Task Domain Research Articles

Integrative work design for telework practices: lessons learned from the COVID-19 pandemic in Thailand

Purpose This study aims to identify an effective work design for telework practices in Thailand by investigating the influence on employee work engagement and job stress of job demands and resources in three domains of work characteristics – task, social and contextual. Design/methodology/approach In total, 1,052 high-intensity teleworkers participated in our online survey. Nested model comparisons and chi-square difference tests were used to test the significance of the three domains of work characteristics by comparing changes in model fit associated with the removal of the parameters associated with each domain. The best fit model was then used to examine the hypothesized relationships. Findings The results revealed that each domain of work characteristics provides additional and meaningful insights on employee outcomes. For telework practices in Thailand, supervisor support and work autonomy, the job resources specified respectively in the social and task domain can enhance work engagement. In contrast, supervisor surveillance and communication overload, the job demands in these respective domains can lead to job stress. Additionally, telework contextual demands of blurred work–life boundaries reduce employee work engagement. Communication overload has paradoxical outcomes of increased job stress and improved work engagement. Originality/value This study contributes to the work design and telework literature by applying an integrative work–design approach to demonstrate that organizations should consider both job demands and resources in a wider context of work design. This study also provides insights in respect of Thai cultural values to explain the effective design of telework practices in Thailand, a country where telework is relatively new and the work–design literature is very limited. This study is useful for international business managers wishing to adopt telework practices in Thailand to localize how telework is organized and ensure a smooth transition to the new world of work more successfully in the post-pandemic period.

Converging evidence for domain-general developmental trends of mental attentional capacity: Validity and reliability of full and abbreviated measures

Our ability to understand the world around us hinges on our cognition. Theoretically, children’s abilities improve with age; however, a lively discussion exists on how factors such as task domain, task interference, and task difficulty, as indexed mainly by relevant cues, affect cognitive performance. Practically, cognitive measures take a substantial amount of time to administer, which poses limitations for researchers in the field of psychology. The current study addressed theoretical and practical questions regarding the nature of child cognition using full and abbreviated versions of classic, recent, and new tasks of mental attentional capacity. We employed a cross-sectional design testing 483 participants in six groups (7–30 years of age) on the new Number Matching Task (NMT), the established Color Matching Task (CMT), and the classic Figural Intersection Task (FIT). Results confirm theoretical predictions of the developmental increase in mental attentional capacity and the adjunct hypothesis that tasks with high interference are better to assess the developmental trajectory of mental attentional capacity quantitatively. NMT scores are significantly equivalent to CMT scores and the theoretically predicted mental attentional capacity, and the abbreviated CMT and NMT produce comparable scores to those of the full tests. We determined that the NMT can be administered developmentally and is appropriate for use in assessing mental attentional capacity in studies with both children and adults.

Task-specific effects of biological sex and sex hormones on object recognition memories in a 6-hydroxydopamine-lesion model of Parkinson's disease in adult male and female rats

Many patients with Parkinson's disease (PD) experience cognitive or memory impairments with few therapeutic options available to mitigate them. This has fueled interest in determining how factors including sex and sex hormones modulate higher order function in this disease. The objective of this study was to use the Novel Object Recognition (NOR) and Object-in-Place (OiP) paradigms to compare the effects of a bilateral neostriatal 6-hydroxydopamine (6-OHDA) lesion model of PD in gonadally intact male and female rats, in orchidectomized male rats and in orchidectomized males supplemented with 17β-estradiol or testosterone propionate on measures of recognition memory similar to those at risk in PD. These studies showed that 6-ODHA lesions impaired discrimination in both tasks in males but not females. Further, 6-OHDA lesions disrupted NOR performance similarly in all males regardless of whether they were gonadally intact, orchidectomized or hormone-supplemented. In contrast, OiP performance was disrupted in males that were orchidectomized or 6-OHDA-lesioned but was spared in orchidectomized and orchidectomized, 6-OHDA lesioned males supplemented with 17β-estradiol. The distinct effects that sex and/or sex hormones have on 6-OHDA lesion-induced NOR vs. OiP deficits identified here also differ from corresponding impacts recently described for 6-OHDA lesion-induced deficits in spatial working memory and episodic memory. Together, the collective data provide strong evidence for effects of sex and sex hormones on cognition and memory in PD as being behavioral task and behavioral domain specific. This specificity could explain why a cohesive clinical picture of endocrine impacts on higher order function in PD has remained elusive.

The Role of Intuitive Cognition in Radiologic Decision Making.

