Enhance Information Processing Research Articles

The Role of Information Processing as an Underlying Mechanism in the Experience of Anxiety Reactivity and Anxiety Perseveration, Two Dissociable Dimensions of Trait Anxiety

Abstract Purpose While long considered a unitary dimension, research has moved towards a multidimensional understanding of trait anxiety, and has identified two dissociable dimensions of trait anxiety: anxiety reactivity and anxiety perseveration. Despite the consolidation of this understanding over the past decade, the mechanisms underlying these two dimensions are not well established. The present study examined the novel role of information processing in the experience of anxiety reactivity and perseveration, specifically, how enhanced processing of situational information influences reactivity, and how enhanced processing of implicational information influences perseveration. Method Undergraduate university students (N = 142) were exposed to an interview-based stressor. As participants approached this stressor, they were provided with information reflecting one of the two content categories, intended to encourage a situational or implicational processing mode. Anxiety reactivity and anxiety perseveration were measured by assessing changes in state anxiety (measured using a single item visual analogue state anxiety measure) in the lead up and wake of this stressor. Results Mixed-design ANOVAs did not reveal a significant relationship between enhanced processing of situational information and anxiety reactivity, nor between enhanced processing of implicational information and anxiety perseveration. An experimental manipulation involving valence revealed that receiving positive information reduced the intensity of state anxiety elevations in the aftermath of the information processing task. Conclusions Findings are discussed with reference to limitations and future extensions that could investigate the potential role of cognitive biases in this proposed relationship between enhanced information processing and anxiety reactivity and perseveration.

Cooperative co-evolution deep echo state network for time series prediction

Abstract The deep echo state network demonstrates strong predictive performance in time series tasks, but inter-layer weight optimization and reservoir structure design are still challenging tasks. To address this, we propose the cooperative co-evolutionary deep echo state network (CCEDESN). Firstly, inspired by the hierarchical structure of the brain, we introduce a modular hierarchical design. Each layer of the reservoir contains multiple sub-reservoirs, with central neurons in each sub-reservoir interacting with those in other sub-reservoirs to enhance information processing. Secondly, an advanced cooperative co-evolutionary algorithm is proposed to simultaneously optimize the layer connection weights and hyperparameters of the deep echo state network. We evaluate the CCEDESN model on the Mackey-Glass system, sunspot data, and Apple stock opening prices. The experimental results show that the CCEDESN model achieves superior prediction accuracy compared to other models.

Exploring the Influence of Mood Triggers on Information Encountering Among Chinese Youth on Mobile Social Media

Noticing is an important stage in information encountering when the user becomes aware of the information. Despite the large amount of research confirming that different information behaviors affect users' moods, there is still a dearth of research on the noticing of information encountered in different moods. This study surveyed 252 Chinese youth using fsQCA to explore how communication processes on mobile social media platforms trigger young users to notice information encountering different moods. This study finds that there are significant differences among moods: In a positive mood, users are more receptive to communication cues such as the perceived usefulness and ease-of-use of the transmitted information, indicating that positive moods may enhance information processing. In a neutral mood, the focus shifts towards the information interesting, suggesting that the user’s mood moderates the reception of communicative content on mobile social platforms. In a negative mood, users’ attention to information is more heavily influenced by social cues and peer communication, suggesting that social influence plays a vital role. Negative moods may prompt users to rely more on the opinions and behaviors of their social network, reinforcing the role of social dynamics in mobile communication. This study offers valuable insights into the relationship between mood and information encountering on mobile social media, providing communication practitioners with actionable strategies for tailoring content based on users' moods. By understanding how mood influences information reception and processing, communicators can enhance message delivery and audience engagement.

