Attention System Research Articles

Neuroelectrical and behavioral correlates of constructed action recognition in Finnish Sign language.

Neuroelectrical and behavioral correlates of constructed action recognition in Finnish Sign language.

A direct comparison of gaze-mediated orienting elicited by schematic and real human faces.

A direct comparison of gaze-mediated orienting elicited by schematic and real human faces.

Development of Student Classroom Attention System Using Artificial Intelligence and Electronic Sensors

Development of Student Classroom Attention System Using Artificial Intelligence and Electronic Sensors

Constructing government attention through policy synergy in China's haze governance.

Constructing government attention through policy synergy in China's haze governance.

Large-scale functional networks underlying visual attention.

Large-scale functional networks underlying visual attention.

Implementation of a Biologically Inspired Responsive Joint Attention System for a Social Robot

The concept of joint attention holds significant importance in human interaction and is pivotal in establishing rapport, understanding, and effective communication. Within social robotics, enhancing user perception and fostering a sense of natural interaction with robots becomes a central element. In this sense, emulating human‐centric qualities in social robots, such as joint attention, defined as the ability of two or more individuals to simultaneously focus on a common event, can increase their acceptability. This work introduces a responsive joint attention system integrated into the social robot Mini. The system calculates the position of the user's head, body, and face orientations in real‐time. With this information, the robot can perform natural movements based on the user's gaze and pointing directions. Moreover, the robot employs verbal expressions to alert the user when distractions are detected, enhancing the perception of the robot's responsiveness. This study also explores differences in user perception of the robot when the joint attention system is active. A user experiment involving an interactive gaming scenario is proposed. The results show significance in various social presence dimensions, validating the system and indicating that users discern when the joint attention system is active and perceive the robot's responsive behavior.

Hemifield Specificity of Attention Response Functions During Multiple Object Tracking.

The difficulty of tracking multiple moving objects among identical distractors increases with the number of tracked targets. Previous research has shown that the number of targets tracked (i.e., load) modulates activity in brain areas related to visuospatial attention, giving rise to so-called "Attention Response Functions" (ARFs). While the hemifield/hemispheric effects of spatial attention (e.g., hemispatial neglect, hemifield capacity limits) are well described, it had not previously been tested whether a hemispheric or hemifield imbalance exists among ARFs. By recording BOLD activity from human brains (n=19, female and male) in a multiple object tracking paradigm, we show that the number of tracked objects modulates activity in a large network of areas bilaterally. A significant effect of contralateral load was found in earlier areas throughout the dorsal and ventral visual streams, while the effects of ipsilateral load emerged in later areas. Both contra- and ipsilateral load significantly influenced activity in the parietal and frontal lobes, specifically the dorsal attention network. In addition, some brain regions in the occipital lobe were significantly more sensitive to contralateral than ipsilateral load. Our results are consistent with findings showing that a diverse set of brain areas contributes to tracking multiple targets. In particular, we extend the canonical view of load-based ARFs to include hemifield bias. Given the hemifield-specific nature of speed and capacity limits to multiple object tracking, we conjecture that areas that show a strong hemifield preference may impose a bottleneck on processing that results in limits on the capacity and speed of tracking.Significance Statement We investigated how attentional effort impacts brain activity. Effort (the number of targets in a multiple object tracking task) parametrically drives activity in the attention system. Our findings reveal brain areas where effort driven increases in activity are dependent on the visual hemifield where targets are tracked. We show that the load-dependent responses differ between earlier visual areas, which prefer targets on the contralateral side, and later areas that respond to targets anywhere in the visual field. This research challenges previous explanations of hemispatial neglect and enhances our understanding of how the brain manages spatial attention and mental effort. Additionally, we identify regions that might be the source of hemifield-specific capacity limits in attentional tracking.

A fusion method of infrared and visible images based on visual salience difference

Most infrared and visible image fusion methods use indiscriminate feature extraction strategies, which ignore the differences between multiple source images, and it is difficult to remove the interference of redundant information, such as noise according to visual habits, which has a negative impact on the fusion results. This paper proposes a fusion method of infrared and visible images based on visual saliency differences. Firstly, to highlight the target and suppress the background noise in the salient map, a visual attention system that conforms to the image fusion task is designed to accurately extract the salient visual features of the source image. Secondly, a salient feature adaptive fusion strategy is proposed based on image content and feature correlation. A saliency map is constructed based on visual salient features. Finally, based on the saliency map fusion strategy, the infrared and visible images are combined, and the texture details of weak activity areas are moderately enhanced. Experimental results show that the proposed method can effectively preserve the saliency of infrared targets, and the fusion results have uniform brightness distribution, clear edge outlines, and better visual effects.