The decision making involved in radiologic interpretation entails distinct cognitive pathways. On one side is analytic reasoning, which represents a deliberate, stepwise process integrating discrete data to formulate an interpretation, in which a range of diagnostic possibilities are directly compared. On the other side is intuition, which represents an automatic, rapid, and holistic form of decision making that generates an interpretation absent the sequential processing of data and direct comparison of possibilities. Nonexpert intuitive cognition often reflects domain-independent heuristics (ie, mental rules of thumb) that are often effective but prone to bias and systematic error. In contrast, expert intuition reflects the domain-specific skills developed among highly experienced practitioners who have gained deep knowledge in a given task domain from extensive practice and feedback. In this article, the authors define intuitive cognition, show evidence for its pervasive use among experts in a variety of fields, and explain its strengths and weaknesses relative to deliberate reasoning. Developing expert intuition requires the opportunity to learn from reliable feedback, and the authors describe various measures that can be used by radiology departments to foster such opportunities. Finally, the authors discuss implications for diagnostic performance and error reduction in clinical radiology.

Posterior temperature optimized Bayesian models for inverse problems in medical imaging.

We present Posterior Temperature Optimized Bayesian Inverse Models (POTOBIM), an unsupervised Bayesian approach to inverse problems in medical imaging using mean-field variational inference with a fully tempered posterior. Bayesian methods exhibit useful properties for approaching inverse tasks, such as tomographic reconstruction or image denoising. A suitable prior distribution introduces regularization, which is needed to solve the ill-posed problem and reduces overfitting the data. In practice, however, this often results in a suboptimal posterior temperature, and the full potential of the Bayesian approach is not being exploited. In POTOBIM, we optimize both the parameters of the prior distribution and the posterior temperature with respect to reconstruction accuracy using Bayesian optimization with Gaussian process regression. Our method is extensively evaluated on four different inverse tasks on a variety of modalities with images from public data sets and we demonstrate that an optimized posterior temperature outperforms both non-Bayesian and Bayesian approaches without temperature optimization. The use of an optimized prior distribution and posterior temperature leads to improved accuracy and uncertainty estimation and we show that it is sufficient to find these hyperparameters per task domain. Well-tempered posteriors yield calibrated uncertainty, which increases the reliability in the predictions. Our source code is publicly available at github.com/Cardio-AI/mfvi-dip-mia.

Structural Brain Imaging Predicts Individual-Level Task Activation Maps Using Deep Learning.

Accurate individual functional mapping of task activations is a potential tool for biomarker discovery and is critically important for clinical care. While structural imaging does not directly map task activation, we hypothesized that structural imaging contains information that can accurately predict variations in task activation between individuals. To this end, we trained a convolutional neural network to use structural imaging (T1-weighted, T2-weighted, and diffusion tensor imaging) to predict 47 different functional MRI task activation volumes across seven task domains. The U-Net model was trained on 591 subjects and then subsequently tested on 122 unrelated subjects. The predicted activation maps correlated more strongly with their actual maps than with the maps of the other test subjects. An ablation study revealed that a model using the shape of the cortex alone or the shape of the subcortical matter alone was sufficient to predict individual-level differences in task activation maps, but a model using the shape of the whole brain resulted in markedly decreased performance. The ablation study also showed that the additional information provided by the T2-weighted and diffusion tensor imaging strengthened the predictions as compared to using the T1-weighted imaging alone. These results indicate that structural imaging contains information that is predictive of inter-subject variability in task activation mapping and that cortical folding patterns, as well as microstructural features, could be a key component to linking brain structure to brain function.

Effects of Dominance in Exposure and Linguistic Distance in the Bilingual Proficiency Profiles of Dutch-German and Greek-German School-Age Bilinguals

Bilingual development is characterized by systematic variability, so called profile effects. This paper investigates dominance in exposure and linguistic distance as potential sources of bilingual profile effects by comparing the lexical and narrative profiles of Greek-German bilinguals to those of Dutch-German bilinguals. Both bilingual groups, aged 10–11 years, were recruited from bilingual school-contexts in Germany. The Greek-German bilinguals (N=15) constitute a classic heritage language population with a fairly distantly related language combination, the Dutch-German bilinguals (N=15) constitute an under-researched group of bilingual speakers with a very closely related language combination. Participants were asked about their language exposure and underwent a productive vocabulary task and a narrative production task in both languages. Findings indicate that dominance in exposure did not directly translate into dominance in lexical proficiency, which might be related to a cognate facilitation effect in the lexical tasks for the Dutch-German bilinguals. The narrative analysis indicates that, on group-level, dominance in exposure translated into dominance in narrative performance. A correlational analysis showed that dominance affected narrative measures differently and correlated differently with narrative measures depending on the group and, thus, language combination. However, it could not be affirmed that the linguistic proximity of Dutch and German balanced out dominance effects in general, since dominance in that group of bilinguals was too stark. It is concluded from this that dominance in exposure and linguistic distance interact in bilingual development, but that this relationship is also dependent on the magnitude of dominance and the type of task and linguistic domain under investigation.

