Attention in Psychology, Neuroscience, and Machine Learning

Effects of memory and attention on the association between video game addiction and cognitive/learning skills in children: mediational analysis

BACKGROUND

Video games have become a prevalent source of entertainment, especially among children. Furthermore, the amount of time spent playing video games has grown dramatically. The purpose of this research was to examine the mediation effects of attention and child memory on the relationship between video games addiction and cognitive and learning abilities in Egyptian children.

METHODS

A cross-sectional research design was used in the current study in two schools affiliated with Dakahlia District, Egypt. The study included 169 children aged 9 to 13 who met the inclusion criteria, and their mothers provided the questionnaire responses. The data collection methods were performed over approximately four months from February to May. Data were collected using different tools: Socio-demographic Interview, Game Addiction Scale for Children (GASC), Children's Memory Questionnaire (CMQ), Clinical Attention Problems Scale, Learning, Executive, and Attention Functioning (LEAF) Scale.

RESULTS

There was a significant indirect effect of video game addiction on cognitive and learning skills through attention, but not child memory. Video game addiction has a significant impact on children's attention and memory. Both attention and memory have a significant impact on a child's cognitive and learning skills.

CONCLUSIONS

These results revealed the significant effect of video game addiction on cognitive and learning abilities in the presence of mediators. It also suggested that attention-focused therapies might play an important role in minimizing the harmful effects of video game addiction on cognitive and learning abilities.

Introduction to the Emerging Cognitive Science of Distributed Human-Autonomy Teams

Teams are a fundamental aspect of life-from sports to business, to defense, to science, to education. While the cognitive sciences tend to focus on information processing within individuals, others have argued that teams are also capable of demonstrating cognitive capacities similar to humans, such as skill acquisition and forgetting (cf., Cooke, Gorman, Myers, & Duran, 2013; Fiore et al., 2010). As artificially intelligent and autonomous systems improve in their ability to learn, reason, interact, and coordinate with human teammates combined with the observation that teams can express cognitive capacities typically seen in individuals, a cognitive science of teams is emerging. Consequently, new questions are being asked about teams regarding teamness, trust, the introduction and effects of autonomous systems on teams, and how best to measure team behavior and phenomena. In this topic, four facets of human-autonomy team cognition are introduced with leaders in the field providing in-depth articles associated with one or more of the facets: (1) defining teams; (2) how trust is established, maintained, and repaired when broken; (3) autonomous systems operating as teammates; and (4) metrics for evaluating team cognition across communication, coordination, and performance.

Object-based suppression in target search but not in distractor inhibition

The present study investigated the effect of object representation on attentional priority regarding distractor inhibition and target search processes while the statistical regularities of singleton distractor location were biased. A color singleton distractor appeared more frequently at one of six stimulus locations, called the 'high-probability location,' to induce location-based suppression. Critically, three objects were presented, each of which paired two adjacent stimuli in a target display by adding background contours (Experiment 1) or using perceptual grouping (Experiments 2 and 3). The results revealed that attention capture by singleton distractors was hardly modulated by objects. In contrast, target selection was impeded at the location in the object containing the high-probability location compared to an equidistant location in a different object. This object-based suppression in target selection was evident when object-related features were parts of task-relevant features. These findings suggest that task-irrelevant objects modulate attentional suppression. Moreover, different features are engaged in determining attentional priority for distractor inhibition and target search processes.

Effects of brief mindfulness meditation training on attention and dispositional mindfulness in young adult males

This study examined the impact of brief mindfulness meditation (BMM) training on attention function and dispositional mindfulness in young males. 126 male participants aged 18-26 from the security industry were recruited, with 66 participants (M = 22.84, SD = 2.41) undergoing 4-week mindfulness meditation training and 60 participants (M = 23.07, SD = 2.29) in the active control group. The intervention was integrated into the participants' schedules. Measures included Five Facets Mindfulness Questionnaires (FFMQ), concentration and assignment attention tasks, Attention Network Test (ANT), and saliva cortisol concentration. Findings indicate that brief mindfulness meditation training led to significant improvements in participants' FFMQ scores), with marginally significant enhancements in the executive control network. However, it had no discernible effect on alertness and orientation networks. Additionally, brief mindfulness meditation training enhanced attention allocation to light stimulation and prolonged individual attention. Surprisingly, there was no observed decrease in saliva cortisol concentration among meditation training participants. However, this study did not find a decrease in saliva cortisol concentration in the brief mindfulness meditation group. In conclusion, this study highlights the potential of a 4-week brief mindfulness meditation training program to enhance dispositional mindfulness and specific aspects of attention function in young men, offering practical insights into the benefits of mindfulness meditation practices for this demographic.

Effects of School-Based Neurofeedback Training on Attention in Students with Autism and Intellectual Disabilities

In this study we aimed to assess the influence of school-based neurofeedback training on the attention of students with autism and intellectual disabilities. We assessed 24 students of a special education center who attended neurofeedback training sessions during the schoolyear; we also assessed 25 controls from the same center. We used two computer tasks to assess sustained attention in simple and cognitively demanding test situations, and we used a pen-and-paper task to assess selective attention. Each student who took part in the study was tested at the beginning and at the end of the schoolyear. Students from the experimental group significantly improved their performance in the task related to sustained attention to simple stimuli. No performance improvement related to neurofeedback treatment was observed in either sustained attention in cognitively demanding situations or selective attention. School-based neurofeedback training may improve sustained attention to simple stimuli in students with developmental disabilities.

