Selection Task Research Articles

Thinking through ACAD - learning to see theory in action

The Activity-Centred Analysis and Design (ACAD) has been developed as a meta-theoretical framework for understanding and improving complex networked learning situations (Goodyear et al., 2021; Goodyear & Carvalho, 2014). ACAD helps to foreground two distinct moments related to the design of complex learning situations. The first entails advanced planning – and it involves design time – when an educator may consider the selection of specific tasks, tools, and complementary social arrangements of a learning situation. The second involves learning time – or what happens as a learning activity unfolds. When designing for networked learning educators need to anticipate a certain form of human activity and consider how designable elements may influence what students do (first view). But educators also need to channel what actually happens on the day, as activity unfolds, noticing how designable elements influence (or not) the intended learning activity (second view). One difficulty for teachers or educational designers relates to being able to draw connections between what has been designed (planned) and what learners actually do (learn).

Quiz as an effective technology for forming the professional personality of medical students in the aspect of the basic values of Zhana Kazakstan

The article is devoted to the current problem of the formation of the professional personality of medical students from the point of view of basic values, such as citizenship and responsibility, corresponding to the new demands of society, lying in the plane of the modern Concept of Zhana Kazakhstan. The purpose of the article is to formulate the principle of the effectiveness of using a quiz in classroom and extracurricular work with students of medical specialties. The authors offer a specific scenario for the quiz «Medical Karaganda», and at the same time describe in detail the tasks and the course of the game. The tasks are based on materials on the history of medicine in Karaganda, including unique archival sources, materials collected in museums and other sources. The quiz also involves conducting it in the second language that the students are studying (in this case, Russian for students in groups with Kazakh as the language of tuition). The article analyzes both the effectiveness of individual tasks and the quiz as a whole. The analysis was carried out based on the results of testing a quiz in the classroom (NС JSC «Karaganda Medical University», 1st year students) and as part of the «City Day» event at the private institution «Bolashaq Academy» with students of the specialty «Pharmacy». Analysis of the results obtained during feedback and questioning proved the effectiveness of the quiz in the formation of basic values (patriotism, responsibility, critical thinking) and allowed us to formulate the principle of effectiveness based on a careful selection of tasks and interdisciplinary connections.

An Improved Binary Crayfish Optimization Algorithm for Handling Feature Selection Task in Supervised Classification

Feature selection (FS) is a crucial phase in data mining (DM) and machine learning (ML) tasks, aimed at removing uncorrelated and redundant attributes to enhance classification accuracy. This study introduces an improved binary crayfish optimization algorithm (IBCOA) designed to tackle the FS problem. The IBCOA integrates a local search strategy and a periodic mode boundary handling technique, significantly improving its ability to search and exploit the feature space. By doing so, the IBCOA effectively reduces dimensionality, while improving classification accuracy. The algorithm’s performance was evaluated using support vector machine (SVM) and k-nearest neighbor (k-NN) classifiers on eighteen multi-scale benchmark datasets. The findings showed that the IBCOA performed better than nine recent binary optimizers, attaining 100% accuracy and decreasing the feature set size by as much as 0.8. Statistical evidence supports that the proposed IBCOA is highly competitive according to the Wilcoxon rank sum test (alpha = 0.05). This study underscores the IBCOA’s potential for enhancing FS processes, providing a robust solution for high-dimensional data challenges.

Electrotactile BCI for Top-Down Somatosensory Training: Clinical Feasibility Trial of Online BCI Control in Subacute Stroke Patients.

This study investigates the feasibility of a novel brain-computer interface (BCI) device designed for sensory training following stroke. The BCI system administers electrotactile stimuli to the user's forearm, mirroring classical sensory training interventions. Concurrently, selective attention tasks are employed to modulate electrophysiological brain responses (somatosensory event-related potentials-sERPs), reflecting cortical excitability in related sensorimotor areas. The BCI identifies attention-induced changes in the brain's reactions to stimulation in an online manner. The study protocol assesses the feasibility of online binary classification of selective attention focus in ten subacute stroke patients. Each experimental session includes a BCI training phase for data collection and classifier training, followed by a BCI test phase to evaluate online classification of selective tactile attention based on sERP. During online classification tests, patients complete 20 repetitions of selective attention tasks with feedback on attention focus recognition. Using a single electroencephalographic channel, attention classification accuracy ranges from 70% to 100% across all patients. The significance of this novel BCI paradigm lies in its ability to quantitatively measure selective tactile attention resources throughout the therapy session, introducing a top-down approach to classical sensory training interventions based on repeated neuromuscular electrical stimulation.

