Task Structure Research Articles

Neuroscience: Setting the neurobiological occasions for hierarchical learning and inference

Hierarchical associations help animals navigate the environment by using contextual and latent states toguide learning and behavior. Chemogenetic inactivation in an occasion setting task reveals that orbitofrontal cortex is essential for implementing a hierarchical task structure, yet both orbitofrontal cortex and dorsal hippocampus are required for new learning based on the established task structure.

Semantic graph neural network with multi-measure learning for semi-supervised classification

Graph Neural Networks (GNNs) have attracted increasing attention in recent years and have achieved excellent performance in semi-supervised node classification tasks. The success of most GNNs is attributed to the availability of the original graph structure. However, recent studies have shown that GNNs are vulnerable to the complex underlying structure of the graph, making it necessary to learn comprehensive and robust graph structures for downstream tasks, rather than relying only on the raw graph structure. In light of this, we seek to learn optimal graph structures for downstream tasks and propose a novel framework for semi-supervised classification. Specifically, based on the structural context information of graph and node representations, we encode the complex interactions in semantics and generate semantic graphs to preserve the global structure. Moreover, we develop a novel multi-measure attention layer to optimize the similarity rather than prescribing it a priori, so that the similarity can be adaptively evaluated by integrating measures. These graphs are fused and optimized together with GNN towards semi-supervised classification objective. Extensive experiments and ablation studies on six real-world datasets clearly demonstrate the effectiveness of our proposed model and the contribution of each component. The proposed model not only addresses the inherent vulnerabilities of GNNs to complex graph structures, but also introduces a pioneering approach to learning comprehensive and robust graph representations for semi-supervised classification tasks.

Forest 5.0: Internet of Things‐Based Transformation of Forests

ABSTRACTThe phrase “Modernization of forests” highlights the long‐term integration of modern technologies into forest management, aiming to enhance ecosystem observation, data collection, and analysis for exploration and improvement. Technologies like the Internet of Things (IoT), Wireless Sensor Networks (WSN), Artificial Intelligence (AI), and the IoT of Trees are key enablers of this shift. This research explores various technological interventions that could significantly benefit forest ecology, focusing on smart sensing, tracking, and analysis systems for detecting wildfires, illegal logging, and poaching. Simplified structures for tasks like vegetation analysis and forest fire predictions are proposed for future use without altering existing forest management systems. Additionally, the study addresses techniques to improve tribal livelihoods, promote small‐scale forest product marketing, and enhance wildlife monitoring. Key recommendations include establishing reliable wireless communication networks, continuous real‐time monitoring systems, and energy harvesting methods for effective digital forest management.

Using Natural Language to Improve Hierarchical Reinforcement Learning in Games

This work investigates how natural language task descriptions can accelerate reinforcement learning in games. Recognizing that human descriptions often imply a hierarchical task structure, we propose a method to extract this hierarchy and convert it into "options" – policies for solving subtasks. These options are generated by grounding natural language descriptions into environment states, which are then used as task boundaries to learn option policies either by leveraging prior successful traces or from human created walkthroughs. We evaluate our approach in both a simpler grid-world environment and the more complex text-based game Zork, comparing option-based agents against standard Q-learning and random agents. Our results demonstrate the effectiveness of incorporating natural language task knowledge for faster and more efficient reinforcement learning across different environments and Q-learning algorithms, including tabular Q-learning and Deep Q-Networks.

A Systematic Review of Motivational Factors in Developing Speaking Skills Among Indonesian EFL Learners

This systematic review aims to identify the key motivational factors that influence the development of speaking skills among Indonesian English as a Foreign Language (EFL) learners. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, five studies were selected for in-depth analysis. The methodology involved the identification, screening, and thematic analysis of research focusing on motivational factors affecting speaking proficiency. The results indicate that both internal factors—such as self-confidence, personal satisfaction, and integrative motivation—and external factors—including teacher support, classroom atmosphere, task structure, and instructional media—play crucial roles in enhancing learners’ motivation to participate in speaking activities. The novelty of this study lies in its focus on the Indonesian EFL context, offering a comprehensive overview of how these motivational factors intersect in this specific educational setting. The study contributes to the field by providing practical recommendations for educators, suggesting that a balanced focus on internal drives and supportive external environments is essential for improving speaking proficiency among Indonesian EFL learners. This review thus highlights the importance of addressing both personal and environmental factors to foster greater participation and confidence in speaking activities.