The neuroscience of active learning and direct instruction

Throughout the educational system, students experiencing active learning pedagogy perform better and fail less than those taught through direct instruction. Can this be ascribed to differences in learning from a neuroscientific perspective? This review examines mechanistic, neuroscientific evidence that might explain differences in cognitive engagement contributing to learning outcomes between these instructional approaches. In classrooms, direct instruction comprehensively describes academic content, while active learning provides structured opportunities for learners to explore, apply, and manipulate content. Synaptic plasticity and its modulation by arousal or novelty are central to all learning and both approaches. As a form of social learning, direct instruction relies upon working memory. The reinforcement learning circuit, associated agency, curiosity, and peer-to-peer social interactions combine to enhance motivation, improve retention, and build higher-order-thinking skills in active learning environments. When working memory becomes overwhelmed, additionally engaging the reinforcement learning circuit improves retention, providing an explanation for the benefits of active learning. This analysis provides a mechanistic examination of how emerging neuroscience principles might inform pedagogical choices at all educational levels.

The relational bottleneck as an inductive bias for efficient abstraction

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.

Co-perceiving: Bringing the social into perception

Humans and other animals possess the remarkable ability to effectively navigate a shared perceptual environment by discerning which objects and spaces are perceived by others and which remain private to themselves. Traditionally, this capacity has been encapsulated under the umbrella of joint attention or joint action. In this comprehensive review, we advocate for a broader and more mechanistic understanding of this phenomenon, termed co-perception. Co-perception encompasses the sensitivity to the perceptual engagement of others and the capability to differentiate between objects perceived privately and those perceived commonly with others. It represents a distinct concept from mere simultaneous individual perception. Moreover, discerning between private and common objects doesn't necessitate intricate mind-reading abilities or mutual coordination. The act of perceiving objects as either private or common provides a comprehensive account for social scenarios where individuals simply share the same context or may even engage in competition. This conceptual framework encourages a re-examination of classical paradigms that demonstrate social influences on perception. Furthermore, it suggests that the impacts of shared experiences extend beyond affective responses, also influencing perceptual processes. This article is categorized under: Psychology > Attention Philosophy > Foundations of Cognitive Science Philosophy > Psychological Capacities.

Theta and alpha oscillations may underlie improved attention and working memory in musically trained children

INTRODUCTION

Attention and working memory are key cognitive functions that allow us to select and maintain information in our mind for a short time, being essential for our daily life and, in particular, for learning and academic performance. It has been shown that musical training can improve working memory performance, but it is still unclear if and how the neural mechanisms of working memory and particularly attention are implicated in this process. In this work, we aimed to identify the oscillatory signature of bimodal attention and working memory that contributes to improved working memory in musically trained children.

MATERIALS AND METHODS

We recruited children with and without musical training and asked them to complete a bimodal (auditory/visual) attention and working memory task, whereas their brain activity was measured using electroencephalography. Behavioral, time-frequency, and source reconstruction analyses were made.

RESULTS

Results showed that, overall, musically trained children performed better on the task than children without musical training. When comparing musically trained children with children without musical training, we found modulations in the alpha band pre-stimuli onset and the beginning of stimuli onset in the frontal and parietal regions. These correlated with correct responses to the attended modality. Moreover, during the end phase of stimuli presentation, we found modulations correlating with correct responses independent of attention condition in the theta and alpha bands, in the left frontal and right parietal regions.

CONCLUSIONS

These results suggest that musically trained children have improved neuronal mechanisms for both attention allocation and memory encoding. Our results can be important for developing interventions for people with attention and working memory difficulties.

The cognitive reality monitoring network and theories of consciousness

Theories of consciousness abound. However, it is difficult to arbitrate reliably among competing theories because they target different levels of neural and cognitive processing or anatomical loci, and only some were developed with computational models in mind. In particular, theories of consciousness need to fully address the three levels of understanding of the brain proposed by David Marr: computational theory, algorithms and hardware. Most major theories refer to only one or two levels, often indirectly. The cognitive reality monitoring network (CRMN) model is derived from computational theories of mixture-of-experts architecture, hierarchical reinforcement learning and generative/inference computing modules, addressing all three levels of understanding. A central feature of the CRMN is the mapping of a gating network onto the prefrontal cortex, making it a prime coding circuit involved in monitoring the accuracy of one's mental states and distinguishing them from external reality. Because the CRMN builds on the hierarchical and layer structure of the cerebral cortex, it may connect research and findings across species, further enabling concrete computational models of consciousness with new, explicitly testable hypotheses. In sum, we discuss how the CRMN model can help further our understanding of the nature and function of consciousness.

We know what attention is!

Attention is one of the most thoroughly investigated psychological phenomena, yet skepticism about attention is widespread: we do not know what it is, it is too many things, there is no such thing. The deficiencies highlighted are not about experimental work but the adequacy of the scientific theory of attention. Combining common scientific claims about attention into a single theory leads to internal inconsistency. This paper demonstrates that a specific functional conception of attention is incorporated into the tasks used in standard experimental paradigms. In accepting these paradigms as valid probes of attention, we commit to this common conception. The conception unifies work at multiple levels of analysis into a coherent scientific explanation of attention. Thus, we all know what attention is.

Political reinforcement learners

Politics can seem home to the most calculating and yet least rational elements of humanity. How might we systematically characterize this spectrum of political cognition? Here, we propose reinforcement learning (RL) as a unified framework to dissect the political mind. RL describes how agents algorithmically navigate complex and uncertain domains like politics. Through this computational lens, we outline three routes to political differences, stemming from variability in agents' conceptions of a problem, the cognitive operations applied to solve the problem, or the backdrop of information available from the environment. A computational vantage on maladies of the political mind offers enhanced precision in assessing their causes, consequences, and cures.

