder (BPD). MethodsWe employed a modified version of the Probabilistic Stimulus Selection (PSS) task in two groups of young adults with recurrent NSSI, with (NSSI+BPD) and without BPD (NSSI), and an additional group of healthy controls (HC). While undergoing functional magnetic resonance imaging (fMRI), participants were asked to choose between pairs of stimuli with different reward probabilities. In the training phase, they received probabilistic feedback and learned to identify the most rewarding option within fixed pairs. In the test phase, these learned stimuli were recombined into novel pairs, where participants' accuracy in selecting the most rewarding and avoiding the most punishing options reflected their ability to learn from reward and punishment, respectively. ResultsCompared to HC, participants in the NSSI and NSSI+BPD groups were less accurate at avoiding the most punishing options than at choosing the most rewarding options, and showed reduced activity in the nucleus accumbens (NAcc) during punishment avoidance relative to reward selection. LimitationsThe modest sample size, descriptive rather than modeling approach, and absence of ecological momentary assessments may limit the results. ConclusionFindings suggest that reduced activation of the NAcc when avoiding loss may underlie difficulties in learning to avoid punishment in young adults with NSSI, regardless of the presence of BPD.

Fitness and historical success information-assisted binary particle swarm optimization for feature selection

Feature selection is a critical preprocessing step in machine learning aimed at identifying the most relevant features or variables from a dataset. Although conventional particle swarm optimization (PSO) has shown efficiency for feature selection tasks, developing an effective PSO algorithm for this task is still challenging. This study proposes a fitness and historical success information-assisted binary particle swarm optimization, denoted by FPSO. The FPSO is developed by integrating different search strategies, including a weighted center-based approach, historical information-based acceleration coefficients, and selection operation. These strategies are embedded into the FPSO to enhance the levels of exploration and exploitation based on the fitness value of particles and their historical search status. In the FPSO, the transfer function is also added to transform the continuous search space into binary search space. Experimental validation and comparison with seven other metaheuristic algorithms on 24 datasets verify the effectiveness of the FPSO in eliminating irrelevant and redundant features.

Modeling and solving time-sensitive task allocation for USVs with mixed capabilities

Efficient task allocation for Unmanned Surface Vehicles (USVs) in maritime operations is crucial for optimal resource utilization and mission success. This paper introduces a task assignment model that accounts for the capabilities and time constraints associated with the heterogeneity of USVs. An Extended-Restriction Multiple Traveling Salesmen Problem (ER-MTSP) model is formulated, encapsulating the diverse capabilities of USVs and time restrictions. To solve this problem, a Fuzzy Enhanced Non-Dominated Sorting Genetic Algorithm (FENSGA-II) is developed, incorporating adaptive random testing initialization and customized hierarchical operators to generate high-quality Pareto frontiers. The fuzzy-linguistic satisfactory degree then re-evaluates the linguistic importance preferences of the objectives within the Pareto set, aiding in reasonable decision-making. Additionally, a two-phase refinement strategy balances individual task values and structure topology, enabling flexible task selection in emergency situations. Simulations and lake trials conducted across various problem variants demonstrate the effectiveness of the model. The results highlight the superiority of our approach compared to current combinatorial optimization methods, providing enhanced solutions across different problem variations.

Impact of Balanced Exploration and Exploitation on Highdimensional Feature Selection with Hierarchical Whale Optimisation Algorithm

High-dimensional datasets (HDDs) pose significant challenges in feature selection due to their complex nature. While metaheuristic algorithms like the Whale Optimisation Algorithm (WOA) have shown promise in addressing these challenges, stability issues are least addressed. Through an extensive literature review, it was identified that stability is manifested in the equilibrium of exploration and exploitation. Hence, this study introduced the Hierarchical Whale Optimisation Algorithm (HiWOA), an approach designed to enhance the WOA’s stability and performance in HDD feature selection tasks. The HiWOA incorporates a two-phase strategy comprising a nonlinear control parameter based on the arcsine function and a hierarchical position-update mechanism adapted from the Grey Wolf Optimiser. The proposed HiWOA was evaluated through 23 benchmark optimisation functions and feature selection experiments on 11 medical HDDs. The results indicate that the HiWOA outperformed the WOA and a modified variant (mWOA) in terms of a better fitness value, a more balanced exploration-exploitation ratio, and improved classification accuracy with fewer selected features. These findings demonstrate the HiWOA’s effectiveness in enhancing stability, making it a robust solution for high-dimensional optimisation and feature selection.

