Task Environment Research Articles

Catching Mind Wandering With Pupillometry: Conceptual and Methodological Challenges.

Mind-wandering (MW) refers to the shift of attention away from an ongoing task and/or external environment towards mental contents (e.g., memories, prospective thoughts) unrelated to the task. Physiological measures (e.g., pupil size, EEG, and fMRI) have often been acquired as objective markers for MW states, which has greatly helped their study as well as triangulation with other measures. Pupillometry in particular has been used as a covert biomarker of MW because it is reliably modulated by several distinct processes spanning arousal, emotion, and attention, and it signals attentional lapses. Yet, coupling MW and the measurement of pupil size has led to seemingly contrasting results. We argue that, common to the studies reviewed here, one reason is resolving to the measurement of tonic pupil size, which reflects low-frequency, slow changes in one's physiological state, and thus implicitly assumes that MW is a static, long-lasting process. We then additionally focus on three major axes of variability in the reviewed studies: (i) the definition and measurement of MW; (ii) the impact of contextual aspects, such as task demands and individual arousal levels; (iii) the identification and tracking of MW in combination with pupillary measures. We provide an overview of these differences and put forward recommendations for using physiological measures-including, but not limited to, pupil size-in MW research effectively. In conclusion, pupillometry can be a very informative tool for MW research, provided that it is used with the due methodological caution.

Design and Implementation of Unity-based Special Unmanned Vehicle Operating Simulator

Special unmanned vehicles can replace personnel to perform military tasks with high risk coefficients, such as long-distance reconnaissance, guided irradiation, and weapons strikes. In order to ensure that the special unmanned vehicle can quickly and accurately complete the established task action, the operator should carry out a large number of trainings in advance for the task content, if the training is directly used in the early stage of the training to manipulate the training, which has the security risks, it’s easy to increase the operator's psychological pressure, and the actual training site can't realistically restore the task scene, the training effect is also single. In view of the above problems, this paper designs a special unmanned vehicle control simulator, which enables the trainers to intuitively understand the task content and key points, and at the same time can control the unmanned vehicle in the virtual task environment to carry out the established task training and result review and analysis, so as to rapidly improve the control level of the trainers and ensure the successful completion of the established task.

Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation

Real-world work environments require operators to perform multiple tasks with continual support from an automated system. Eye movement is often used as a surrogate measure of operator attention, yet conventional summary measures such as percent dwell time do not capture dynamic transitions of attention in complex visual workspace. This study analyzed eye movement data collected in a controlled a MATB-II task environment using gaze transition entropy analysis. In the study, human subjects performed a compensatory tracking task, a system monitoring task, and a communication task concurrently. The results indicate that both gaze transition entropy and stationary gaze entropy, measures of randomness in eye movements, decrease when the compensatory tracking task required more continuous monitoring. The findings imply that gaze transition entropy reflects attention allocation of operators performing dynamic operational tasks consistently.

Enhancing flow states in neurodivergent individuals through cognitive network integration

This article explores the concept of “flow” in the context of neurodivergence, focusing on the interaction between the default mode network (DMN) and task-positive networks (TPNs), particularly in conditions such as autism spectrum condition (ASC) and attention-deficit/hyperactivity disorder (ADHD). Flow, a state of deep immersion and focused engagement, is associated with enhanced performance and satisfaction across activities. The DMN, typically active during rest and self-referential thinking, interacts uniquely with TPNs during flow states, creating a dynamic balance crucial for sustained focus and reduced self-referential thinking. Neurodivergent individuals often exhibit distinct DMN activity patterns, which affect their capacity for achieving flow. For instance, individuals with ADHD may experience flow in stimulating tasks, whereas those with ASC may achieve it in areas of deep interest due to their intense focus. This paper examines the expertise-and-release model in creative flow and transient hypofrontality as a potential mechanism facilitating flow in neurodivergent individuals. It also proposes strategies to enhance flow through personalized cognitive training, specialized task environments, and technology-assisted interventions, aiming to harness neurodivergent strengths and accommodate unique cognitive profiles. This comprehensive analysis deepens understanding of neurodiversity and offers practical applications for improving quality of life and performance in neurodivergent populations.