Understanding Collective Intelligence: Investigating the Role of Collective Memory, Attention, and Reasoning Processes

As society has come to rely on groups and technology to address many of its most challenging problems, there is a growing need to understand how technology-enabled, distributed, and dynamic collectives can be designed to solve a wide range of problems over time in the face of complex and changing environmental conditions-an ability we define as "collective intelligence." We describe recent research on the Transaction Systems Model of Collective Intelligence (TSM-CI) that integrates literature from diverse areas of psychology to conceptualize the underpinnings of collective intelligence. The TSM-CI articulates the development and mutual adaptation of transactive memory, transactive attention, and transactive reasoning systems that together support the emergence and maintenance of collective intelligence. We also review related research on computational indicators of transactive-system functioning based on collaborative process behaviors that enable agent-based teammates to diagnose and potentially intervene to address developing issues. We conclude by discussing future directions in developing the TSM-CI to support research on developing collective human-machine intelligence and to identify ways to design technology to enhance it.

Predictors for quality of life in older adults: network analysis on cognitive and neuropsychiatric symptoms

BACKGROUND

Quality of life (QoL) of older adults has become a pivotal concern of the public and health system. Previous studies found that both cognitive decline and neuropsychiatric symptoms (NPS) can affect QoL in older adults. However, it remains unclear how these symptoms are related to each other and impact on QoL. Our aim is to investigate the complex network relationship between cognitive and NPS symptoms in older adults, and to further explore their association with QoL.

METHODS

A cross-sectional study was conducted in a sample of 389 older individuals with complaints of memory decline. The instruments included the Neuropsychiatric Inventory, the Mini Mental State Examination, and the 36-item Short Form Health Survey. Data was analyzed using network analysis and mediation analysis.

RESULTS

We found that attention and agitation were the variables with the highest centrality in cognitive and NPS symptoms, respectively. In an exploratory mediation analysis, agitation was significantly associated with poor attention (β = -0.214, P < 0.001) and reduced QoL (β = -0.137, P = 0.005). The indirect effect of agitation on the QoL through attention was significant (95% confidence interval (CI) [-0.119, -0.035]). Furthermore, attention served as a mediator between agitation and QoL, accounting for 35.09% of the total effect.

CONCLUSIONS

By elucidating the NPS-cognition-QoL relationship, the current study provides insights for developing rehabilitation programs among older adults to ensure their QoL.

Onset of Inattention and Hyperactivity in Children and Adolescents With Epilepsy 6 months After the Diagnosis

OBJECTIVE

Complete or major symptoms of ADHD are often present in epilepsy. This study evaluated inattention and hyperactivity symptoms over the first 6 months in newly diagnosed pediatric epilepsy without comorbid ADHD.

METHOD

Children and adolescents with newly diagnosed epilepsy were followed for 6 months after starting antiseizure medications. The Nisonger Child Behavior Rating Form (NCBRF), Adverse Event Profile (AEP), and the Revised Wechsler Intelligence Scale for Children were used.

RESULTS

There was a marked increase in attention difficulties while a moderate increase in hyperactivity levels. AEP scores, changes in non-verbal aspects of intelligence, levels of hyperactivity at the follow-up, and attention at baseline were significant predictors for inattention. In contrast, only levels of hyperactivity at the baseline and inattention at the follow-up were significant predictors for hyperactivity.

CONCLUSION

Significant inattention and hyperactivity levels originated 6 months after the diagnosis of epilepsy and starting antiseizure medication.

Unveiling human origins of replication using deep learning: accurate prediction and comprehensive analysis

Accurate identification of replication origins (ORIs) is crucial for a comprehensive investigation into the progression of human cell growth and cancer therapy. Here, we proposed a computational approach Ori-FinderH, which can efficiently and precisely predict the human ORIs of various lengths by combining the Z-curve method with deep learning approach. Compared with existing methods, Ori-FinderH exhibits superior performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.9616 for K562 cell line in 10-fold cross-validation. In addition, we also established a cross-cell-line predictive model, which yielded a further improved AUC of 0.9706. The model was subsequently employed as a fitness function to support genetic algorithm for generating artificial ORIs. Sequence analysis through iORI-Euk revealed that a vast majority of the created sequences, specifically 98% or more, incorporate at least one ORI for three cell lines (Hela, MCF7 and K562). This innovative approach could provide more efficient, accurate and comprehensive information for experimental investigation, thereby further advancing the development of this field.

Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments

BACKGROUND AND OBJECTIVE

Obstructive sleep apnea (OSA) is a common sleep disorder that causes intermittent hypoxia and sleep fragmentation, leading to attention impairment and other cognitive deficits. Magnetic resonance imaging (MRI) is a powerful modality that can reveal the structural and functional brain alterations associated with attention impairment in OSA patients. The objective of this systematic review is to identify and synthesize the evidence on MRI biomarkers and neuropsychological assessments of attention deficits in OSA patients.

METHODS

We searched the Scopus and PubMed databases for studies that used MRI to measure biomarkers related to attention alteration in OSA patients and reported qualitative and quantitative data on the association between MRI biomarkers and attention outcomes. We also included studies that found an association between neuropsychological assessments and MRI findings in OSA patients with attention deficits.

RESULTS

We included 19 studies that met our inclusion criteria and extracted the relevant data from each study. We categorized the studies into three groups based on the MRI modality and the cognitive domain they used: structural and diffusion tensor imaging MRI findings, functional, perfusion, and metabolic MRI findings, and neuropsychological assessment findings.

CONCLUSIONS

We found that OSA is associated with structural, functional, and metabolic brain alterations in multiple regions and networks that are involved in attention processing. Treatment with continuous positive airway pressure can partially reverse some of the brain changes and improve cognitive function in some domains and in some studies. This review suggests that MRI techniques and neuropsychological assessments can be useful tools for monitoring the progression and response to treatment of OSA patients.

Modelling decision-making biases

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate.