Emotion Induction Modulates Neural Dynamics Related to the Originality of Ideational Creativity.

Emotions remarkably impact our creative minds; nevertheless, a comprehensive mapping of their underlying neural mechanisms remains elusive. Therefore, we examined the influence of emotion induction on ideational originality and its associated neural dynamics. Participants were randomly presented with three short videos with sad, neutral, and happy content. After each video, ideational originality was evaluated using the alternate uses task. Both happy and sad inductions significantly enhanced ideational originality relative to the neutral induction condition. However, no significant difference was observed in ideational originality between the happy and sad emotion inductions. Associated neural dynamics were assessed through EEG time-frequency (TF) power and phase-amplitude coupling (PAC) analyses. Our findings suggest that emotional states elicit distinct TF and PAC profiles associated with ideational originality. Relative to baseline, gamma activity was enhanced after the neutral induction and more enhanced after the induction of a happy emotion but reduced after the induction of sad emotion 2-4 s after starting the task. Our functional connectivity couplings suggest that inducing happy and sad emotions may influence the working memory and attentional system differently, leading to varying effects on associated processing modes. Inducing a happy emotion may result in decreased neural activity and processing of rich information in working memory for exploring more original ideas through cognitive flexibility. In contrast, inducing a sad emotion may enhance neural activity and increase coupling within the attention system to exploit and select fewer original ideas through cognitive persistence.

Adaptive modes of attention: Evidence from attentional networks.

Adaptive modes of attention: Evidence from attentional networks.

An event-related potential study of onset primacy in visual change detection

Onset primacy is a behavioural phenomenon whereby humans identify the appearance of an object (onset) with greater efficiency than other kinds of visual change, such as the disappearance of an object (offset). The default mode hypothesis explains this phenomenon by postulating that the attentional system is optimised for onset detection in its initial state. The present study extended this hypothesis by combining a change-detection task and measurement of the P300 event-related potential, which was thought to index the amount of processing resources available to detecting onsets and offsets. In an experiment, while brain activity was monitored by electroencephalography, participants indicated the locations of onsets and offsets under the condition in which they occurred equally often in the same locations across trials. Although there was no reason to prioritise detecting one type of change over the other, onsets were detected more quickly, and they evoked a larger P300 than offsets. These results suggest that processing resources are preferentially allocated to onset detection. This biased allocation may be a basis on which the attentional system defaults to the ‘onset detection’ mode.

A Smart Space Focus Enhancement System Based on Grey Wolf Algorithm Positioning and Generative Adversarial Networks for Database Augmentation

In the age of technological advancement, brainwave monitoring and attention tracking are critical for individual productivity and organizational efficiency. However, distractions pose significant challenges, making an effective brainwave monitoring and attention system essential. Generative Adversarial Networks (GANs) enhance medical datasets by synthesizing diverse samples. This paper explores their application in improving datasets for indoor positioning and brainwave monitoring-based attention tracking. The goal is to develop an intelligent lighting system that adjusts settings based on users’ brainwave states and positions. GANs enhance brainwave monitoring and positioning datasets, with Principal Component Analysis (PCA) applied for dimensionality reduction. machine learning and deep learning models train on these augmented datasets, enabling dynamic lighting adjustments to optimize user experience. GANs undergo parameter fine-tuning to improve dataset quality. Various classification models, including neural networks (NN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM), are used for brainwave monitoring, attention, and positioning. Fuzzy logic enhances system stability. The trained models are integrated with hardware components, such as the Raspberry Pi 4, to implement an “Indoor Positioning Deep Learning Brainwave Monitoring and Attention Monitoring System Based on the Grey Wolf Optimizer Algorithm”. Experimental results demonstrate a positioning accuracy of 15 cm and significant improvements in brainwave monitoring and attention tracking.

Paced Breathing Associated With Pupil Diameter Oscillations at the Same Rate and Reduced Lapses in Attention.