Deep domain adaptation via joint transfer networks

Domain adaptation aims to transfer the enrich label knowledge from a large-scale labeled tasks to new ones with no labeled data. In the real-world scenario, the domain discrepancy of feature distributions between different tasks (domains) is usually uncontrollable, which is dramatically motivated to match the feature distributions in the face of the domain discrepancy is completely divergence. Under the condition that target task (domain) annotations are unknown, how to successfully adapt the trained classified from source to the target of interest still remains an open issue. In this paper, a unified domain adaptation method is proposed, Joint Transfer Networks (JTN), which jointly generates domain transferable features across two different domains and a double weighting scheme is able to learn more meaningful and transferable features. Additionally, we also exploit the pseudo-labeled target data to generate discriminative features in the target domain, which further boosts the adaptation performance. Finally, after a thorough evaluation of proposed method utilizing several benchmark datasets of varying difficulty. JTN yielded the state-of-the-art performance and outperformed baseline approaches for various domain adaptation tasks.

Meta captioning: A meta learning based remote sensing image captioning framework

Remote sensing image captioning models require large amounts of caption-labeled training data. Though image classification models are normally trained with sufficient training data, they cannot be straightforwardly applied to remote sensing image captioning, because the labels for classification and captioning arise from different task domains. Additionally, remote sensing images with caption labels are not as sufficient as images with class labels. Such limitations render difficulty to effective remote sensing image captioning. To address these limitations, we develop a meta captioning framework that conducts remote sensing image captioning with meta learning. The meta captioning framework extracts meta features from two support tasks, i.e., natural image classification and remote sensing image classification, and transfers the meta features to one target task, i.e., remote sensing image captioning. The two support tasks train classification models with big amounts of class-labeled data such that they extract meta features that comprehensively represent image visual features from the perspective of classification. The target task, equipped by the meta features, just requires a relatively small amount of caption-labeled training data for achieving effective remote sensing image captioning. Experimental evaluations on three public datasets validate that the meta captioning framework achieves state-of-the-art performance on remote sensing image captioning. We release the code for our work at: https://github.com/QiaoqiaoYang/MetaCaptioning.

Metacognitive accuracy across cognitive and physical task domains.

Metacognition is often considered a critical component of learning and higher-order cognition; however, does metacognitive accuracy remain constant across all tasks, specifically in tasks that involve physical or procedural components? To investigate the consistency of metacognitive judgments across various task types, participants completed word and number recall tasks, and also completed three simple physical skill tasks (e.g., catching a ball in a cup). Participants made metacognitive judgments about their performance in all tasks. Results indicated that while participants demonstrated traditional levels of relative metacognitive accuracy in more cognitive tasks, participants were significantly more accurate in their judgments for physical skill tasks. In other words, relative accuracy for metacognitive judgments in physical tasks appears to be significantly higher than for cognitive tasks. This represents the first such demonstration of this effect and suggests that characteristics of physical tasks somehow improve participants' judgments of how well they have learned.

Replicable patterns of causal information flow between hippocampus and prefrontal cortex during spatial navigation and spatial-verbal memory formation.

Interactions between the hippocampus and prefrontal cortex (PFC) play an essential role in both human spatial navigation and episodic memory, but the underlying causal flow of information between these regions across task domains is poorly understood. Here we use intracranial EEG recordings and spectrally resolved phase transfer entropy to investigate information flow during two different virtual spatial navigation and memory encoding/recall tasks and examine replicability of information flow patterns across spatial and verbal memory domains. Information theoretic analysis revealed a higher causal information flow from hippocampus to lateral PFC than in the reverse direction. Crucially, an asymmetric pattern of information flow was observed during memory encoding and recall periods of both spatial navigation tasks. Further analyses revealed frequency specificity of interactions characterized by greater bottom-up information flow from hippocampus to PFC in delta-theta band (0.5-8Hz); in contrast, top-down information flow from PFC to hippocampus was stronger in beta band (12-30Hz). Bayesian analysis revealed a high degree of replicability between the two spatial navigation tasks (Bayes factor > 5.46e+3) and across tasks spanning the spatial and verbal memory domains (Bayes factor > 7.32e+8). Our findings identify a domain-independent and replicable frequency-dependent feedback loop engaged during memory formation in the human brain.

Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks.

A framework to pinpoint the scope of unconscious processing is critical to improve models of visual consciousness. Previous research observed brain signatures of unconscious processing in visual cortex, but these were not reliably identified. Further, whether unconscious contents are represented in high-level stages of the ventral visual stream and linked parieto-frontal areas remains unknown. Using a within-subject, high-precision functional magnetic resonance imaging approach, we show that unconscious contents can be decoded from multi-voxel patterns that are highly distributed alongside the ventral visual pathway and also involving parieto-frontal substrates. Classifiers trained with multi-voxel patterns of conscious items generalized to predict the unconscious counterparts, indicating that their neural representations overlap. These findings suggest revisions to models of consciousness such as the neuronal global workspace. We then provide a computational simulation of visual processing/representation without perceptual sensitivity by using deep neural networks performing a similar visual task. The work provides a framework for pinpointing the representation of unconscious knowledge across different task domains.

MDMN: Multi-task and Domain Adaptation based Multi-modal Network for early rumor detection

With the development of social media, people tend to express their opinions on multimedia such as text, photos, audios, and videos. Meanwhile, more rumors hiding in multi-modal content are misleading social media users. The early rumor detection task aims to detect rumors before spreading. However, annotation on multi-modal data often involves a large amount of manpower. Existing approaches universally used transfer learning to overcome it. But they ignored the differences between the source domain of pre-trained models and the task domain. In this paper, Multi-task and Domain Adaptation based Multi-modal Network (MDMN) is proposed, which consists of three components: Textual Feature Extractor, Visual Feature Extractor, and Fusion & Classification Network. To improve the diversity and stability of textual representation, a Multi-task Sharing Layer, a task-specific Transformer Encoder and a Selection Layer are applied. Domain Adaptation is involved in training an adaptive model for extracting visual representation. The adaptive models can encode task data better than fine-tuning the pre-trained models. Then, multi-modal representations are fused through two fusion strategies, each having their own benefits. The experiment on multi-modal datasets collected from Weibo and Twitter show that the proposed MDMN can outperform the baseline methods. The decision-level fusion strategy achieves a Recall of over 92%.

Helping People Through Space and Time: Assistance as a Perspective on Human-Robot Interaction.

As assistive robotics has expanded to many task domains, comparing assistive strategies among the varieties of research becomes increasingly difficult. To begin to unify the disparate domains into a more general theory of assistance, we present a definition of assistance, a survey of existing work, and three key design axes that occur in many domains and benefit from the examination of assistance as a whole. We first define an assistance perspective that focuses on understanding a robot that is in control of its actions but subordinate to a user’s goals. Next, we use this perspective to explore design axes that arise from the problem of assistance more generally and explore how these axes have comparable trade-offs across many domains. We investigate how the assistive robot handles other people in the interaction, how the robot design can operate in a variety of action spaces to enact similar goals, and how assistive robots can vary the timing of their actions relative to the user’s behavior. While these axes are by no means comprehensive, we propose them as useful tools for unifying assistance research across domains and as examples of how taking a broader perspective on assistance enables more cross-domain theorizing about assistance.

Selecting Workers Wisely for Crowdsourcing When Copiers and Domain Experts Co-exist

Crowdsourcing integrates human wisdom to solve problems. Tremendous research efforts have been made in this area. However, most of them assume that workers have the same credibility in different domains and workers complete tasks independently. This leads to an inaccurate evaluation of worker credibility, hampering crowdsourcing results. To consider the impact of worker domain expertise, we adopted a vector to more accurately measure the credibility of each worker. Based on this measurement and prior task domain knowledge, we calculated fine-grained worker credibility on each given task. To avoid tasks being assigned to dependent workers who copy answers from others, we conducted copier detection via Bayesian analysis. We designed a crowdsourcing system called SWWC composed of a task assignment stage and a truth discovery stage. In the task assignment stage, we assigned tasks wisely to workers based on worker domain expertise calculation and copier removal. In the truth discovery stage, we computed the estimated truth and worker credibility by an iterative method. Then, we updated the domain expertise of workers to facilitate the upcoming task assignment. We also designed initialization algorithms to better initialize the accuracy of new workers. Theoretical analysis and experimental results showed that our method had a prominent advantage, especially under a copying situation.

Metacognition during unfamiliar face matching.

Kruger and Dunning (1999) described a metacognitive bias in which insight into performance is linked to competence: poorer performers are less aware of their mistakes than better performers. Competence-based insight has been argued to apply generally across task domains, including a recent report investigating social cognition using a variety of face-matching tasks. Problematically, serious statistical and methodological criticisms have been directed against the traditional method of analysis used by researchers in this field. Here, we further illustrate these issues and investigate new sources of insight within unfamiliar face matching. Over two experiments (total N=1077), where Experiment 2 was a preregistered replication of the key findings from Experiment 1, we found that insight into performance was multi-faceted. Participants demonstrated insight which was not based on competence, in the form of accurate updating of estimated performance. We also found evidence of insight which was based on competence: the difference in confidence on correct versus incorrect trials increased with competence. By providing ways that we can move beyond problematic, traditional approaches, we have begun to reveal a more realistic story regarding the nature of insight into face perception.

Gating Mechanism in Deep Neural Networks for Resource-Efficient Continual Learning

Catastrophic forgetting is a well-known tendency in continual learning of a deep neural network to forget previously learned knowledge when optimizing for sequentially incoming tasks. To address the issue, several methods have been proposed in research on continual learning. However, these methods cannot preserve the previously learned knowledge when training for a new task. Moreover, these methods are susceptible to negative interference between tasks, which may lead to catastrophic forgetting. It even becomes increasingly severe when there exists a notable gap between the domains of tasks. This paper proposes a novel method of controlling gates to select a subset of parameters learned for old tasks, which are then used to optimize a new task while avoiding negative interference efficiently. The proposed approach executes the subset of old parameters that provides positive responses by evaluating the effect when the old and new parameters are used together. The execution or skipping of old parameters through the gates is based on several responses across the network. We evaluate the proposed method in different continual learning scenarios involving image classification datasets. The proposed method outperforms other competitive methods and requires fewer parameters than the state-of-the-art methods during inference by applying the proposed gating mechanism that selectively involves a set of old parameters that provides positive prior knowledge to newer tasks. Additionally, we further prove the effectiveness of the proposed method through various analyses.

Weighted Correlation Embedding Learning for Domain Adaptation.

Domain adaptation leverages rich knowledge from a related source domain so that it can be used to perform tasks in a target domain. For more knowledge to be obtained under relaxed conditions, domain adaptation methods have been widely used in pattern recognition and image classification. However, most of the existing domain adaptation methods only consider how to minimize different distributions of the source and target domains, which neglects what should be transferred for a specific task and suffers negative transfer by distribution outliers. To address these problems, in this paper, we propose a novel domain adaptation method called weighted correlation embedding learning (WCEL) for image classification. In the WCEL approach, we seamlessly integrated correlation learning, graph embedding, and sample reweighting into a unified learning model. Specifically, we extracted the maximum correlated features from the source and target domains for image classification tasks. In addition, two graphs were designed to preserve the discriminant information from interclass samples and neighborhood relations in intraclass samples. Furthermore, to prevent the negative transfer problem, we developed an efficient sample reweighting strategy to predict the target with different confidence levels. To verify the performance of the proposed method in image classification, extensive experiments were conducted with several benchmark databases, verifying the superiority of the WCEL method over other state-of-the-art domain adaptation algorithms.

Discriminative Extreme Learning Machine with Cross‐Domain Mean Approximation for Unsupervised Domain Adaptation

Extreme Learning Machine (ELM) is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation (DA) in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross‐Domain Mean Approximation (DELM‐CDMA) for unsupervised domain adaptation. It introduces Cross‐Domain Mean Approximation (CDMA) into the hidden layer of ELM to reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis (LDA) is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross‐Domain Mean Approximation (DKELM‐CDMA) as the kernelization extension of DELM‐CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM‐CDMA and DKELM‐CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches.

A Named Entity Recognition Model Based on Multi-Task Learning and Cascading Pointer Network

Abstract Because, the existing named entity recognition models lack the Specificity of the field, and most of their combine the prediction of entity Location and entity category, which results in the accumulation of errors. So, named entity recognition model based on multi-task learning and pointer network is proposed, and innovations are made in the task Construction and domain entity information utilization in the model of Named entity recognition. This model is based on Transformer with multi-Head attention mechanism, and decomposes traditional tasks in entity recognition tasks and entity classification tasks, and carries out multi-tasks Task learning to reduce the accumulation of errors between tasks. Model in this paper also uses the similarity calculation based on the Comprehensive description of the entity category in entity classification Task for better pertinence of the domain entity. Experiments are conducted on public datasets and domain datasets to prove the advancement of the Model.