Perawatan Preventive Maintenance Mobil Satlantas Model Sedan Mazda dengan Metode Reliability Centered Maintenance pada Polres Kota X

The aim of this research is to find out the preventive maintenance of Mazda Sedan Model Traffic Cars using the reliability centered maintenance method at the City functional block diagram, determining system function and functional failure, failure mode and effect analysis, logic tree analysis, and task selection. The results of this research are that the maintenance system for traffic vehicle units carries out routine check-ups every 5,000 kilometers or every 3 months and for brake and tire components every 10,000 kilometers or 6 months, the damage that appears with the current maintenance system is the break caliper (right front), hose (water pump), and ball joint, factors that arise with current maintenance problems are safety problems, proposed improvements that need to be made to resolve the problem are preventive maintenance for the break caliper (right front), hose (water pump), and ball joint every 11000 hours or 458 days.

Exploring Pointer Enhancement Techniques for Target Selection on Large Curved Display

Large curved displays are becoming increasingly popular due to their ability to provide users with a wider field of view and a more immersive experience compared to flat displays. Current interaction techniques for large curved displays often assume a user is positioned at the display's centre, crucially failing to accommodate general use conditions where the user may move during use. In this work, we investigated how user position impacts pointing interaction on large curved displays and evaluated cursor enhancement techniques to provide faster and more accurate performance across positions. To this effect, we conducted two user studies. First, we evaluated the effects of user position on pointing performance on a large semi-circular display (3m-tall, 3270R curvature) through a 2D Fitts' Law selection task. Our results indicate that as users move away from the display, their pointing speed significantly increases (at least by 9%), but accuracy decreases (by at least 6%). Additionally, we observed participants were slower when pointing from laterally offset positions. Secondly, we explored which pointing techniques providing motor- and visual-space enhancements best afford effective pointing performance across user positions. Across a total of six techniques tested, we found that a combination of acceleration and distance-based adjustments with cursor enlargement significantly improves target selection speed and accuracy across different user positions. Results further show techniques with visual-space enhancements (e.g., cursor enlargement) are significantly faster and more accurate than their non-visually-enhanced counterparts. Based on our results we provide design recommendations for implementing cursor enhancement techniques for large curved displays.

Comparison of Unencumbered Interaction Technique for Head-Mounted Displays

Head Mounted Displays (HMDs) are gaining more public attention. With the advancement of tracking technologies, they are incorporating unencumbered interaction techniques to address the need for user-friendly and efficient interaction techniques for day-to-day activities. While there is a good understanding of the different interaction techniques individually, very little research has been done to compare them directly. This would be vital to understanding their strengths and weaknesses in different contexts and building better synergies among them. This paper uses a target selection task to compare the performance and user preferences for four interaction techniques: gaze-pinch, ray pointer, hand-proximate user interface, and direct mid-air interactions. Results indicate that the gaze-pinch interaction technique required significantly more time to complete the task than the others, whose time to complete was similar. However, in terms of preferences and errors, the interaction techniques mostly performed similar.

Effects of wearing a surgical face mask on cognitive functioning and mood states: a randomised controlled trial in young adults.