Distributed cognition in oral poetry improvisation: a semiosis-centered approach

Abstract We propose a semiotic externalist approach that takes cognition as semiosis, gives central importance to the notion of temporal distribution, describes the elements of distributed cognitive systems (DCSs) as signs, and identifies the DCS itself as a system that enacts a sign. This is a semiosis-centered, and thus a non-agent-centered account of DCSs. In order to develop and illustrate our argument, we describe an example of DCS – the Brazilian verbo-musical improvisational tradition of repente – considering it first as embodiment of the formal structure of a cognitive task, and then as embodiment of a semiotic process. The latter corresponds to a semiotization of the description of repente sessions as DCSs, that focuses on how the DCS can embody a meta semiotic process, semiosis that supervenes on, and determines, distinct types of smaller-scale semiotic process.

A cellular basis for mapping behavioural structure

To flexibly adapt to new situations, our brains must understand the regularities in the world, as well as those in our own patterns of behaviour. A wealth of findings is beginning to reveal the algorithms that we use to map the outside world1, 2, 3, 4, 5–6. However, the biological algorithms that map the complex structured behaviours that we compose to reach our goals remain unknown. Here we reveal a neuronal implementation of an algorithm for mapping abstract behavioural structure and transferring it to new scenarios. We trained mice on many tasks that shared a common structure (organizing a sequence of goals) but differed in the specific goal locations. The mice discovered the underlying task structure, enabling zero-shot inferences on the first trial of new tasks. The activity of most neurons in the medial frontal cortex tiled progress to goal, akin to how place cells map physical space. These ‘goal-progress cells’ generalized, stretching and compressing their tiling to accommodate different goal distances. By contrast, progress along the overall sequence of goals was not encoded explicitly. Instead, a subset of goal-progress cells was further tuned such that individual neurons fired with a fixed task lag from a particular behavioural step. Together, these cells acted as task-structured memory buffers, implementing an algorithm that instantaneously encoded the entire sequence of future behavioural steps, and whose dynamics automatically computed the appropriate action at each step. These dynamics mirrored the abstract task structure both on-task and during offline sleep. Our findings suggest that schemata of complex behavioural structures can be generated by sculpting progress-to-goal tuning into task-structured buffers of individual behavioural steps.

Persistent threat avoidance following negative reinforcement is not associated with elevated state anxiety.

Obsessive-Compulsive Disorder (OCD) is a debilitating illness consisting of obsessions and compulsions. OCD severity and treatment response are correlated with avoidant behaviors thought be performed to alleviate obsession-related anxiety. However, little is known about either the role of avoidance in the development of OCD or the interplay between anxiety states and avoidance behaviors. We have developed an instrumental negative reinforcement (i.e. active avoidance) paradigm in which mice must lever-press to avoid upcoming foot shocks. We show that mice (both sexes) can learn this task with high acquisition rates (75%) and that this behavior is largely stable when introducing uncertainty and modifying task structure. Furthermore, mice continue to perform avoidance responses on trials where lever pressing is not reinforced and increase response rates as they are maintained on this paradigm. With this paradigm, we did not find a relationship between negative reinforcement history and anxiety-related behaviors in well-established anxiety assays. Finally, we performed exploratory analyses to identify candidate regions involved in well-trained negative reinforcement using expression of the immediate early gene c-Fos. We detected correlated c-Fos expression in 1) cortico-striatal regions which regulate active avoidance in other paradigms and 2) amygdala circuits known to regulate conditioned defensive behaviors.Significance Statement Studies in patients with OCD suggest that compulsions are performed to avoid perceived threats and modulate anxiety tied to obsessions and/or compulsions. The negative reinforcement of avoidance and alleviated anxiety could therefore be a key driver of compulsive behaviors. However, there are still outstanding questions concerning the relationship between these two behaviors and the neural circuits involved in mediating negative reinforcement. We have developed an operant negative reinforcement paradigm in mice with discrete avoid and escape behaviors that can be learned without prior reward training with high throughput (75% acquisition) with responding that persists during nonreinforced trials. However, no differences were observed between negative reinforcement vs. unshocked and inescapably shocked controls, suggesting that continued negative reinforcement did not impact anxiety.