Research of Converter Station Intelligent Inspection System Algorithm

Abstract This article mainly reveals a converter station equipment defect detection system based on the YOLOv5 algorithm. The research encompasses the establishment of a simulated environment for converter station inspection tasks, problem formulation, algorithm design, training process, and experimental evaluation. Key contributions include the enhancement of the YOLOv5 algorithm through improved clustering techniques, dataset preparation, and multi-object detection model training. This highlights the importance of appropriate anchor box initialization in object detection tasks, especially when dealing with small-sized images and varied defect types. In order to verify the correctness of this research, a comparative simulation analysis with mainstream detection algorithms further validates the superiority of the proposed method. The findings of this research contribute valuable insights into the development of AI-driven defect detection systems for power equipment inspection.

Technological Development of Hybrid Aquatic-Aerial Vehicle based on Bionics: Research Progress and Trends

In both military and civilian domains, the spacecraft is commonly employed to accomplish various tasks. With the ongoing advancements in mil-itary strategy and civilian technologies, task environments have become increasingly complex. Consequently, there’s a growing trend across all sectors towards integrating water and air operations to enhance task completion. With the continuous progress of biology and mechanics, the development direction of hybrid aquatic-aerial vehicle has emerged in the direction of bionic vehicle. The purpose of this paper is to analyze the current research status and development trend of different hybrid aquatic-aerial vehicles based on bionics. Firstly, the early research and application of hybrid aquatic-aerial vehicles are reviewed by time tracing method. Then, the implications of different biological models for the development of hybrid aquatic-aerial vehicles are analyzed and the research status of different vehicles is discussed. Additionally, the paper analyzes the advantages and disadvantages of hybrid aquatic-aerial vehicles and other vehicle types. It also proposes solutions, primarily focusing on methods for integrating different vehicle types. The current research status, advantages, and disadvantages of various hybrid aquatic-aerial vehicles are analyzed in this paper. It also suggests feasible schemes for hybrid aquatic-aerial vehicles, serving as a valuable research reference in certain areas.

Effect of Acute Alcohol Consumption in a Novel Rodent Model of Decision Making.

Alcohol use, especially at high consumption levels, can lead to irrational decision-making. In humans, this can lead to harmful outcomes often seen in the context of driving under the influence and or aggressive behavior. To date, the field is lacking comprehensive animal models to examine the impact of alcohol use on decision making in rodents, particularly to examine sex differences in choice behavior. To address this issue, the present study examined the effects of acute alcohol consumption during a behavioral approach-avoidance task that captures momentary changes in decision-making behavior and choice selection in female and male rats. Our team has developed a novel behavioral protocol involving a concurrent choice to consume four different concentrations of alcohol and sucrose combinations. During the task, female or male rats can approach or avoid drinking solutions in four distinct corners of our test apparatus. The solutions were prepared in inverse concentrations (higher sucrose was paired with lower alcohol and vice versa) so that the rodents pursue minimal alcohol use by consuming the higher sucrose concentrations or higher concentrations of alcohol by drinking the lower sucrose concentrations. The animals also have the option to avoid drinking alcohol by not approaching any of the drinking cups. Behavior and choice were tracked during task performance involving different solution concentrations of alcohol and sucrose. The choice of consuming different concentrations of alcohol or sucrose resulted in sex-dependent differences in an approach-avoid trade-off pattern of behavior that was sensitive to different concentrations of alcohol/sucrose combinations. Notably, males were greatly affected by the introduction of alcohol into the task environment, approaching higher alcohol concentrations significantly more often than the non-alcohol containing options. In contrast, females choice patterns and task performance were largely unchanged during alcohol and non-alcohol containing tasks. Regardless of sex, we identify a novel method for identifying individual subject decision-making abnormalities during and after alcohol consumption. This research reveals a novel approach for examining the effects of acute alcohol exposure during a trade-off task, with decision patterns being more impacted by alcohol use in males as compared to females. We also offer the field a novel approach for identifying individual abnormalities in decision making behavior with the presentation of alcohol. Future research can explore these abnormal patterns in both acute and chronic alcohol conditions to develop methods for identifying subjects at-risk for developing an alcohol use disorder and the deleterious impact of alcohol on rational decision making.