Adaptive Cross-Modal Experts Network with Uncertainty-Driven Fusion for Vision–Language Navigation

Vision-and-Language Navigation (VLN) enables an agent to autonomously navigate in real-world environments based on language instructions to reach specified destinations and accurately locate relevant targets. Although significant progress has been made in recent years, two major limitations remain: (1) Existing methods lack flexibility and diversity in processing multimodal information and cannot dynamically adjust to different input features. (2) Current fixed fusion strategies fail to dynamically adapt to varying data quality in open environments, insufficiently leveraging multi-scale features and handling complex nonlinear relationships. In this paper, an adaptive cross-modal experts network (ACME) with uncertainty-driven fusion is proposed to address these issues. The adaptive cross-modal experts module dynamically selects the most suitable expert network based on the input features, enhancing information processing diversity and flexibility. Additionally, the uncertainty-driven fusion module balances coarse-grained and fine-grained information by calculating their confidences and dynamically adjusting the fusion weights. Comprehensive experiments on the R2R, SOON, and REVERIE datasets demonstrate that our approach significantly outperforms existing VLN approaches.

The effectiveness of transcranial direct current stimulation (tDCS) on information processing speed in children with intellectual disability

PurposeThe speed of information processing is known to play an important role in mental disorders; This study investigated the effect of the transcranial direct current stimulation on information processing speed in children with intellectual disabilities. MethodAn experimental design with a pretest- posttest control group was used. The study population consisted of 8- to 14-year-old children with intellectual disabilities attending special primary schools in Urmia, Iran. 36 children were randomly assigned to the experimental and control groups (18 for each group). The experimental group received 10 sessions of tDCS with anode positioned over the F3 electrode (left DLPFC) and cathode over F4 (right DLPFC) The control group did not receive any intervention. Trail Making Test (TMT) was used to assess information processing speed. Data were analyzed using MANCOVA. ResultsThe results revealed that tDCS led to a significant increase in information processing speed in the experimental group compared to the control group. ConclusionThis study demonstrates that tDCS applied to the dorsolateral prefrontal cortex (DLPFC) is effective in enhancing information processing speed in children with intellectual disabilities. These finding have important implications for the development of interventions aimed at improving cognitive functioning in children with intellectual disabilities.

The Role of Multi-sensory Learning Activities on Iraqi Preparatory School Pupils' Communication

The research aims to identify how multi-sensory learning activities affect the communication growth of Iraqi preparatory school pupils as well as how those pupils' communication skills evolve in the classroom. Multi-sensory learning activities engage multiple senses (visual, auditory, kinesthetic, tactile) to enhance information processing and retention. By incorporating diverse sensory stimuli, these activities create rich and engaging learning experiences that cater to various learning styles. The research is hypothetical in that, in the posttest for Visual Learning Pupils, posttest for Auditory Learning Pupils, posttest for Kinesthetic Learning Pupils, and posttest for Tactile Learning Pupils communication test, The mean scores of the experimental groups and the control group differ statistically significantly. In the current research, 100 fifth-stage pupils were chosen during the academic year 2023–2024 from Aghalar Preparatory School for Boys. Every group is equal in terms of age and parents' level of education. The researcher statistically analyzed the data collected from the four post-test outcomes using one-way, two-way, three-way, and four-way ANOVA (analysis of variance) to gauge the pupils' post-test communication. Multi-sensory learning activities are more successful than using the traditional approach for communication, The results demonstrate a statistically significant difference in mean scores between the experimental groups and the control group. Concluding remarks, suggestions for more research, and recommendations complete the research.

Agents seeking long-term access to the wisdom of the crowd reduce immediate decision-making accuracy.

Living in groups offers social animals the significant advantage of accessing collective wisdom and enhanced information processing, enabling more accurate decisions related to foraging, navigation and habitat selection. Preserving group membership is crucial for sustaining access to collective wisdom, incentivizing animals to prioritize group cohesion. However, when individuals encounter divergent information about the quality of various options, this can create a conflict between pursuing immediate rewards and the maintenance of group membership to improve access to future pay-offs. In this study, I show that rational agents who seek to maximize long-term rewards will be more inclined to follow the decisions of their peers than those with short-term horizons. In doing so, they necessarily make less-rewarding decisions in the short-term, which manifests in a lower individual accuracy when choosing the better of two options. Furthermore, I demonstrate that intuitions about collective wisdom can be misleading in groups of agents who prioritize long-term rewards, with disagreement being a better signal for the accuracy of collective choices than consensus. These results demonstrate that observed patterns of sociality should be interpreted in the context of the life history of an individual and its peers, rather than through the lens of an isolated decision.This article is part of the discussion meeting issue 'Understanding age and society using natural populations'.