A dynamical systems model proposes that respiratory, locus coeruleus-noradrenaline (LC-NA), and cortical attentional systems interact, producing emergent states of attention. We tested a prediction that fixing respiratory pace (versus spontaneous respiration) stabilizes oscillations in pupil diameter (LC-NA proxy) and attentional state. Primary comparisons were between 'Instructed Breath' (IB) and 'No Instructed Breath' (NIB) groups. Secondarily, we investigated the effects of shifting respiratory frequency in the IB group from 0.15 to 0.1-0.15 Hz in Experiment 1 (n = 55) and 0.15-0.1 Hz only in Experiment 2 (n = 48) (replication). In the Paced Auditory Cue Entrainment (PACE) task, participants heard two auditory tones, alternating higher and lower pitches, cycling continuously. Tones acted as a breath guide for IB and an attention monitor for both groups. Participants gave rhythmic mouse responses to the transition points between tones (left for high-to-low, right for low-to-high). We derived accuracy of mouse click timing (RTm), variability in click timing (RTVL), and counts of erroneously inverting the left/right rhythm (IRs and Switches). Despite no differences between groups in RTm or RTVL, IB committed significantly fewer IRs and switches, indicating less lapses in attention during paced breathing. Differences in behavioral metrics were present across tone cycle frequencies but not exclusive to IB, so breath frequency did not appear to have a specific effect. Pupil diameter oscillations in IB closely tracked the frequency of the instructed breathing, implicating LC-NA activity as being entrained by the breath intervention. We conclude that pacing respiratory frequency did stabilize attention, possibly through stabilizing fluctuations in LC-NA.

Beyond the Score: Exploring the Associations Between Adverse Childhood Experiences and Electrophysiological Responses to Errors.

Adverse childhood experiences (ACEs) have diverse effects on physical development and mental health. This study aimed to clarify the relationship between the quantity of ACE exposure, type of ACE exposure, and subjective level of stress felt, correlated with event-related potential activity across the scalp, while controlling for relevant confounding variables. Fifty-three participants aged 18-32years completed questionnaires assessing their current mental health, self-regulation, childhood socioeconomic status, and history of traumatic events. Electroencephalographic activity was recorded while participants completed the Combined Attention Systems Task, a modified flanker task. Using cluster-corrected robust statistical approaches, significant relationships existed between the total number of ACEs, ACE type, the subjective impact of trauma, and amplitudes during the error-related negativity (ERN) and error positivity (Pe) at various scalp locations. In the ERN time window, greater error-correct differences were associated with greater total ACEs, abuse, and other ACEs at C5, P9, and TP10/C1 clusters, respectively. In addition, reduced error-correct differences at cluster-maximal C2 during the timing of the Pe were related to experiencing greater numbers of total ACEs while increased error-correct differences at cluster-maximal FPz during the timing of the Pe were associated with greater numbers of other ACEs. The subjective impact of total number of ACEs was not associated with error-correct differences, however, the subjective impact of household dysfunction, abuse, and 'other' ACE types were linked to error-correct differences at various scalp locations and timings. Notably, increased, rather than decreased, subjective impact of household dysfunction was related to greater error-correct differentiation during the timing of the ERN, maximal at Cz. These results suggest that both ACE type and subjective rating are relevant to future outcomes. The effects extended beyond the ERN-affecting error-related positivity and later event-related potentials-indicating associations with the number, type, and subjective impact of ACEs across a larger time window and scalp topography.

Eye movements follow the dynamic shifts of attention through serial order in verbal working memory

How are arbitrary sequences of verbal information retained and manipulated in working memory? Increasing evidence suggests that serial order in verbal WM is spatially coded and that spatial attention is involved in access and retrieval. Based on the idea that brain areas controlling spatial attention are also involved in oculomotor control, we used eye tracking to reveal how the spatial structure of serial order information is accessed in verbal working memory. In two experiments, participants memorized a sequence of auditory words in the correct order. While their eye movements were being measured, they named the memorized items in a self-determined order in Experiment 1 and in a cued order in Experiment 2. We tested the hypothesis that serial order in verbal working memory interacts with the spatial attention system whereby gaze patterns in visual space closely follow attentional shifts in the internal space of working memory. In both experiments, we found that the gaze shifts in visual space correlated with the spatial shifts of attention along the left-to-right one-dimensional mapping of serial order positions in verbal WM. These findings suggest that spatial attention is employed for dynamically searching through verbal WM and that eye movements reflect the spontaneous association of order and space even in the absence of visuospatial input.

Experience-induced plasticity in the attention system of healthy adults practising musical or non-musical activities

Experience-induced plasticity in the attention system of healthy adults practising musical or non-musical activities

Social Media Algorithms and Teen Addiction: Neurophysiological Impact and Ethical Considerations.

Does it matter how many hours we spend scrolling through Instagram? This article examines the neurobiological impact of prolonged social media use, focusing on how it affects the brain's reward, attention, and emotional regulation systems. Frequent engagement with social media platforms alters dopamine pathways, a critical component in reward processing, fostering dependency analogous to substance addiction. Furthermore, changes in brain activity within the prefrontal cortex and amygdala suggest increased emotional sensitivity and compromised decision-making abilities. The role of artificial intelligence (AI) in this process is significant. AI-driven social media algorithms are designed solely to capture our attention for profit without prioritizing ethical concerns, personalizing content, and enhancing user engagement by continuously tailoring feeds to individual preferences. These adaptive algorithms are designed to maximize screen time, thereby deepening the activation of the brain's reward centers. This cycle of optimized content and heightened engagement accelerates the development of addictive behaviors. The interplay between altered brain physiology and AI-driven content optimization creates a feedback loop that promotes social media addiction among teenagers. This raises significant ethical concerns regarding privacy and the promotion of personalized content. This review article offers a comprehensive and in-depth analysis of the neurophysiological impact of social media on adolescents and the moral concerns governing them. It also provides solutions for ethical social media use and preventing addiction among teenagers.

Forecasting Time Series Data using Recurrent Neural Networks: A Systematic Review

The method of time series forecasting stands crucial in multiple application areas that include finance as well as healthcare and energy management and climate modeling. RNNs serve as a powerful tool under deep learning because they possess ability to detect sequential data patterns while extracting temporal dependencies from time series data using traditional statistical methods which were previously the dominant approach. This paper conducts an organized review of modern techniques for predicting time series data by using RNNs. This discussion covers three major RNN architectures together with Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) as well as their combination with hybrid models. The paper examines how RNN-based models perform against traditional approaches before addressing RNN-based forecasting problems and suggesting potential research paths for the future. The analysis reviews multiple performance indicators utilized in past research to establish profound knowledge about RNN-based forecasting methods. The paper examines RNN benefits while analyzing the computational limitations and overfitting risks and interpretability problems that RNN systems encounter. The review investigates new frameworks including attention systems together with strengthening strategies and combination methods of statistical analysis with machine learning structures. Research outcomes demonstrate that RNN models particularly LSTM and GRU achieve great forecasting precision but future application research needs to optimize execution performance and advance interpretability capabilities of these models.

Feladatorientált figyelem és detektívregény

The detective novel is one of the most schematically structured genres, and therefore one that readers have high expectations of in terms of the narrative structure and the way in which the plot is constructed. Furthermore, readers anticipate the appearance of specific character types and plot elements. Additionally, they expect writers to deliberately direct their attention. This is achieved by focusing on seemingly insignificant details and distracting readers from important clues. This makes the mystery more challenging to solve and provides an exciting challenge. The reader's attention is thus trained to the peculiarities of the genre in a manner analogous to how it is directed towards a task: it focuses on the task at hand while avoiding events that are not pertinent to it. The principal argument of this study is that the detective novel employs genre-specific attentional control techniques that put the reader’s attentional system in a state of conflict: concentrated attention versus automatic perception, and exogenous versus endogenous attention are in constant tension with each other. In the initial section of the paper, I will delineate the task-oriented attentional functioning that is characteristic of detective fiction. Subsequently, through an analysis of Bence Hidas’s crime novel And the Snow Covers Everything, I will demonstrate how detective stories employ attentional narrative techniques and how they entice the reader into the trap of their own attentional functioning.

Long-term effects of concussion on attention, sensory gating and motor learning.

The current work aimed to understand the behavioral manifestations that result from disruptions to the selective facilitation of task-relevant sensory information at early cortical processing stages in those with a history of concussion. A total of 40 participants were recruited to participate in this study, with 25 in the concussion history group (Hx) and 15 in the control group (No-Hx). Somatosensory-evoked potentials (SEPs) were elicited via median nerve stimulation while subjects performed a task that manipulated their focus of attention toward or away from proprioceptive cues. Participants also completed an implicit motor sequence learning task relying solely on proprioceptive cues, as well as a visual attentional blink (AB) task to understand the effect of concussion on rapid shifts in attention. The Hx SEP data replicated past work showing an absence of relevancy-based facilitation at early cortical processing stages (N20-P27) that emerged at later processing stages. Our Hx showed evidence of relevancy-based facilitation at either the P50-N70 or the N70-P100. Performance on the learning task was not significantly different between the Hx and No-Hx. Performance on the AB task revealed greater AB magnitude in the Hx compared to the No-Hx. Collectively, these results suggest a compensatory strategy in the Hx that enables them to learn to the same degree as controls. However, when the attentional system is taxed with high temporal demands there are decrements in performance. These results are of particular importance given that these individuals are at an increased risk of sustaining subsequent concussions, and musculoskeletal injuries.