Towards a taxonomy of team workflow structures

AbstractTeam workflow represents interactions between individuals and specific actions or tasks. Individuals’ interactions have important effects on fellow teammates’ actions by expanding or constraining actions available to them. For example, teammates may avoid performing the same action to avoid duplication of effort or they may perform their actions sequentially if one task’s completion is a prerequisite for another task. Complex dependencies embedded in these interactions suggest the need to understand team workflows from a relational perspective. As workflow structures are shaped by elements of organizational design, cognitive factors, and features of the task environment, no single workflow structure is optimal for all teams, and team workflows may manifest in countless distinct configurations. Through a systematic, network-based representation of team workflows, this paper uses a sample of 139,500 teams on GitHub to identify common patterns of team workflows. Each team is represented as a two-mode network where individuals form ties to up to fifteen distinct actions capturing productivity, discussion, and team management. Several node-level and graph-level centrality indices highlight patterns of differentiation across team workflows, and a k-means clustering algorithm detects three distinct clusters of team workflow structures: small teams of highly active generalists, small teams with a moderately active mix of focused and generalist members, and large, segmented teams of focused individuals collectively engaging in a few extremely popular actions. These results demonstrate how a structural representation of team workflows provides unique insight into team behavior and highlights distinctions that may otherwise be lost when examining team activity in aggregate.

Adaptive decision-making with deep Q-network for heterogeneous unmanned aerial vehicle swarms in dynamic environments

With the continuous advancement of Unmanned Aerial Vehicle (UAV) swarm technology, the field of adaptive decision-making for heterogeneous UAV swarms across a spectrum of tasks is rapidly emerging as a focal point of research. This domain holds significant importance for enhancing the operational efficiency and responsiveness of UAV swarms in complex and dynamic environments. This paper addresses this challenge by introducing an innovative adaptive task decision-making method, which leverages Deep Q-Network (DQN) algorithms, specifically tailored to meet the unique requirements of heterogeneous UAV swarms. We initially construct a mathematical model that transforms the intricate swarm decision-making issues into a standard Markov Decision Process (MDP), and meticulously design the state space, action space, reward function, and state transition function to cater to the decision-making needs of UAV swarms. Utilizing this model, we further integrate a hybrid experience replay mechanism, coupled with DQN algorithms for offline training, enabling the efficient transformation of training outcomes into a network model that is directly applicable to real-time online decision-making. This approach markedly enhances the decision-making adaptability and dynamic adaptability of UAV swarms when confronted with a variety of tasks. During the offline training phase, we also employ a hybrid experience replay mechanism and target network approximation techniques to bolster the network's stability and training efficiency, ensuring high reliability in online decision-making. Comparative simulations with existing DQN algorithms reveal the distinct superiority of our method in addressing adaptive task decision-making challenges. Moreover, through 1000 comprehensive simulation experiments, we further validate the stability and effectiveness of our method across diverse task environments, providing a solid theoretical foundation and technical support for the practical application and proliferation of UAV swarm technology.

How does market competition affect enterprise cooperative innovation? The moderating role of intellectual property protection and government subsidies

Cooperative innovation is an effective strategy for enterprises to obtain innovation resources and reduce innovation costs. However, existing studies have paid less attention to why and when enterprises choose cooperative innovation strategies. Focusing on market competition as a salient feature of the task environment, this study explores the mechanisms through which it affects cooperative innovation. Using a sample of leading manufacturing enterprises in China between 2010 and 2018, we adopt a negative binomial model to test our hypotheses. The results show that market competition has a U-shaped relationship with cooperative innovation breadth and cooperative innovation depth. And the relationship between market competition and cooperative innovation is moderated by the institutional environment factors. Specifically, the main effect is negatively moderated by intellectual property protection. And government subsidies only have significant moderating effects on the relationship between market competition and cooperative innovation breadth, but no significant moderating effects on the relationship between market competition and cooperative innovation depth. Besides, revealing the aforementioned relationship, this study provides theoretical and practical implications on how to strike a balance between innovation resources acquisition from the external partners and the associated risks of cooperation.

Hippocampal Cognitive Function Based on Deep Learning

Abstract This research focuses on the study of agent behavior decision-making based on hippocampal cognitive functions, aiming to enhance the decision-making capabilities of agents in complex task environments by deeply exploring the crucial role of the hippocampus in learning, memory, and cognitive processes. By drawing inspiration from the biological structure and functional characteristics of the hippocampus, researchers are dedicated to designing and developing more intelligent and adaptive decision-making models to enhance agents' behavioral performance, problem-solving abilities, and adaptability to new situations. To achieve this goal, the research integrates advanced artificial intelligence technologies such as reinforcement learning and deep learning to simulate the complex functions of the hippocampus in memory encoding, storage, retrieval, and cognitive reasoning. This research not only contributes to advancing intelligent systems towards higher levels of intelligence and personalization but also plays a significant role in improving the interaction between intelligent agents and humans, providing intelligent services that better meet user needs. We found that the neural network trained in multi-task learning benefits from a loss term that promotes relevant and irrelevant representations. Therefore, the complementary coding we found in CA3 can provide extensive computational advantages for solving complex tasks. Furthermore, the study emphasizes the importance of further elucidating the functional mechanisms of the hippocampus, with the expectation of providing a more solid theoretical foundation for the optimization and refinement of agent decision-making models in the future.