Despite significant public concerns voiced about wearing face masks and reports from healthcare workers of adverse effects on cognition, research into potential adverse effects remains limited. The present trial investigated the effects of wearing a surgical face mask for prolonged periods on cognitive functioning and mood. We tested 42 university students (18-36years old) using a controlled counterbalanced crossover design that involved a mask session and a control session, separated by 1week. The two sessions were identical except that on the day of the mask session, participants were asked to wear a surgical mask for at least 8h and to continue wearing it while visiting our laboratory, during which cognitive performance and mood were assessed as per the control session. Results showed that participants reported feeling less happy and more tense during the mask session compared to no-mask control. Additionally, cognitive performance differed between the two sessions for a selective attention task, reflecting slower response latencies during the mask session, which for the most part appeared to be driven by those who felt anxious wearing the mask. Although significant differences emerged for only two of six mood scales and one of eight cognitive tests, the evidence of adverse effects in a university population signals a need for research investigating vulnerable populations. Individuals with elevated anxiety may be particularly important to target.Trail Registration: This randomized controlled trial was retrospectively registered (ACTRN12620001215910). Date registered: 16/11/2020, retrospectively registered.

Research on hybrid strategy Particle Swarm Optimization algorithm and its applications

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional Particle Swarm Optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy Particle Swarm Optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly Optimization Algorithm (BOA), Ant Colony Optimization (ACO), and Firefly Algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

A feedback matrix based evolutionary multitasking algorithm for high-dimensional ROC convex hull maximization

Multi-objective evolutionary algorithms have shown their competitiveness in solving ROC convex hull maximization. However, due to “the curse of dimensionality”, few of them focus on high-dimensional ROCCH maximization. Therefore, in this paper, a feedback matrix (FM)-based evolutionary multitasking algorithm, termed as FM-EMTA, is proposed. In FM-EMTA, to tackle “the curse of dimensionality”, a feature importance based low-dimensional task construction strategy is designed to transform the high-dimensional ROCCH maximization task into several low-dimensional tasks. Then, each low-dimensional task evolves with a population. To ensure that the low-dimensional task achieves a better ROCCH, an FM-based evolutionary multitasking operator is proposed. Specifically, for each low-dimensional task i, the element FM(i,j) in feedback matrix is defined to measure the degree that the low-dimensional task j could assist task i. Based on it, an FM-based assisted task selection operator and an FM-based knowledge transfer operator are developed to constitute the evolutionary multitasking operator, with which the useful knowledge is transferred among the low-dimensional tasks. After the evolution, the best ROCCHs obtained by the low-dimensional tasks are combined together to achieve the final ROCCH on the original high-dimensional task. Experiments on twelve high-dimensional datasets with different characteristics demonstrate the superiority of the proposed FM-EMTA over the state-of-the-arts in terms of the area under ROCCH, the hypervolume indicator and the running time.

LOCATE-SM PROTOCOL: LOCATION ORIENTED CORTICAL AND SUB-CORTICAL ASSESSMENT FOR THERAPEUTIC EVALUATION- SENSORY-MOTOR TESTING IN AWAKE CRANIOTOMY

Abstract AIMS Awake craniotomy is a well-established surgical procedure to debulk intracranial tumours in eloquent areas (‘functional hubs’). Intraoperative mapping facilitates maximal safe debulking. Review of current sensory motor assessment tools highlighted the absence of a comprehensive ‘location to function’ protocol. We aimed to develop a new protocol that enables ‘location to function’ sensory-motor task selection in preparation for, and during, awake craniotomy. METHOD A literature review demonstrated multiple different intraoperative strategies exist for the preservation of sensory-motor function in awake craniotomy. Incorporating these with functional neuroanatomy, and consensus agreement in the Tessa Jowell Academy Physiotherapy Awake Craniotomy Working Group and the National Brain Mapping MDT, led to the development of testing strategies aligned with ‘location to function’ task selection. Testing strategies were peer-reviewed by three specialist Neuro-Oncology physiotherapists. RESULTS A protocol (LOCATE-SM) has been developed at Leeds to identify critical cortical and subcortical sensory-motor functions that are aligned with anatomo-functional models. Physiotherapists have used this to select individualised pre-, intra- and postoperative tests depending on tumour location and sensory-motor impairment. Additionally, this facilitates intraoperative mapping of eloquent functional regions that are variably located. Cases using LOCATE-SM are presented. CONCLUSION LOCATE-SM is a new awake sensory-motor testing protocol developed by specialised Neuro-Oncology physiotherapists at a Tessa Jowell Centre of Excellence (Leeds). Current cases demonstrate that it leads to greater consistency between physiotherapists and specificity in ‘location to function’ sensory-motor task selection. Future work will systematically analyse the outcomes (onco-functional balance) of using this protocol, and validation in other centres.