Investigating an effort avoidance account of attentional strategy choice.

People often choose suboptimal attentional control strategies during visual search. This has been at least partially attributed to the avoidance of the cognitive effort associated with the optimal strategy, but aspects of the task triggering such avoidance remain unclear. Here, we attempted to measure effort avoidance of an isolated task component to assess whether this component might drive suboptimal behavior. We adopted a modified version of the Adaptive Choice Visual Search (ACVS), a task designed to measure people's visual search strategies. To perform optimally, participants must make a numerosity judgment-estimating and comparing two color sets-before they can advantageously search through the less numerous of the two. If participants skip the numerosity judgment step, they can still perform accurately, albeit substantially more slowly. To study whether effort associated with performing the optional numerosity judgment could be an obstacle to optimal performance, we created a variant of the demand selection task to quantify the avoidance of numerosity judgment effort. Results revealed a robust avoidance of the numerosity judgment, offering a potential explanation for why individuals choose suboptimal strategies in the ACVS task. Nevertheless, we did not find a significant relationship between individual numerosity judgment avoidance and ACVS optimality, and we discussed potential reasons for this lack of an observed relationship. Altogether, our results showed that the effort avoidance for specific subcomponents of a visual search task can be probed and linked to overall strategy choices.

The Roles of Rule Type and Word Term in the Deductive Reasoning of Adults with and without Dyslexia.

Despite its importance to everyday functioning, reasoning is underexplored in developmental dyslexia. The current study investigated verbal deductive reasoning on the Wason selection task, not previously used in dyslexia research despite its well-established pedigree. Reasoning rule was manipulated, with the conditional rules varying in the logical values presented. The word frequency and imageability of the word terms was also manipulated. Twenty-six adults with dyslexia and 31 adults without dyslexia completed Wason selection task problems. No group difference in reasoning accuracy or completion time was found. However, the participants were most accurate when reasoning with the rule type "If p, then not q" and least accurate with the rule type "If p then q". More trials were also answered correctly when the word terms were highly imageable but of average word frequency. These findings are in line with the general reasoning literature. Dyslexia status did not interact with either rule type or word term type. The study expands upon previous research by testing verbal deductive reasoning in dyslexia, highlighting the role of imageability in facilitating reasoning performance for all, regardless of the presence or absence of dyslexia. Implications for the design of educational materials are considered.

Effect of L-theanine on selective attention in a traffic-related reaction task in sleep-deprived young adults: a double-blind placebo-controlled, crossover study

ABSTRACT Background L-theanine is a non-protein-forming amino acid found in tea. Limited evidence suggests that it improves selective attention. Sleep deprivation impairs attention and psychomotor reactions, affecting automobile driving. We aimed to determine whether L-theanine improves neurobehavioral measures of visual attention in acutely sleep-deprived healthy adults in a traffic-scene-based attention task. Methods In a double-blind, placebo-controlled, counterbalanced, two-way crossover study, we compared the effects of a 200-mg dose of L-theanine with a placebo (150 ml of distilled water) on a computerised, traffic-scene-based visual recognition reaction task in 24 healthy volunteers (age 20–25 years; 13 males) sleep-deprived overnight. The participants made speeded button-presses to imminent accident scenes (i.e. hits), while ignoring safe scenes. They were tested pre-dose and 45 min post-dose, each treatment administered one week apart. Results Hit rates were more than 90% in all sessions, and were similar in two treatments, pre- vs post-dose. L-theanine significantly reduced false alarms (i.e. responses to safe scenes) (p = 0.014) and increased A′ (i.e. target-distractor discriminability) (p = 0.009), whereas placebo did not (p > 0.05). L-theanine reduced hit reaction time by 38.65 ms (p = 0.007), and placebo by 19.08 ms (p = 0.016), however reaction time changes from baseline were not significantly different between treatments (p > 0.05). Conclusions L-theanine in high doses appears to improve selective visual attention by concurrently improving information processing speed and target-distractor discriminability in acutely sleep-deprived individuals. This is consistent with previous functional neuroimaging findings, where L-theanine suppressed distractor-processing and default-mode-network activity in visual selective attention tasks.