Quantifying the Transition from Unconscious to Conscious Detection of Temporal Patterns in Vigilance Tasks: A Unique Adaptation of Mackworth's Clock Test.

This study investigates the cognitive mechanisms underlying vigilance and pattern recognition using a novel adaptation of Mackworth's Clock Test. We aimed to quantify the time it takes for temporal patterns detected unconsciously through implicit learning to surface in the conscious mind within a dynamic vigilance task environment. Forty-eight participants detected random and non-disclosed rhythmic anomalous clock hand movements in this setting. Our results indicate significant variability in detection accuracy, reaction times, and the ability to recognize the hidden pattern among participants. Notably, 23% of all participants and 56% of those who consciously reported the pattern exhibited statistically lower reaction times indicative of knowledge of the pattern 40 s before conscious identification. These findings provide valuable insights into the transition from unconscious to conscious detection, highlighting the complexity of sustained attention and pattern recognition. The study's implications extend to designing training programs and tasks for high-stakes professions requiring prolonged vigilance. Future research should further explore the cognitive and neural correlates of these processes and the impact of task complexity on performance.

Gambling habits and Probability Judgements in a Bayesian Task Environment.

Little is known about how gamblers form probability assessments. This paper reports on a preregistered study that administered an incentivized Bayesian choice task to n = 465 self-reported gamblers and non-gamblers. The task elicits subjective probability assessments and allows one to estimate the degree to which distinct information sources are weighted in forming probability assessments. Our data failed to support our main hypotheses that experienced online gamblers would be more accurate than non-gamblers in estimating probabilities, that gamblers experienced in games of skill (e.g., poker) would be more accurate than gamblers experienced only in non-skill games (e.g., slots), that accuracy would differ by sex, or that information sources would be weighted differently across different participant groups. Exploratory analysis, however, revealed that gambling frequency predicted lower Bayesian accuracy, while cognitive reflection predicted higher accuracy. The decline in accuracy linked to self-reported gambling frequency was stronger for female participants. Decision modeling estimated a decreased weight place on new evidence (over base rate odds) for those participant groups who showed decreased accuracy, which suggests that a proper incorporation of new information is important for probability assessments. Our results link online gambling frequency to worse performance in the critical probability assessment skills that should benefit gambling success (i.e., in skill-based games). Additional research is needed to better understand the mechanism linking reported gambling frequency to probability assessment accuracy.

Immersive Robot Teleoperation Based on User Gestures in Mixed Reality Space.

Recently, research has been conducted on mixed reality (MR), which provides immersive visualization and interaction experiences, and on mapping human motions directly onto a robot in a mixed reality (MR) space to achieve a high level of immersion. However, even though the robot is mapped onto the MR space, their surrounding environment is often not mapped sufficiently; this makes it difficult to comfortably perform tasks that require precise manipulation of the objects that are difficult to see from the human perspective. Therefore, we propose a system that allows users to operate a robot in real space by mapping the task environment around the robot on the MR space and performing operations within the MR space.

Personal control and cognitive flexibility: Does uncontrollability reduce flexible shifting of attention?

Acting and learning in unpredictable and volatile environments requires cognitive flexibility. However, the ability to flexibly adapt to the changing task environment can be reduced when people experience uncontrollability due to exposure to action – outcome non-contingency. In this research, we propose that flexible shifting of attention is affected by experiences of uncontrollability. In two studies, a lack of control was experimentally induced and subsequently cognitive flexibility was assessed either with a Wisconsin Card Sorting Task (Study 1) or a cognitive set-shifting paradigm (Study 2). Results showed a reduced tendency to flexibly shift attention and to overcome dominant response tendencies amongst participants who experienced uncontrollability. Modulation of the stability-flexibility balance by situationally induced experiences of uncontrollability is discussed.

Task-Motion Planning System for Socially Viable Service Robots Based on Object Manipulation.