An Exploratory Study of the Impact of Task Selection Strategies on Worker Performance in Crowdsourcing Microtasks

In microtask crowdsourcing systems like Amazon Mechanical Turk (AMT) and Appen Figure-Eight, workers often employ task selection strategies, completing sequences of tasks to maximize earnings. While previous literature has explored the effects of sequential tasks with varying complexities of the same type, there is a lack of knowledge on the consequences when multiple types of tasks with similar levels of difficulty are performed. This study examines the impact of sequences of three frequently employed task types, namely image classification, text classification, and surveys, on workers' engagement, accuracy, and perceived workloads. In addition, we analyze the influence of workers' personality traits on their strategies for selecting tasks. Our study, which involved 558 participants using AMT, found that engaging in sequences of distinct task types had a detrimental effect on classification task engagement and accuracy. It also increases the perceived task load and the worker's frustration. Nevertheless, the precise order of tasks does not significantly impact these results. Moreover, we showed a slight association between personality traits and the workers' selection strategy for the tasks. The results offered valuable knowledge for designing an efficient and inclusive crowdsourcing platform.

Comparing typing methods for uppercase input in virtual reality: Modifier Key vs. alternative approaches

Typing tasks are basic interactions in a virtual environment (VE). The presence of uppercase letters affects the meanings of words and their readability. By typing uppercase letters on a QWERTY keyboard, the layers can be switched using a modifier key. Considering that VE controllers are typically used in a VE, this input method can result in user fatigue and errors. Thus, this study proposed new alternative interactions for the modifier key input and compared their typing performance and user experience. In an experiment, 30 participants were instructed to type 10 sentences using different typing interaction methods (shift, long press, and double-tap) on a virtual keyboard in a VE. The typing speed, error rate, and number of backspace inputs were measured to compare typing performance. Upon the completion of the typing task, the usability, workload, and sickness associated with each typing method were evaluated. The results showed that the double-tap method exhibited significantly higher typing speed, error rate, ease of use, satisfaction, and workload. This result is consistent with those of previous studies demonstrating that selection tasks were more efficient with fewer hand movements. Thus, this study implies that the double-tap method can be considered as a potential typing interaction for the VEs instead of the traditional method using the shift as a modifier key. Therefore, this study is expected to contribute to the design and development of user-friendly interactions.

Large language model uncertainty proxies: discrimination and calibration for medical diagnosis and treatment.

The inability of large language models (LLMs) to communicate uncertainty is a significant barrier to their use in medicine. Before LLMs can be integrated into patient care, the field must assess methods to estimate uncertainty in ways that are useful to physician-users. Evaluate the ability for uncertainty proxies to quantify LLM confidence when performing diagnosis and treatment selection tasks by assessing the properties of discrimination and calibration. We examined confidence elicitation (CE), token-level probability (TLP), and sample consistency (SC) proxies across GPT3.5, GPT4, Llama2, and Llama3. Uncertainty proxies were evaluated against 3 datasets of open-ended patient scenarios. SC discrimination outperformed TLP and CE methods. SC by sentence embedding achieved the highest discriminative performance (ROC AUC 0.68-0.79), yet with poor calibration. SC by GPT annotation achieved the second-best discrimination (ROC AUC 0.66-0.74) with accurate calibration. Verbalized confidence (CE) was found to consistently overestimate model confidence. SC is the most effective method for estimating LLM uncertainty of the proxies evaluated. SC by sentence embedding can effectively estimate uncertainty if the user has a set of reference cases with which to re-calibrate their results, while SC by GPT annotation is the more effective method if the user does not have reference cases and requires accurate raw calibration. Our results confirm LLMs are consistently over-confident when verbalizing their confidence (CE).