Recurrence resonance - noise-enhanced dynamics in recurrent neural networks

Understanding how neural networks process information is a fundamental challenge in neuroscience and artificial intelligence. A pivotal question in this context is how external stimuli, particularly noise, influence the dynamics and information flow within these networks. Traditionally, noise is perceived as a hindrance to information processing, introducing randomness and diminishing the fidelity of neural signals. However, distinguishing noise from structured input uncovers a paradoxical insight: under specific conditions, noise can actually enhance information processing. This intriguing possibility prompts a deeper investigation into the nuanced role of noise within neural networks. In specific motifs of three recurrently connected neurons with probabilistic response, the spontaneous information flux, defined as the mutual information between subsequent states, has been shown to increase by adding ongoing white noise of some optimal strength to each of the neurons. However, the precise conditions for and mechanisms of this phenomenon called ‘recurrence resonance’ (RR) remain largely unexplored. Using Boltzmann machines of different sizes and with various types of weight matrices, we show that RR can generally occur when a system has multiple dynamical attractors, but is trapped in one or a few of them. In probabilistic networks, the phenomenon is bound to a suitable observation time scale, as the system could autonomously access its entire attractor landscape even without the help of external noise, given enough time. Yet, even in large systems, where time scales for observing RR in the full network become too long, the resonance can still be detected in small subsets of neurons. Finally, we show that short noise pulses can be used to transfer recurrent neural networks, both probabilistic and deterministic, between their dynamical attractors. Our results are relevant to the fields of reservoir computing and neuroscience, where controlled noise may turn out a key factor for efficient information processing leading to more robust and adaptable systems.

Enhancing motor skill learning through multiple sessions of online high-definition transcranial direct current stimulation in healthy adults: insights from EEG power spectrum.

The purpose of this study was to evaluate the influence of high-definition transcranial direct current stimulation (HD-tDCS) on finger motor skill acquisition. Thirty-one healthy adult males were randomly assigned to one of three groups: online HD-tDCS (administered during motor skill learning), offline HD-tDCS (delivered before motor skill learning), and a sham group. Participants engaged in a visual isometric pinch task for three consecutive days. Overall motor skill learning and speed-accuracy tradeoff function were used to evaluate the efficacy of tDCS. Electroencephalography was recorded and power spectral density was calculated. Both online and offline HD-tDCS total motor skill acquisition was significantly higher than the sham group (P < 0.001 and P < 0.05, respectively). Motor skill acquisition in the online group was higher than offline (P = 0.132, Cohen's d =1.46). Speed-accuracy tradeoff function in the online group was higher than both offline and sham groups in the post-test. The online group exhibited significantly lower electroencephalography activity in the frontal, fronto-central, and centro-parietal alpha band regions compared to the sham (P < 0.05). The findings suggest that HD-tDCS application can boost finger motor skill acquisition, with online HD-tDCS displaying superior facilitation. Furthermore, online HD-tDCS reduces the power of alpha rhythms during motor skill execution, enhancing information processing and skill learning efficiency.

Impact of AI Integration on Journalists' Mental Health: A Quantitative Study.

The field of journalism has undergone substantial transformation with the integration of artificial intelligence (AI), leveraging technologies like natural language processing and automated reporting. These advancements enhance information processing speed, enable personalised content delivery and improve data analysis capabilities, thereby reshaping journalism practices. Despite the benefits AI offers, concerns persist regarding its impact on job security and the mental health of journalists. Rapid technological changes can lead to increased job insecurity, altered job roles and heightened pressure to adapt, potentially affecting journalists' mental well-being. This study utilises the Depression, Anxiety, and Stress Scale (DASS-21) to assess levels of depression, anxiety and stress among 500 journalists from various media organisations that have integrated AI technologies. Quantitative data analysis explores the relationship between AI integration and mental health outcomes. The findings indicate significant correlations between the perceived threat of AI replacing jobs and higher levels of depression among journalists. Mixed effects were observed regarding the impact of AI integration on job roles, with associations found between AI integration and both increased depression and reduced stress levels. AI integration in journalism presents both opportunities and challenges for journalists' mental health. Strategies to address job security concerns, enhance comfort with AI tools through training and establish mental health support systems are crucial for fostering a supportive environment in AI-driven newsrooms.