On a dynamic and decentralized energy-aware technique for multi-robot task allocation

In the real world, multi-robot systems need to deal with on-the-fly (runtime) arrivals of new sets of tasks. This entails repeated adjustments of their current task allocations to include the newer ones while also ensuring that the overall performance does not degrade. This paper proposes a decentralized and distributed dynamic task allocation algorithm to handle this issue in a multi-robot scenario. The proposed work provides a conflict-free allocation of a set of tasks constituting a job to robots and minimizes the total execution time. These jobs can comprise multiple independent and/or dependent tasks or a combination thereof, which are injected on-the-fly into a network of robots. The dependent tasks of a job are related by precedence constraints that specify the ordering or dependencies between pairs of tasks. The work also describes a decentralized adaptive energy threshold mechanism for determining whether or not a robot needs to visit a battery stockpile after the execution of a task. Conflicting task selections among the robots in this decentralized set-up are resolved using mobile agents during runtime. Apart from allocating tasks to the robots, these mobile agents exploit the benefits of centralized and decentralized systems and provide an advantage over auction-based task allocation algorithms. The proposed algorithm takes into consideration the energy requirements, both during the task allocation process and actual execution. The proposed algorithm also caters to strategies to deal with delays caused by obstacles and congestion during the actual execution of the tasks. Experiments conducted using Webots, an open-source robot simulator, and Tartarus, a multi-agent platform, authenticate the efficacy of the proposed algorithm compared to other prominent task allocation algorithms in terms of minimization of average waiting time, total task allocation time, total job allocation time, and total execution time of an experiment.

The JavaScript Package Selection Task: A Comparative Experiment Using an LLM-based Approach

The JavaScript Package Selection Task: A Comparative Experiment Using an LLM-based Approach

NICER: A New and Improved Consumed Endurance and Recovery Metric to Quantify Muscle Fatigue of Mid-Air Interactions

Natural gestures are crucial for mid-air interaction, but predicting and managing muscle fatigue is challenging. Existing torque-based models are limited in their ability to model above-shoulder interactions and to account for fatigue recovery. We introduce a new hybrid model, NICER , which combines a torque-based approach with a new term derived from the empirical measurement of muscle contraction and a recovery factor to account for decreasing fatigue during rest. We evaluated NICER in a mid-air selection task using two interaction methods with different degrees of perceived fatigue. Results show that NICER can accurately model above-shoulder interactions as well as reflect fatigue recovery during rest periods. Moreover, both interaction methods show a stronger correlation with subjective fatigue measurement ( ρ = 0.978/0.976) than a previous model, Cumulative Fatigue ( ρ = 0.966/0.923), confirming that NICER is a powerful analytical tool to predict fatigue across a variety of gesture-based interactive applications.

Spatio-temporal task pricing for shared electric micro-mobility battery-swapping platform with reinforcement learning

Spatial crowdsourcing has emerged in shared electric micro-mobility platforms, compensating occasional drivers (ODs) per task of swapping micro-mobility batteries. As ODs autonomously select tasks only when satisfied with predetermined compensation and travel distance, a traditional uniform pricing strategy results in possible low task completion. To resolve the imbalance between ODs and tasks, this study introduces a spatio-temporal pricing strategy where task prices differ by region and time interval. Considering the daily variations in task distribution and OD availability, the goal is to minimise the platform costs equal to the sum of total OD wages and penalties for uncompleted tasks. The reinforcement learning approach with proximal policy optimisation (PPO) is implemented to generate real-time continuous task prices. A domain-specific masking technique is incorporated to improve the learning process by disregarding the data from inactive grids in loss calculations. Computational results show that the PPO agent strategically raises prices in regions with insufficient ODs according to the OD density level. Further comparison with the mixed integer programming model with perfect information on ODs' willingness-to-accept parameters demonstrates the superior capability of our algorithm in navigating the uncertainties of OD task acceptance. A sensitivity analysis provides insights into the decision of system parameters.

Asymptotic teleportation scheme bridging between standard and port-based teleportation

Various modified quantum teleportation schemes are proposed to overcome experimental constraints or to meet specific application requirements for quantum communication. Hence, most schemes are developed and studied with unique methodologies, each with its own inherent challenges. Our research focuses on interconnecting these schemes, which appear to be unrelated to each other, based on the idea that the unique advantages of one scheme can compensate for the limitations of another. In this paper, we introduce an asymptotic teleportation scheme that requires the receiver to complete a classical selection task before performing a quantum correction. This scheme bridges standard teleportation with port-based teleportation through the transformation of joint measurements. Specifically, we categorize and analytically investigate protocols within this scheme for qubit systems. Given that the linear optics teleportation protocol without ancilla qubits is contained in the two non-trivial groups, we provide a novel perspective on its expansion. Furthermore, we discuss the potential application of a protocol from one of these groups as a universal programmable processor and extend these protocols to higher-dimensional systems while maintaining the same properties and potential, providing the analytic form of the joint measurement and its performance. These results thereby propose new avenues for developing a quantum network in higher-dimensional systems.

Do Children with Autism Spectrum Disorders Show Selective Trust in Social Robots?

Previous researches suggest that social robots can facilitate the learning of children with Autism Spectrum Disorder (ASD) by enhancing their interests, engagement, and attention. However, there is limited understanding regarding whether children with ASD can learn directly from the testimony of social robots and whether they can remain vigilant based on the perceived accuracy of these robots. Therefore, the present study was conducted to examine whether children with ASD demonstrated selective trust towards social robots. Twenty-nine children with ASD between ages of 4-7 years, and 38 typically-developing (TD) age and IQ-matched peers participated in classic selective trust tasks. During the tasks, they learned the names of novel objects from either a pair of social robots or a pair of human informants, where one informant had previously been established as accurate and the other inaccurate. Children with ASD trusted information from an accurate social robot over an inaccurate one, similar to their performance with human informants. However, compared to TD children, children with ASD exhibited lower levels of selective trust regardless of the type of informants they learned from. Our study suggests that children with ASD can selectively trust and acquire knowledge from social robots, shedding light on the potential use of social robots in supporting individuals with ASD.

Picker routing in scattered storage warehouses: an evaluation of solution methods based on TSP transformations

AbstractTo optimize their order fulfillment processes, many e-commerce warehouses employ a storage assignment strategy known as scattered or mixed-shelves storage. Under this approach, unit loads of homogeneous products are divided, and individual pieces are stored in various shelves throughout the warehouse. This arrangement ensures that products that appear together on unpredictable pick lists are stored in close proximity somewhere in the huge warehouses, reducing the travel distance for pickers. Despite these advancements, efficiently guiding pickers through the warehouse remains a significant planning challenge. Since the same products can be found in multiple storage positions, the traditional picker routing problem becomes more complex, as an additional selection task arises regarding which shelf to retrieve each requested product from. While previous research has developed several tailor-made solution algorithms, we demonstrate that known transformation schemes used for different variants of the well-known Traveling Salesman Problem (TSP) can be utilized to convert the single picker routing problem with scattered storage (SPRP-SS) into a classical TSP. This approach enables us to leverage the extensive array of state-of-the-art TSP solvers. The purpose of this paper is to explore the performance of these solvers when applied to solving the SPRP-SS. Through our computational study, we found that existing TSP solvers exhibit good performance, allowing near-optimal solutions to be obtained in less than a second for real-world scale SPRP-SS instances. Moreover, the efficiency of these TSP solvers remains unaffected by the number of cross aisles in the warehouse. Consequently, we exploit this flexibility to investigate the impact of cross aisles on picking performance in scattered storage warehouses.

Development and Validation of Phonological Processing Assessment Tool in Kannada Language

The research evidence in orthographically transparent language Kannada indicates that phonological processing significantly influences reading acquisition, and deficits increase the risk of dyslexia. A comprehensive assessment of phonological processing that includes phonological awareness, phonological memory, and phonological naming is crucial for implementing effective intervention strategies, thereby reducing the risk of dyslexia. However, there is a notable absence of phonological processing assessment tools specifically designed and validated for children learning to read alphasyllabary Kannada. The present study addresses this gap by developing and validating a phonological processing assessment tool for children between Grade I and Grade III learning to read alphasyllabary languages such as Kannada. The study was conducted in two distinct phases. The first phase consisted of developing and piloting a phonological processing assessment tool. It included the stages of task selection, item generation, content validation, pilot testing, and reliability analysis. In the second phase, the developed tool was validated by administering it to both typically developing children and children at-risk for dyslexia from Grade I through Grade III. Subsequently, the developmental appropriateness of the tool was tested by comparing the performance of typically developing children between the grades. Additionally, diagnostic validity, including sensitivity, specificity, and area under the curve, was established by comparing the performance of typically developing and at-risk children. The study makes a substantial contribution to research on reading in Akshara orthographies, offering a valuable clinical tool for identifying children at-risk for dyslexia.

Goal-directed attention transforms both working and long-term memory representations in the human parietal cortex.

The abundance of distractors in the world poses a major challenge to our brain's limited processing capacity, but little is known about how selective attention modulates stimulus representations in the brain to reduce interference and support durable target memory. Here, we collected functional magnetic resonance imaging (fMRI) data in a selective attention task in which target and distractor pictures of different visual categories were simultaneously presented. Participants were asked to selectively process the target according to the effective cue, either before the encoding period (i.e., perceptual attention) or the maintenance period (i.e., reflective attention). On the next day, participants were asked to perform a memory recognition task in the scanner in which the targets, distractors, and novel items were presented in a pseudorandom order. Behavioral results showed that perceptual attention was better at enhancing target memory and reducing distractor memory than reflective attention, although the overall memory capacity (memory for both target and distractor) was comparable. Using multiple-voxel pattern analysis of the neural data, we found more robust target representation and weaker distractor representation in working memory for perceptual attention than for reflective attention. Interestingly, perceptual attention partially shifted the regions involved in maintaining the target representation from the visual cortex to the parietal cortex. Furthermore, the targets and distractors simultaneously presented in the perceptual attention condition showed reduced pattern similarity in the parietal cortex during retrieval compared to items not presented together. This neural pattern repulsion positively correlated with individuals' recognition of both targets and distractors. These results emphasize the critical role of selective attention in transforming memory representations to reduce interference and improve long-term memory performance.

The role of cognitive biases involving selective interrogation of taste-based information in sugar-sweetened beverage consumption

Background and objectivesConsumption of sugar-sweetened beverages (SSBs) is a global public health issue. Consequently, there is strong interest in advancing understanding of the cognitive mechanisms that underpin excessive SSB consumption. This work proposes that selective interrogation of taste-based characteristics (e.g., flavour), rather than health-based characteristics (e.g., sugar content), of beverages is associated with greater SSB choice and consumption. MethodsTo evaluate this novel hypothesis, undergraduate students (n = 210) were recruited to complete self-report measures of SSB consumption, motivation and success in reducing SSB consumption and unhealthy eating more broadly, as well as a novel selective interrogation assessment task. This task provided participants with the opportunity to selectively interrogate taste-based and health-based characteristics of various mystery beverages. ResultsResults supported the hypothesis that greater selective interrogation of taste-based characteristics, rather than health-based characteristics, of beverages is associated with greater SSB choice and consumption. Additionally, results revealed that greater selective interrogation of taste-based characteristics was associated with decreased motivation to reduce SSB consumption and decreased motivation and success to reduce unhealthy consumption more broadly. LimitationsThe current study did not evaluate whether selective interrogation of taste-based characteristics of beverages predicted actual SSB consumption. ConclusionThese findings provide novel evidence for the potential role of selective interrogation of taste-based characteristics of beverages in SSB choice and consumption. An important implication of these findings is that in order for interventions focusing on front-of-package nutrition labels to be effective in reducing unhealthy consumption, it is necessary that individuals are selectively interrogating this ‘health-based’ information.

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.

The Effects of Reinforcing Task Alternations on Voluntary Task Selection

Everyday life often requires us to switch between states of cognitive flexibility and stability. However, little is known about what drives their regulation at the meta-control level. Based on current theories of cognitive control, we tested whether the regulation of cognitive flexibility and stability is guided by reinforcement learning. Using a task-switching paradigm with a double registration procedure – where people need to choose which task to perform before seeing the target, we systematically reinforced cued task switches or repetitions to test whether this led to more voluntary task switching (flexibility) or repeating (stability) on interspersed unrewarded free choice trials, respectively. While we did not find the hypothesized effect in an uninstructed version of the experiment (n=97), informing participants (n=58) on the reinforcement schedule on cued trials induced more switching or repeating on unrewarded free-choice trials. We speculate that people adapt their control strategies if they are aware of the respective benefits, even when they cease to be rewarded. Interestingly, choices were predominantly driven by task preference in both experiments and strongly correlated with task performance costs. This finding suggests that, when using an interspersed design with a double registration procedure, people mostly choose tasks based on task difficulty rather than switch avoidance. Together, these experiments help determine whether and when people can learn about the value of task selection strategies beyond the scope of a single task, and provide a self-regulating system to understand putative higher-order control processes.

Evaluating the effects of user motion and viewing mode on target selection in augmented reality

Target selection is a crucial task in augmented reality (AR). Recent evidence suggests that user motion can significantly influence target selection. However, no systematic research has been conducted on target selection within varied intensity user motions and AR settings. This paper was carried out to investigate the effects of four user motions (i.e., standing, walking, running, and jumping) and two viewing modes (i.e., viewpoint-dependent and viewpoint-independent) on user performance of target selection in AR. Two typical selection techniques (i.e., virtual hand and ray-casting) were utilized for short-range and long-range selection tasks, respectively. Our results indicate that the target selection performance decreased as the intensity of user motion increased, and users demonstrated better performance in the viewpoint-independent mode than in the viewpoint-dependent mode. We also observed that users took a longer amount of time to select targets when using the ray-casting technique than the virtual hand technique. We conclude with a set of design guidelines to improve the AR target selection performance of users while in motion.