TIMSS INTERNATIONAL STUDY AND ITS IMPACT ON THE THINKING OF 4TH GRADE STUDENTS

The article examines changes in the state and psychological perception of fourth-grade students when performing TIMSS tasks, especially in the mathematical part. Attention is paid to the international assessment study TIMSS and its significance in modern society and leading world powers. The article describes the contribution of the leading countries in the study to the development of education and comprehensive personality development of primary school students. The development of schoolchildren affects the stability of education in the state and its international capabilities. Thearticle describes the educational environment in several major countries with a full description of its structure. The article also discusses the results of TIMSS and its impact on students 'creative thinking, the structure of TIMSS test tasks (blocks "Knowledge", "Applications", "Reasoning"). The articleis relevantforteachers, teachersandstudentsof primaryeducation.

Implementation of Disaster Preparedness in Hospitals by Emergency Department Nurses

Aims: The purpose of this study is to determine the preparedness of emergency room nurses in handling disasters in hospitals. Method: This study employs a qualitative research design, utilizing a single case study that focuses on the implementation of disaster preparedness by emergency room nurses in hospitals. The subjects of this case study are two nurses who meet the inclusion criteria: they have attended disaster preparedness training, work as emergency room nurses, have been part of the hospital disaster team for at least five years, and are considered reliable and competent sources of data related to the research objective. Purposive sampling was used to select the participants, and data was collected through in-depth interviews guided by an interview protocol. Results: Four themes were identified: disaster preparedness, the organizational structure of disaster management functions and tasks, patient grouping, and the communication of clear, effective information along with coordination among medical personnel. Conclusion: The study found that implementing preparedness among emergency room nurses in disaster management is crucial to ensure a prompt and effective response in emergency situations.

Network Bottlenecks and Task Structure Control the Evolution of Interpretable Learning Rules in a Foraging Agent.

Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural constraints on the learning network influence the optimal plasticity mechanisms remains obscure even for simple settings. To elucidate these dependencies, we study meta-learning via evolutionary optimization of simple reward-modulated plasticity rules in embodied agents solving a foraging task. We show that unconstrained meta-learning leads to the emergence of diverse plasticity rules. However, regularization and bottlenecks in the model help reduce this variability, resulting in interpretable rules. Our findings indicate that the meta-learning of plasticity rules is very sensitive to various parameters, with this sensitivity possibly reflected in the learning rules found in biological networks. When included in models, these dependencies can be used to discover potential objective functions and details of biological learning via comparisons with experimental observations.

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.

Effectiveness of Implementing the Government's Internal Control System in the District

The urgency of this research is based on the need to ensure that the internal control system runs well to increase accountability, transparency, and performance of local government. This research aims to analyze the effectiveness of the implementation of the government's internal control system in Cisarua District, Bogor Regency. This research uses a qualitative descriptive method with a case study approach. Data was collected through in-depth interviews, observation, and documentation. Data analysis was carried out using triangulation techniques to ensure the validity and reliability of the findings. The research results show that good relationships between leaders and members, clarity of task structure, and appropriate use of technology are key factors that support the effectiveness of internal control. However, research also identifies several inhibiting factors, such as limited human resources, lack of coordination between departments, and budget constraints. The research discussion emphasizes the importance of increasing the number and quality of human resources through continuous recruitment and training, as well as optimizing the use of information technology to automate control processes. In addition, improving coordination and communication between departments and seeking alternative funding sources were also identified as important steps to overcome these inhibiting factors.