Advancing in RGB-D Salient Object Detection: A Survey

The human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent regions or objects in images or videos. This reduces the amount of data needed to process while enhancing the accuracy and efficiency of information extraction. In recent years, SOD has made significant progress in many areas such as deep learning, multi-modal fusion, and attention mechanisms. Additionally, it has expanded in real-time detection, weakly supervised learning, and cross-domain applications. Depth images can provide three-dimensional structural information of a scene, aiding in a more accurate understanding of object shapes and distances. In SOD tasks, depth images enhance detection accuracy and robustness by providing additional geometric information. This additional information is particularly crucial in complex scenes and occlusion situations. This survey reviews the substantial advancements in the field of RGB-Depth SOD, with a focus on the critical roles played by attention mechanisms and cross-modal fusion methods. It summarizes the existing literature, provides a brief overview of mainstream datasets and evaluation metrics, and quantitatively compares the discussed models.

Compact multichannel reconfigurable mode demultiplexer enabled by phase change material

The reconfigurable mode demultiplexer is a crucial component for flexibly routing modes into different channels in on-chip multimode photonic systems with enhanced information processing capabilities. In this paper, we present a multichannel reconfigurable mode demultiplexer enabled by ultralow-loss phase-changing Sb2Se3. By harnessing the phase-change-mediated mode coupling in asymmetric directional couplers (ADCs), one or more of the higher-order modes including TE1, TE2 and TE3 modes could be selectively dropped from the bus waveguide with low losses. With an optimized ADCs structure, the proposed mode demultiplexer demonstrates insertion loss less than 0.227 dB in the ON (amorphous) state and the extinction ratios large than 23.28 dB over the C-band. By coupling the access waveguides of the higher-order mode in parallel on both sides of the bus waveguide, the device size can be compact with a footprint of ∼ 7 × 75 µm2, and this design approach can be further extended to enable more higher-order mode multiplexing.

The MMI-HCPS method: Integrating perception-decision-control capabilities of multiple mine inspectors

Due to the complex mining environment, the development of equipment intelligence has stagnated, with challenges in the application of key technologies. Additionally, underground inspectors exhibit a lower level of intelligence in monitoring, decision-making, and control aspects. This paper proposes an MMI-HCPS (Multiple Mine Inspectors- Human-Cyber-Physical System) method that integrates the perception, decision, and control capabilities of multiple mine inspectors. Firstly, it reviews the current status of equipment intelligentization in fully mechanized mining faces to highlight the necessity of equipment intelligentization. Secondly, the paper establishes the theoretical framework MMI-HCPS and further refines the overall structure of the entire document based on MMI-HCPS, composed of high-performance multi-site and multi-device communication network (MCN), intelligent inference system (IIS), inspection-human-system (IHS), and center-human-system (CHS). MCN forms the foundation for the overall architecture to operate smoothly, while IIS, IHS, and CHS constitute the core components driving the operation of MMI-HCPS. Both IIS and CHS have their own monitoring and control subsystems, and their decision subsystems are in a loop with IIS, achieving closed-loop inference. Finally, a utilizing the laboratory environment, separate tests were conducted on the network operation status of MCN, monitoring and control capabilities of IHS and CHS, experimental validation of decision-making abilities of multiple inspectors, and ultimately, an experiment was undertaken to assess the overall inspection process. The results indicate that promoting the intelligence of inspectors in the fully mechanized mining face from an intelligent perspective is feasible with this method. The method achieves a shared perception of the fully mechanized mining face among different inspectors, complementing each other’s strengths and weaknesses, enhancing information processing efficiency, and improving individual work efficiency. Furthermore, the method enables an interactive loop between human, computers, and the physical mining face, providing support for further intelligent development of mining equipment.