This paper presents a software architecture to implement a task-motion planning system that can improve human-robot interactions by including social behavior when social robots provide services related to object manipulation to users. The proposed system incorporates four main modules: knowledge reasoning, perception, task planning, and motion planning for autonomous service. This system adds constraints to the robot motions based on the recognition of the object affordance from the perception module and environment states from the knowledge reasoning module. Thus, the system performs task planning by adjusting the goal of the task to be performed, and motion planning based on the functional aspects of the object, enabling the robot to execute actions consistent with social behavior to respond to the user's intent and the task environment. The system is verified through simulated experiments consisting of several object manipulation services such as handover and delivery. The results show that, by using the proposed system, the robot can provide different services depending on the situation, even if it performs the same tasks. In addition, the system demonstrates a modular structure that enables the expansion of the available services by defining additional actions and diverse planning modules.

Animated VR and 360-degree VR to assess and train team sports decision-making: a scoping review.

In team sports, athletes' ability to make quick decisions plays a crucial role. Decision-making proficiency relies on the intricate balance of athletes' perceptual and cognitive abilities, enabling them to assess the competitive environment swiftly and select the most appropriate actions from various options. Virtual reality (VR) technology is emerging as a valuable tool for evaluating and refining athletes' decision-making skills. This study systematically examined the integration of VR technology into decision-making processes in team sports, aiming to identify more effective methods for presenting and interacting with virtual decision-making systems, thus enhancing the evaluation and refinement of athletes' decision making abilities. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a thorough search of respected research databases, including Web of Science, PubMed, SPORTDiscus, ScienceDirect, PsycINFO, and IEEE, was conducted using carefully selected keywords. Twenty research papers meeting predefined inclusion criteria were included after careful evaluation. These papers were systematically analyzed to delineate the attributes of virtual decision-making task environments, the interactive dynamics inherent in motor decision-making tasks, and the significant findings. This review indicate that (1) the effectiveness of VR technology in assessing and improving athletes' decision-making skills in team sports; (2) the construction of virtual environments using the Head-Mounted Display (HMD) system, characterized by enhanced ease and efficiency; (3) the potential for future investigations to explore computer simulations to create more expansive virtual motion scenarios, thus efficiently generating substantial task scenario material, diverging from the constraints posed by 360-degree panoramic videos; and (4) the integration of motion capture technology for identifying and monitoring athletes' decision-making behaviors, which not only enhances ecological validity but also augments the transfer validity of virtual sports decision-making systems. Future research endeavors could explore integrating eye-tracking technology with virtual reality to gain insights into the intrinsic cognitive-action associations exhibited by athletes.

Toddlers strategically adapt their information search

Adaptive information seeking is essential for humans to effectively navigate complex and dynamic environments. Here, we developed a gaze-contingent eye-tracking paradigm to examine the early emergence of adaptive information-seeking. Toddlers (N = 60, 18-36 months) and adults (N = 42) either learnt that an animal was equally likely to be found in any of four available locations, or that it was most likely to be found in one particular location. Afterwards, they were given control of a torchlight, which they could move with their eyes to explore the otherwise pitch-black task environment. Eye-movement data and Markov models show that, from 24 months of age, toddlers become more exploratory than adults, and start adapting their exploratory strategies to the information structure of the task. These results show that toddlers’ search strategies are more sophisticated than previously thought, and identify the unique features that distinguish their information search from adults’.

Perirhinal cortex learns a predictive map of the task environment

Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chronic two-photon calcium imaging, population analysis, and computational modeling revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that can progressively be decoded earlier in the trial as training advances and that generalize as animals learn new contingencies. Stimulus-outcome associations are linked to prospective network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and systemic pharmacological perturbation. We propose that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.

RAZMERJE MED ZMOGLJIVOSTJO V MOČI IN GIBALNO ZMOGLJIVOSTJO V STOJI NA ROKAH: MODEL STROJNEGA UČENJA

The present study investigated the relationship between strength capacity and motor performance in the gymnastic handstand. The hypothesis stipulated a positive relationship between motor performance and strength capacity levels. Thirty-two university students, 16 female and 16 male (24.03 ± 4.74 years of age,) participated in the study. The handstand was assessed using the absolute error of the three angles produced by the model (video) and the three angles produced by the performer. We conducted four strength tests: explosive force, maximum right-hand grip strength, maximum left-hand grip strength, and resistance force. The machine learning model was trained using 10 of the folds and cross-validated, and a linear regression test was performed using motor performance (absolute error) and strength tests (explosive force, maximum force right-hand, maximum force left-hand, and resistance force). The results showed that the machine learning model indicated a low relationship between strength capacity and motor performance. Additionally, motor performance was not found to be related to strength capacity. The results may indicate that specific capacities and the interaction of factors such as task specificity, environment, and individual characteristics influence motor performance.