An eye tracking investigation of attention mechanism in driving behavior under emotional issues and cognitive load

Emotions have specific effects on behavior. At present, studies are increasingly interested in how emotions affect driving behavior. We designed the experiment by combing driving tasks and eye tracking. DSM-V assessment scale was applied to evaluate the depression and manic for participants. In order to explore the dual impacts of emotional issues and cognitive load on attention mechanism, we defined the safety-related region as the area of interest (AOI) and quantified the concentration of eye tracking data. Participants with depression issues had lower AOI sample percentage and shorter AOI fixation duration under no external cognitive load. During our experiment, the depression group had the lowest accuracy in arithmetic quiz. Additionally, we used full connected network to detect the depression group from the control group, reached 83.33%. Our experiment supported that depression have negative influences on driving behavior. Participants with depression issues reduced attention to the safety-related region under no external cognitive load, they were more prone to have difficulties in multitasking when faced with high cognitive load. Besides, participants tended to reallocate more attention resources to the central area under high cognitive load, a phenomenon we called "visual centralization" in driving behavior.

Strip steel surface defect detection based on lightweight YOLOv5

Deep learning-based methods for detecting surface defects on strip steel have advanced detection capabilities, but there are still problems of target loss, false alarms, large computation, and imbalance between detection accuracy and detection speed. In order to achieve a good balance between detection accuracy and speed, a lightweight YOLOv5 strip steel surface defect detection algorithm based on YOLOv5s is proposed. Firstly, we introduce the efficient lightweight convolutional layer called GSConv. The Slim Neck, designed based on GSConv, replaces the original algorithm's neck, reducing the number of network parameters and improving detection speed. Secondly, we incorporate SimAM, a non-parametric attention mechanism, into the improved neck to enhance detection accuracy. Finally, we utilize the SIoU function as the regression prediction loss instead of the original CIoU to address the issue of slow convergence and improve efficiency. According to experimental findings, the YOLOv5-GSS algorithm outperforms the YOLOv5 method by 2.9% on the NEU-DET dataset and achieves an average accuracy (mAP) of 83.8% with a detection speed (FPS) of 100 Hz, which is 3.8 Hz quicker than the YOLOv5 algorithm. The proposed model outperforms existing approaches and is more useful, demonstrating the efficacy of the optimization strategy.

Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System

Suicidal behavior among young people has become an increasingly relevant topic after the COVID-19 pandemic and constitutes a public health problem. This study aimed to examine the variables associated with suicide risk and determine their predictive capacity. The specific objectives were: (1) to analyze the relationship between suicide risk and model variables and (2) to design an artificial neural network (ANN) with predictive capacity for suicide risk. The sample comprised 337 youths aged 18-33 years. An ex post facto design was used. The results showed that emotional attention, followed by problem solving and perfectionism, were variables that contributed the most to the ANN's predictive capacity. The ANN achieved a hit rate of 85.7%, which is much higher than chance, and with only 14.3% of incorrect cases. This study extracted relevant information on suicide risk and the related risk and protective factors via artificial intelligence. These data will be useful for diagnosis as well as for psycho-educational guidance and prevention. This study was one of the first to apply this innovative methodology based on an ANN design to study these variables.

Transformer-Based Semantic Segmentation for Extraction of Building Footprints from Very-High-Resolution Images

Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images. Vision Transformer networks have shown significant improvements in performance compared to traditional convolutional neural networks (CNNs) in semantic segmentation. Vision Transformer networks have different architectures to CNNs. Image patches, linear embedding, and multi-head self-attention (MHSA) are several of the main hyperparameters. How we should configure them for the extraction of objects in VHR images and how they affect the accuracy of networks are topics that have not been sufficiently investigated. This article explores the role of vision Transformer networks in the extraction of building footprints from very-high-resolution (VHR) images. Transformer-based models with different hyperparameter values were designed and compared, and their impact on accuracy was analyzed. The results show that smaller image patches and higher-dimension embeddings result in better accuracy. In addition, the Transformer-based network is shown to be scalable and can be trained with general-scale graphics processing units (GPUs) with comparable model sizes and training times to convolutional neural networks while achieving higher accuracy. The study provides valuable insights into the potential of vision Transformer networks in object extraction using VHR images.

Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification

Exploring the effective signal features of electroencephalogram (EEG) signals is an important issue in the research of brain-computer interface (BCI), and the results can reveal the motor intentions that trigger electrical changes in the brain, which has broad research prospects for feature extraction from EEG data. In contrast to previous EEG decoding methods that are based solely on a convolutional neural network, the traditional convolutional classification algorithm is optimized by combining a transformer mechanism with a constructed end-to-end EEG signal decoding algorithm based on swarm intelligence theory and virtual adversarial training. The use of a self-attention mechanism is studied to expand the receptive field of EEG signals to global dependence and train the neural network by optimizing the global parameters in the model. The proposed model is evaluated on a real-world public dataset and achieves the highest average accuracy of 63.56% in cross-subject experiments, which is significantly higher than that found for recently published algorithms. Additionally, good performance is achieved in decoding motor intentions. The experimental results show that the proposed classification framework promotes the global connection and optimization of EEG signals, which can be further applied to other BCI tasks.

The neurobiology of functional neurological disorders characterised by impaired awareness

We review the neurobiology of Functional Neurological Disorders (FND), i.e., neurological disorders not explained by currently identifiable histopathological processes, in order to focus on those characterised by impaired awareness (functionally impaired awareness disorders, FIAD), and especially, on the paradigmatic case of Resignation Syndrome (RS). We thus provide an improved more integrated theory of FIAD, able to guide both research priorities and the diagnostic formulation of FIAD. We systematically address the diverse spectrum of clinical presentations of FND with impaired awareness, and offer a new framework for understanding FIAD. We find that unraveling the historical development of neurobiological theory of FIAD is of paramount importance for its current understanding. Then, we integrate contemporary clinical material in order to contextualise the neurobiology of FIAD within social, cultural, and psychological perspectives. We thus review neuro-computational insights in FND in general, to arrive at a more coherent account of FIAD. FIAD may be based on maladaptive predictive coding, shaped by stress, attention, uncertainty, and, ultimately, neurally encoded beliefs and their updates. We also critically appraise arguments in support of and against such Bayesian models. Finally, we discuss implications of our theoretical account and provide pointers towards an improved clinical diagnostic formulation of FIAD. We suggest directions for future research towards a more unified theory on which future interventions and management strategies could be based, as effective treatments and clinical trial evidence remain limited.

Mindfulness Training Improves Attention: Evidence from Behavioral and Event-related Potential Analyses

Mindfulness meditation helps to improve attentional capacity. However, the neural correlates that indicate the mechanism through which mindfulness improves attention are unclear. To address this gap, we aimed to assess the effects of mindfulness training on sustained attentional capacity. Event-related potentials (ERPs) associated with the modified sustained attention response task (mSART) were used in this study. A total of 45 college students were randomly assigned to either the mindfulness group (n = 21) or the control group (n = 24). Participants in the mindfulness group received a three-week mindfulness training. The self-report results showed that the mindfulness group reported higher mindfulness scores (observing and non-judgment of inner experiences) after the training. The mindfulness group also scored lower on the state anxiety than the control group. Behavioral results also showed that self-caught mind wandering in the mindfulness group significantly decreased after the training, and the mindfulness group showed a faster response after the training. The ERP results showed that N2 amplitudes in the post-test were significantly greater than those in the pre-test in the mindfulness group. We did not find any interactions between group and time for P3. The findings suggest that mindfulness training can effectively improve sustained attentional capacity, as indicated by reduced mind wandering and increased N2 responses.

Large-Kernel Attention for 3D Medical Image Segmentation

Automated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.

A comprehensive review of attention tests: can we assess what we exactly do not understand?

Attention, as it is now defined as a process matching data from the environment to the needs of the organism, is one of the main aspects of human cognitive processes. There are several aspects to attention including tonic alertness (a process of intrinsic arousal that varies by minutes to hours), phasic alertness (a process that causes a quick change in attention as a result of a brief stimulus), selective attention (a process differentiating multiple stimuli), and sustained attention (a process maintaining persistence of response and continuous effort over an extended period). Attention dysfunction is associated with multiple disorders; therefore, there has been much effort in assessing attention and its domains, resulting in a battery of tests evaluating one or several attentional domains; instances of which are the Stroop color-word test, Test of Everyday Attention, Wisconsin Card Sorting Test, and Cambridge Neuropsychological Test Automated Battery. These tests vary in terms of utilities, range of age, and domains. The role of attention in human life and the importance of assessing it merits an inclusive review of the efforts made to assess attention and the resulting tests; Here we highlight all the necessary data regarding neurophysiological tests which assess human attentive function and investigates the evolution of attention tests over time. Also, the ways of assessing the attention in untestable patients who have difficulty in reading or using a computer, along with the lack of ability to comprehend verbal instructions and executive tasks, are discussed. This review can be of help as a platform for designing new studies to researchers who are interested in working on attention and conditions causing deficits in this aspect of body function, by collecting and organizing information on its assessment.

Active visual search in naturalistic environments reflects individual differences in classic visual search performance

Visual search is a ubiquitous activity in real-world environments. Yet, traditionally, visual search is investigated in tightly controlled paradigms, where head-restricted participants locate a minimalistic target in a cluttered array that is presented on a computer screen. Do traditional visual search tasks predict performance in naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality technology to test the degree to which classic and naturalistic search are limited by a common factor, set size, and the degree to which individual differences in classic search behavior predict naturalistic search behavior in a large sample of individuals (N = 75). In a naturalistic search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic search task, participants searched for a target within a simple array of colored letters using only eye-movements. In each task, we found that participants' search performance was impacted by increases in set size-the number of items in the visual display. Critically, we observed that participants' efficiency in classic search tasks-the degree to which set size slowed performance-indeed predicted efficiency in real-world scenes. These results demonstrate that classic, computer-based visual search tasks are excellent models of active, real-world search behavior.

Cognition Assessment Technologies on Deaf People

In recent years there has been a growing interest in research about the different ways of processing and consolidating cognition in deaf people. It is known that hearing loss can lead to differences in some executive functions like control inhibitory or working memory. This literature review describes executive functions in deaf people and how they could be evaluated through technological devices complementing traditional assessments, like neuropsychological batteries. We identified biometric devices, digital and physical interfaces, and software from the literature, whose goal is to design or adapt technology to assess some cognition domains in several ways. The results of the review suggest the need to understand the cognitive phenomenon that significantly impacts the context of deaf people; moreover, it becomes relevant as a line of research in the Cognitive Science of Hearing. Using technologies to measure them and gain a better understanding of cognition in deaf people may provide possibilities for designing or adapting targeted educational or therapeutic strategies.

Millimeter-wave radar object classification using knowledge-assisted neural network

To improve the cognition and understanding capabilities of artificial intelligence (AI) technology, it is a tendency to explore the human brain learning processing and integrate brain mechanisms or knowledge into neural networks for inspiration and assistance. This paper concentrates on the application of AI technology in advanced driving assistance system. In this field, millimeter-wave radar is essential for elaborate environment perception due to its robustness to adverse conditions. However, it is still challenging for radar object classification in the complex traffic environment. In this paper, a knowledge-assisted neural network (KANN) is proposed for radar object classification. Inspired by the human brain cognition mechanism and algorithms based on human expertise, two kinds of prior knowledge are injected into the neural network to guide its training and improve its classification accuracy. Specifically, image knowledge provides spatial information about samples. It is integrated into an attention mechanism in the early stage of the network to help reassign attention precisely. In the late stage, object knowledge is combined with the deep features extracted from the network. It contains discriminant semantic information about samples. An attention-based injection method is proposed to adaptively allocate weights to the knowledge and deep features, generating more comprehensive and discriminative features. Experimental results on measured data demonstrate that KANN is superior to current methods and the performance is improved with knowledge assistance.

Visual object detection biases escape trajectories following acoustic startle in larval zebrafish

In this study, we investigated whether the larval zebrafish is sensitive to the presence of obstacles in its environment. Zebrafish execute fast escape swims when in danger of predation. We posited that collisions with solid objects during escape would be maladaptive to the fish, and therefore, the direction of escape swims should be informed by the locations of barriers. To test this idea, we developed a closed-loop imaging rig outfitted with barriers of various qualities. We show that when larval zebrafish escape in response to a non-directional vibrational stimulus, they use visual scene information to avoid collisions with obstacles. Our study demonstrates that barrier avoidance rate corresponds to the absolute distance of obstacles, as distant barriers outside of collision range elicit less bias than nearby collidable barriers that occupy the same amount of visual field. The computation of barrier avoidance is covert: the fact that fish will avoid barriers during escape cannot be predicted by its routine swimming behavior in the barrier arena. Finally, two-photon laser ablation experiments suggest that excitatory bias is provided to the Mauthner cell ipsilateral to approached barriers, either via direct excitation or a multi-step modulation process. We ultimately propose that zebrafish detect collidable objects via an integrative visual computation that is more complex than retinal occupancy alone, laying a groundwork for understanding how cognitive physical models observed in humans are implemented in an archetypal vertebrate brain. VIDEO ABSTRACT.

V2ReID: Vision-Outlooker-Based Vehicle Re-Identification

With the increase of large camera networks around us, it is becoming more difficult to manually identify vehicles. Computer vision enables us to automate this task. More specifically, vehicle re-identification (ReID) aims to identify cars in a camera network with non-overlapping views. Images captured of vehicles can undergo intense variations of appearance due to illumination, pose, or viewpoint. Furthermore, due to small inter-class similarities and large intra-class differences, feature learning is often enhanced with non-visual cues, such as the topology of camera networks and temporal information. These are, however, not always available or can be resource intensive for the model. Following the success of Transformer baselines in ReID, we propose for the first time an outlook-attention-based vehicle ReID framework using the Vision Outlooker as its backbone, which is able to encode finer-level features. We show that, without embedding any additional side information and using only the visual cues, we can achieve an 80.31% mAP and 97.13% R-1 on the VeRi-776 dataset. Besides documenting our research, this paper also aims to provide a comprehensive walkthrough of vehicle ReID. We aim to provide a starting point for individuals and organisations, as it is difficult to navigate through the myriad of complex research in this field.

Guiding visual attention in deep convolutional neural networks based on human eye movements

Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, and consequently indicate strong architectural and functional parallelism with the ventral visual pathway throughout comparisons with neuroimaging and neural time series data. As recent advances in deep learning seem to decrease this similarity, computational neuroscience is challenged to reverse-engineer the biological plausibility to obtain useful models. While previous studies have shown that biologically inspired architectures are able to amplify the human-likeness of the models, in this study, we investigate a purely data-driven approach. We use human eye tracking data to directly modify training examples and thereby guide the models’ visual attention during object recognition in natural images either toward or away from the focus of human fixations. We compare and validate different manipulation types (i.e., standard, human-like, and non-human-like attention) through GradCAM saliency maps against human participant eye tracking data. Our results demonstrate that the proposed guided focus manipulation works as intended in the negative direction and non-human-like models focus on significantly dissimilar image parts compared to humans. The observed effects were highly category-specific, enhanced by animacy and face presence, developed only after feedforward processing was completed, and indicated a strong influence on face detection. With this approach, however, no significantly increased human-likeness was found. Possible applications of overt visual attention in DCNNs and further implications for theories of face detection are discussed.

Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques

Crop yield and its prediction are crucial in agricultural production planning. This study investigates and predicts arabica coffee yield in order to match the market demand, using artificial neural networks (ANN) and multiple linear regression (MLR). Data of six variables, including areas, productivity zones, rainfalls, relative humidity, and minimum and maximum temperature, were collected for the recent 180 months between 2004 and 2018. The predicted yield of the cherry coffee crop continuously increases each year. From the dataset, it was found that the prediction accuracy of the R² and RMSE from ANN was 0.9524 and 0.0784 tons, respectively. The ANN model showed potential in determining the cherry coffee yields.

Preattentive facilitation of target trajectories in a dragonfly visual neuron

The ability to pursue targets in visually cluttered and distraction-rich environments is critical for predators such as dragonflies. Previously, we identified Centrifugal Small-Target Motion Detector 1 (CSTMD1), a dragonfly visual neuron likely involved in such target-tracking behaviour. CSTMD1 exhibits facilitated responses to targets moving along a continuous trajectory. Moreover, CSTMD1 competitively selects a single target out of a pair. Here, we conducted in vivo, intracellular recordings from CSTMD1 to examine the interplay between facilitation and selection, in response to the presentation of paired targets. We find that neuronal responses to both individual trajectories of simultaneous, paired targets are facilitated, rather than being constrained to the single, selected target. Additionally, switches in selection elicit suppression which is likely an important attribute underlying target pursuit. However, binocular experiments reveal these results are constrained to paired targets within the same visual hemifield, while selection of a target in one visual hemifield establishes ocular dominance that prevents facilitation or response to contralaterally presented targets. These results reveal that the dragonfly brain preattentively represents more than one target trajectory, to balance between attentional flexibility and resistance against distraction.

A dragonfly visual neuron independently facilitates responses to rival targets within the same visual field, mediating selective attention.

The Multifaceted Nature of Bilingualism and Attention

Attention has recently been proposed as the mechanism underlying the cognitive effects associated with bilingualism. However, similar to bilingualism, the term attention is complex, dynamic, and can vary from one activity to another. Throughout our daily lives, we use different types of attention that differ in complexity: sustained attention, selective attention, alternating attention, divided attention, and disengagement of attention. The present paper is a focused review summarizing the results from studies that explore the link between bilingualism and attention. For each level of attention, a brief overview of relevant theoretical models will be discussed along with a spotlight on paradigms and tasks used to measure these forms of attention. The findings illustrate that different types and levels of attention are modified by the variety of bilingual experiences. Future studies wishing to examine the effects of bilingualism on attention are encouraged to embrace the complexity and diversity of both constructs rather than making global claims about bilingualism and attention.

Could simplified stimuli change how the brain performs visual search tasks? A deep neural network study

Visual search is a complex behavior influenced by many factors. To control for these factors, many studies use highly simplified stimuli. However, the statistics of these stimuli are very different from the statistics of the natural images that the human visual system is optimized by evolution and experience to perceive. Could this difference change search behavior? If so, simplified stimuli may contribute to effects typically attributed to cognitive processes, such as selective attention. Here we use deep neural networks to test how optimizing models for the statistics of one distribution of images constrains performance on a task using images from a different distribution. We train four deep neural network architectures on one of three source datasets—natural images, faces, and x-ray images—and then adapt them to a visual search task using simplified stimuli. This adaptation produces models that exhibit performance limitations similar to humans, whereas models trained on the search task alone exhibit no such limitations. However, we also find that deep neural networks trained to classify natural images exhibit similar limitations when adapted to a search task that uses a different set of natural images. Therefore, the distribution of data alone cannot explain this effect. We discuss how future work might integrate an optimization-based approach into existing models of visual search behavior.

The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults

Background

One of the goals of neuropsychology is to understand the brain mechanisms underlying aspects of attention and cognitive control. Several tasks have been developed as a part of this body of research, however their results are not always consistent. A reliable comparison of the data and a synthesis of study conclusions has been precluded by multiple methodological differences. Here, we describe a publicly available, high-density electroencephalography (EEG) dataset obtained from 42 healthy young adults while they performed 3 cognitive tasks: (i) an extended multi-source interference task; (ii) a 3-stimuli oddball task; (iii) a control, simple reaction task; and (iv) a resting-state protocol. Demographic and psychometric information are included within the dataset.

Dataset Validation

First, data validation confirmed acceptable quality of the obtained EEG signals. Typical event-related potential (ERP) waveforms were obtained, as expected for attention and cognitive control tasks (i.e., N200, P300, N450). Behavioral results showed the expected progression of reaction times and error rates, which confirmed the effectiveness of the applied paradigms.

Conclusions

This dataset is well suited for neuropsychological research regarding common and distinct mechanisms involved in different cognitive tasks. Using this dataset, researchers can compare a wide range of classical EEG/ERP features across tasks for any selected subset of electrodes. At the same time, 128-channel EEG recording allows for source localization and detailed connectivity studies. Neurophysiological measures can be correlated with additional psychometric data obtained from the same participants. This dataset can also be used to develop and verify novel analytical and classification approaches that can advance the field of deep/machine learning algorithms, recognition of single-trial ERP responses to different task conditions, and detection of EEG/ERP features for use in brain-computer interface applications.

School-aged children diagnosed with an FASD exhibit visuo-cortical network disturbance: A magnetoencephalography (MEG) study

Children with prenatal alcohol exposure (PAE) often suffer from cognitive and neurobehavioral dysfunction throughout their lives, which may rise to a level of concern such that children receive a diagnosis under the fetal alcohol spectrum disorders (FASD) umbrella. Magnetoencephalography (MEG) contributes direct insight into neural processing and functional connectivity measures with temporal precision to understand cortical processing disorders that manifest during development. The impairment of perception may become more consequential among school-aged children with an FASD in the process of intellectual functioning and behavioral maturation. Fifty participants with the age range of 8-13 years participated in our study following parental informed consent and child assent. For each participant, visual responses were recorded using magnetoencephalography (MEG) while performing a prosaccade task with central stimuli (fovea centralis) and peripheral stimuli (left and right of central) presented on a screen, requiring participants to shift their gaze to the stimuli. After source analysis using minimum norm estimation (MNE), we investigated visual responses from each participant by measuring the latency and amplitude of visual evoked fields. Delayed peak latency of the visual response was identified in the primary visual area (calcarine fissure) and visual association areas (v2, v3) in young children with an FASD for both stimulus types (central and peripheral). But the difference in visual response latency was only statistically significant (p ≤ 0.01) for the peripheral (right) stimulus. We also observed reduced amplitude (p ≤ 0.006) of visual evoked response in children with an FASD for the central stimulus type in both primary and visual association areas. Multiple visual areas show impairment in children with an FASD, with visual delay and conduction disturbance more prominent in response to peripheral stimuli. Children with an FASD also exhibit significantly reduced amplitude of neural activation to central stimuli. These sensory deficits may lead to slow cognitive processing speed through continued intra-cortical network disturbance in children with an FASD.

The Algorithms of Mindfulness

This paper analyzes notions and models of optimized cognition emerging at the intersections of psychology, neuroscience, and computing. What I somewhat polemically call the algorithms of mindfulness describes an ideal that determines algorithmic techniques of the self, geared at emotional resilience and creative cognition. A reframing of rest, exemplified in corporate mindfulness programs and the design of experimental artificial neural networks sits at the heart of this process. Mindfulness trainings provide cues as to this reframing, for they detail each in their own way how intermittent periods of rest are to be recruited to augment our cognitive capacities and combat the effects of stress and information overload. They typically rely on and co-opt neuroscience knowledge about what the brains of North Americans and Europeans do when we rest. Current designs for artificial neural networks draw on the same neuroscience research and incorporate coarse principles of cognition in brains to make machine learning systems more resilient and creative. These algorithmic techniques are primarily conceived to prevent psychopathologies where stress is considered the driving force of success. Against this backdrop, I ask how machine learning systems could be employed to unsettle the concept of pathological cognition itself.

Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics

Semantic knowledge about individual entities (i.e., the referents of proper names such as Jacinta Ardern) is fine-grained, episodic, and strongly social in nature, when compared with knowledge about generic entities (the referents of common nouns such as politician). We investigate the semantic representations of individual entities in the brain; and for the first time we approach this question using both neural data, in the form of newly-acquired EEG data, and distributional models of word meaning, employing them to isolate semantic information regarding individual entities in the brain. We ran two sets of analyses. The first set of analyses is only concerned with the evoked responses to individual entities and their categories. We find that it is possible to classify them according to both their coarse and their fine-grained category at appropriate timepoints, but that it is hard to map representational information learned from individuals to their categories. In the second set of analyses, we learn to decode from evoked responses to distributional word vectors. These results indicate that such a mapping can be learnt successfully: this counts not only as a demonstration that representations of individuals can be discriminated in EEG responses, but also as a first brain-based validation of distributional semantic models as representations of individual entities. Finally, in-depth analyses of the decoder performance provide additional evidence that the referents of proper names and categories have little in common when it comes to their representation in the brain.

Sliding Scale Theory of Attention and Consciousness/Unconsciousness

Attention defined as focusing on a unit of information plays a prominent role in both consciousness and the cognitive unconscious, due to its essential role in information processing. Existing theories of consciousness invariably address the relationship between attention and conscious awareness, ranging from attention is not required to crucial. However, these theories do not adequately or even remotely consider the contribution of attention to the cognitive unconscious. A valid theory of consciousness must also be a robust theory of the cognitive unconscious, a point rarely if ever considered. Current theories also emphasize human perceptual consciousness, primarily visual, despite evidence that consciousness occurs in diverse animal species varying in cognitive capacity, and across many forms of perceptual and thought consciousness. A comprehensive and parsimonious perspective applicable to the diversity of species demonstrating consciousness and the various forms—sliding scale theory of attention and consciousness/unconsciousness—is proposed with relevant research reviewed. Consistent with the continuous organization of natural events, attention occupies a sliding scale in regards to time and space compression. Unconscious attention in the form of the “cognitive unconscious” is time and spaced diffused, whereas conscious attention is tightly time and space compressed to the present moment. Due to the special clarity derived from brief and concentrated signals, the tight time and space compression yields conscious awareness as an emergent property. The present moment enhances the time and space compression of conscious attention, and contributes to an evolutionary explanation of conscious awareness.

Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention

Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of precision (internal noise suppression) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neuron's input or on its outgoing connection. We show that modulating the connection is most effective in selectively enhancing information processing by redistributing spiking activity and by introducing additional task-relevant information, as shown by representational similarity analyses. Precision only produced minor attentional effects in performance. Our results, which mirror empirical findings, show that it is possible to adjudicate between attention mechanisms using more biologically realistic models and natural stimuli.

Does the d2 Test of Attention only assess sustained attention? Evidence of working memory processes involved

The d2 Test of Attention (d2) is widely used for assessing sustained attention and we aimed at verifying whether working memory may be a secondary construct measured by d2. 70 university students were assessed using d2 conventional paper-and-pencil and computational version. The experimental group and control group performed the task with or without target key, respectively. Continuous Performance Test (CPT) and N-back (1 and 2-back) tasks were used to measure sustained attention and working memory, respectively. Computational d2 performance was predicted by CPT (p < .05; R2 = .15) in the experimental group, and it was predicted by 2-back (p < .05; R2 = .28) in the control group. Conventional d2 performance was predicted by 2-back for both control group (p = .01; R2 = .20) and experimental group (p = .02, R2 = .17). Results suggest the involvement of working memory in d2, possibly a secondary construct assessed by this instrument.

Linking patterns of infant eye movements to a neural network model of the ventral stream using representational similarity analysis

Little is known about the development of higher-level areas of visual cortex during infancy, and even less is known about how the development of visually guided behavior is related to the different levels of the cortical processing hierarchy. As a first step toward filling these gaps, we used representational similarity analysis (RSA) to assess links between gaze patterns and a neural network model that captures key properties of the ventral visual processing stream. We recorded the eye movements of 4- to 12-month-old infants (N = 54) as they viewed photographs of scenes. For each infant, we calculated the similarity of the gaze patterns for each pair of photographs. We also analyzed the images using a convolutional neural network model in which the successive layers correspond approximately to the sequence of areas along the ventral stream. For each layer of the network, we calculated the similarity of the activation patterns for each pair of photographs, which was then compared with the infant gaze data. We found that the network layers corresponding to lower-level areas of visual cortex accounted for gaze patterns better in younger infants than in older infants, whereas the network layers corresponding to higher-level areas of visual cortex accounted for gaze patterns better in older infants than in younger infants. Thus, between 4 and 12 months, gaze becomes increasingly controlled by more abstract, higher-level representations. These results also demonstrate the feasibility of using RSA to link infant gaze behavior to neural network models. A video abstract of this article can be viewed at https://youtu.be/K5mF2Rw98Is.

Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review

The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual’s interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user’s attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.