Activation of M1 cholinergic receptors in mouse somatosensory cortex enhances information processing and detection behaviour

To optimise sensory representations based on environmental demands, the activity of cortical neurons is regulated by neuromodulators such as Acetylcholine (ACh). ACh is implicated in cognitive functions including attention, arousal and sleep cycles. However, it is not clear how specific ACh receptors shape the activity of cortical neurons in response to sensory stimuli. Here, we investigate the role of a densely expressed muscarinic ACh receptor M1 in information processing in the mouse primary somatosensory cortex and its influence on the animal’s sensitivity to detect vibrotactile stimuli. We show that M1 activation results in faster and more reliable neuronal responses, manifested by a significant reduction in response latencies and the trial-to-trial variability. At the population level, M1 activation reduces the network synchrony, and thus enhances the capacity of cortical neurons in conveying sensory information. Consistent with the neuronal findings, we show that M1 activation significantly improves performances in a vibriotactile detection task.

Design and Implementation of an IIoT Driven Information System: A Case Study

AbstractInformation systems are critical for companies since they offer quick and easy access to complex and significant data in a structured manner to make informed and effective business decisions. Hence, the objective of this study is to conceptualize and implement an innovative information system in the case study organization. The study identified the requirements for Organizing Vision Theory (OVT) and developed architecture based on Organizational Information Processing Theory (OIPT). This architecture is designed and developed using the Industrial Internet of Things (IIoT) to support a self-organizing vision and enhanced information processing. The study’s contribution lies in developing and executing an integrative architecture of IIoT-driven information systems from the lenses of OVT and OIPT. Further, this study contributes by mapping OVT elements (such as transparency, continuity, and coherence) and OIPT elements (information processing needs and capabilities) to drive value and knowledge through a robust architecture of IIoT-driven information systems. The study also highlights the contribution of IIoT-based information systems to a new knowledge system, facilitating better decision-making by professionals.

The association between managerial short-termism and green innovation: the moderating role of firms’ digital transformation

Purpose The purpose of this study is to examine the impact of managerial short-termism on green innovation of firms and the moderating role of digital transformation of enterprises in the association between managerial short-termism and green innovation. Design/methodology/approach This study uses data from Chinese A-share listed companies from 2001 to 2021 and employ panel fixed model and moderating effect model to examine the impact of managerial short-termism on green innovation of firms and the moderating role of digital transformation of enterprises in the association between managerial short-termism and green innovation. Findings The findings of this study reveal that managerial short-termism exerts negative influence on green innovation. Digital transformation enables firms to reduce the adverse effect of managerial short-termism on green innovation because digital transformation enhances information processing ability and then improves internal corporate governance and analyst coverage. Moreover, the moderating role of digital transformation is more prominent for firms with lower internal corporate governance, for firms with less analyst coverage and for non-state-owned enterprises. Originality/value This paper intends to address the following two questions: what is the impact of managerial short-termism on green innovation and what is the role of digital transformation in the two variables’ association? By using data of Chinese A-share listed companies from 2001 to 2021 and developing two individual indexes to measure managerial short-termism and digital transformation, the authors empirically test these above two questions. The results of this study indicate that: First, drawn on time-oriented theory and upper echelon theory, managerial short-termism has an adverse effect on firms’ green innovation. Second, digital transformation enables firms to reduce the negative effect of managerial short-termism on green innovation. Furthermore, the moderating mechanism tests show that the corporate governance effects of digital transformation play a supervisory role that impels managers to reduce short-term investments and promote firms’ green R&amp;D investments, which helps to reduce the negative effect of managerial short-termism on green innovation. Additionally, the heterogeneity checks show that the moderating role of digital transformation in the relation between managerial short-termism and green innovation is more prominent for firms with lower internal corporate governance, with less analyst coverage and for non-state-owned enterprises.