An architectural analysis of DeepOnet and a general extension of the physics-informed DeepOnet model on solving nonlinear parametric partial differential equations

The Deep Neural Operator, as proposed by Lu et al. (2021), marks a considerable advancement in solving parametric partial differential equations. This paper examines the DeepOnet model’s neural network design, focusing on the effectiveness of its trunk-branch structure in operator learning tasks. Three key advantages of the trunk-branch structure are identified: the global learning strategy, the independent operation of the trunk and branch networks, and the consistent representation of solutions. These features are especially beneficial for operator learning. Building upon these findings, we have evolved the traditional DeepOnet into a more general form from a network perspective, allowing a nonlinear interfere of the branch net on the trunk net than the linear combination limited by the conventional DeepOnet. The operator model also incorporates physical information for enhanced integration. In a series of experiments tackling partial differential equations, the extended DeepOnet consistently outperforms than the traditional DeepOnet, particularly in complex problems. Notably, the extended DeepOnet model shows substantial advancements in operator learning with nonlinear parametric partial differential equations and exhibits a remarkable capacity for reducing physics loss.

Progress and opportunities of foundation models in bioinformatics.

Bioinformatics has undergone a paradigm shift in artificial intelligence (AI), particularly through foundation models (FMs), which address longstanding challenges in bioinformatics such as limited annotated data and data noise. These AI techniques have demonstrated remarkable efficacy across various downstream validation tasks, effectively representing diverse biological entities and heralding a new era in computational biology. The primary goal of this survey is to conduct a general investigation and summary of FMs in bioinformatics, tracing their evolutionary trajectory, current research landscape, and methodological frameworks. Our primary focus is on elucidating the application of FMs to specific biological problems, offering insights to guide the research community in choosing appropriate FMs for tasks like sequence analysis, structure prediction, and function annotation. Each section delves into the intricacies of the targeted challenges, contrasting the architectures and advancements of FMs with conventional methods and showcasing their utility across different biological domains. Further, this review scrutinizes the hurdles and constraints encountered by FMs in biology, including issues of data noise, model interpretability, and potential biases. This analysis provides a theoretical groundwork for understanding the circumstances under which certain FMs may exhibit suboptimal performance. Lastly, we outline prospective pathways and methodologies for the future development of FMs in biological research, facilitating ongoing innovation in the field. This comprehensive examination not only serves as an academic reference but also as a roadmap for forthcoming explorations and applications of FMs in biology.

From task-general towards task-specific cognitive operations in a few minutes? Working memory performance as an adaptive process.

Measurement of cognitive functions is typically based on the implicit assumption that the mental architecture underlying cognitive task performance is constant throughout the task. In contrast, skill learning theory implies that cognitively demanding task performance is an adaptive process that progresses from initial heavy engagement of effortful and task-general metacognitive and executive control processes towards more automatic and task-specific performance. However, this hypothesis is rarely applied to the short time spans of traditional cognitive tasks such as working memory (WM) tasks. We utilised longitudinal structural equation models on two well-powered data sets to test the hypothesis that the initial stages of WM task performances load heavily on a task-general g-factor and then start to diverge towards factors specific to task structure. In line with the hypothesis, data from the first experiment (N = 296) were successfully fitted in a model with task-initial unity of the WM paradigm-specific latent factors, after which their intercorrelations started to diverge. The second experiment (N = 201) replicated this pattern except for one paradigm-specific latent factor. These preliminary results suggest that the processes underlying WM task performance tend to progress rapidly from more task-general towards task-specific, in line with the cognitive skill learning framework. Such task-internal dynamics has important implications for the measurement of complex cognitive functions.

Flexible Work in the Public Sector: A Dual Perspective on Cognitive Benefits and Costs in Remote Work Environments

The COVID-19 pandemic has accelerated the adoption of flexible work arrangements within the Italian public administration. While much of the existing research has focused on the drawbacks of such arrangements, there has been less exploration of their benefits. Cognitive demands related to the structure of work activities, planning of working hours, planning of workplaces, and coordination with others, under flexible working conditions, might be considered as job resources that act as challenging demands within the Job Demand-Resource (JD-R) model. This study aimed to explore how the “cognitive challenge of flexible work” (CCFW) impact job satisfaction through home-based performance, taking into account the role of weekly working hours on home-based performance. Furthermore, the potential moderating role of cognitive and physical job demands between CCFW and home-based performance was explored. Using structural equation modeling on data from 484 public employees, the findings confirmed the positive impact of the structure of work tasks and planning of working times on both job satisfaction and home-based performance. In addition, cognitive demands (i.e., perception of cognitive work overload) played a moderating role in the mediated relationship between coordinating with others on job satisfaction and the structure of working tasks on job satisfaction through home-based performance.

A Multi-Task Convolutional Neural Network Relative Radiometric Calibration Based on Temporal Information

Due to the continuous degradation of onboard satellite instruments over time, satellite images undergo degradation, necessitating calibration for tasks reliant on satellite data. The previous relative radiometric calibration methods are mainly categorized into traditional methods and deep learning methods. The traditional methods involve complex computations for each calibration, while deep-learning-based approaches tend to oversimplify the calibration process, utilizing generic computer vision models without tailored structures for calibration tasks. In this paper, we address the unique challenges of calibration by introducing a novel approach: a multi-task convolutional neural network calibration model leveraging temporal information. This pioneering method is the first to integrate temporal dynamics into the architecture of neural network calibration models. Extensive experiments conducted on the FY3A/B/C VIRR datasets showcase the superior performance of our approach compared to the existing state-of-the-art traditional and deep learning methods. Furthermore, tests with various backbones confirm the broad applicability of our framework across different convolutional neural networks.

A line balancing problem with parallel workers and cycle time minimization

We study the problem of balancing assembly lines with parallel workers, motivated by features observed at a third-party logistics provider. The assembly line differs from the two known types of line balancing problems in the literature since it determines simultaneously the line cycle time and the number of workers per station. Furthermore, each station can be occupied by more than one worker and a restriction on the minimum number of stations is imposed. This new type of line balancing problem minimizes the line cycle time, where two types of decisions must be made: how to group the tasks into stations and how many workers to assign to each station. We adapt mathematical models based on assembly line balancing problems with parallel stations. Furthermore, we propose a new model based on network flow formulations for the special case of lines with a serial task structure, frequently observed at the company. We perform an extensive computational study with realistic instances in order to compare the speed of solving different formulations, which is important as such a problem is at the operational level. We also provide a sensitivity analysis of key parameters aiming to better understand the trade-offs and provide meaningful managerial insights.

Functional benefits of continuous vs. categorical listening strategies on the neural encoding and perception of noise-degraded speech

Acoustic information in speech changes continuously, yet listeners form discrete perceptual categories to ease the demands of perception. Being a more continuous/gradient as opposed to a more discrete/categorical listener may be further advantageous for understanding speech in noise by increasing perceptual flexibility and resolving ambiguity. The degree to which a listener’s responses to a continuum of speech sounds are categorical versus continuous can be quantified using visual analog scaling (VAS) during speech labeling tasks. Here, we recorded event-related brain potentials (ERPs) to vowels along an acoustic–phonetic continuum (/u/ to /a/) while listeners categorized phonemes in both clean and noise conditions. Behavior was assessed using standard two alternative forced choice (2AFC) and VAS paradigms to evaluate categorization under task structures that promote discrete vs. continuous hearing, respectively. Behaviorally, identification curves were steeper under 2AFC vs. VAS categorization but were relatively immune to noise, suggesting robust access to abstract, phonetic categories even under signal degradation. Behavioral slopes were correlated with listeners’ QuickSIN scores; shallower slopes corresponded with better speech in noise performance, suggesting a perceptual advantage to noise degraded speech comprehension conferred by a more gradient listening strategy. At the neural level, P2 amplitudes and latencies of the ERPs were modulated by task and noise; VAS responses were larger and showed greater noise-related latency delays than 2AFC responses. More gradient responders had smaller shifts in ERP latency with noise, suggesting their neural encoding of speech was more resilient to noise degradation. Interestingly, source-resolved ERPs showed that more gradient listening was also correlated with stronger neural responses in left superior temporal gyrus. Our results demonstrate that listening strategy modulates the categorical organization of speech and behavioral success, with more continuous/gradient listening being advantageous to sentential speech in noise perception.