A Novel Web-Based eMemo for Tertiary Institutions in Developing Nations: From Conceptualization to Implementation

The new need to move to an online mode of administration has necessitated the adoption of Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS). The conventional paper-based administrative memorandum is relatively costly, requires manpower, insecure and non-confidential, prone to unauthorized access and has a relatively slow cycle time; among others. An electronic memo (e-memo) can save cost, offer high speed of processing, reliability and security. The aim of this work was to conceptualize, design and develop a novel semantic web-based memo platform that allows for handling memos within an organization. The tools used include HTML, CSS, PHP, JavaScript, jQuery and AJAX, MySQL. The developed system was tested in three stages. Testing was done in the Department of Computer Science of the institution of case study, then in the Faculty of Science and finally in the entire Lagos State University. Agile development method was used in the development process. The designed system was hosted on a cloud-server and a total of 43 test cases were considered and an average of four minutes was recorded for a complete memo cycle. eMemo allows users to generate, sign, treat and memo electronically. It is such a useful tool that will enhance information processing in a faster, standardized and more secure manner.

Effects of multisession cathodal transcranial direct current stimulation with cognitive training on sociocognitive functioning and brain dynamics in autism: A double-blind, sham-controlled, randomized EEG study

BackgroundFew treatment options are available for targeting core symptoms of autism spectrum disorder (ASD). The development of treatments that target common neural circuit dysfunctions caused by known genetic defects, namely, disruption of the excitation/inhibition (E/I) balance, is promising. Transcranial direct current stimulation (tDCS) is capable of modulating the E/I balance in healthy individuals, yet its clinical and neurobiological effects in ASD remain elusive. ObjectiveThis double-blind, randomized, sham-controlled trial investigated the effects of multisession cathodal prefrontal tDCS coupled with online cognitive remediation on social functioning, information processing efficiency and the E/I balance in ASD patients aged 14–21 years. MethodsSixty individuals were randomly assigned to receive either active or sham tDCS (10 sessions in total, 20 min/session, stimulation intensity: 1.5 mA, cathode: F3, anode: Fp2, size of electrodes: 25 cm2) combined with 20 min of online cognitive remediation. Social functioning, information processing efficiency during cognitive tasks, and theta- and gamma-band E/I balance were measured one day before and after the treatment. ResultsCompared to sham tDCS, active cathodal tDCS was effective in enhancing overall social functioning [F(1, 58) = 6.79, p = .012, ηp2 = 0.105, 90% CI: (0.013, 0.234)] and information processing efficiency during cognitive tasks [F(1, 58) = 10.07, p = .002, ηp2 = 0.148, 90% CI: (0.034, 0.284)] in these individuals. Electroencephalography data showed that this cathodal tDCS protocol was effective in reducing the theta-band E/I ratio of the cortical midline structures [F(1, 58) = 4.65, p = .035, ηp2 = 0.074, 90% CI: (0.010, 0.150)] and that this reduction significantly predicted information processing efficiency enhancement (b = −2.546, 95% BCa CI: [-4.979, −0.113], p = .041). ConclusionOur results support the use of multisession cathodal tDCS over the left dorsolateral prefrontal cortex combined with online cognitive remediation for reducing the elevated theta-band E/I ratio in sociocognitive information processing circuits in ASD patients, resulting in more adaptive regulation of global brain dynamics that is associated with enhanced information processing efficiency after the intervention.

Context-dependent attentional spotlight in pulvinar-V1 interaction

Spatial attention is often described as a mental spotlight that enhances information processing at the attended location. Using fMRI, we investigated background connectivity between the pulvinar and V1 in relation to focused versus diffused attention allocation, in weak and strong crowding contexts. Our findings revealed that focused attention led to enhanced correlations between the pulvinar and V1. Notably, this modulation was initiated by the pulvinar, and the strength of the modulation was dependent on the saliency of the target. These findings suggest that the pulvinar initiates information reweighting to V1, which underlies attentional selection in cluttered scenes.

Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems

The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems.