Information Processing Capacity Research Articles

RsfMRI-based brain entropy is negatively correlated with gray matter volume and surface area.

The brain entropy (BEN) reflects the randomness of brain activity and is inversely related to its temporal coherence. In recent years, BEN has been found to be associated with a number of neurocognitive, biological, and sociodemographic variables such as fluid intelligence, age, sex, and education. However, evidence regarding the potential relationship between BEN and brain structure is still lacking. In this study, we use resting-state fMRI (rsfMRI) data to estimate BEN and investigate its associations with three structural brain metrics: gray matter volume (GMV), surface area (SA), and cortical thickness (CT). We performed separate analyses on BEN maps derived from four distinct rsfMRI runs, and used a voxelwise as well as a regions-of-interest (ROIs) approach. Our findings consistently showed that lower BEN was related to increased GMV and SA in the lateral frontal and temporal lobes, inferior parietal lobules, and precuneus. We hypothesize that lower BEN and higher SA might reflect higher brain reserve as well as increased information processing capacity.

Asymmetrically connected reservoir networks learn better

We show that connectivity within the high-dimensional recurrent layer of a reservoir network is crucial for its performance. To this end, we systematically investigate the impact of network connectivity on its performance, i.e., we examine the symmetry and structure of the reservoir in relation to its computational power. Reservoirs with random and asymmetric connections are found to perform better for an exemplary Mackey-Glass time series than all structured reservoirs, including biologically inspired connectivities, such as small-world topologies. This result is quantified by the information processing capacity of the different network topologies which becomes highest for asymmetric and randomly connected networks. Published by the American Physical Society 2025

An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models.

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

Model of acceptance and use of social customer relationship management in micro and small enterprises

Objective of the study: The article aims to propose and test a Social Customer Relationship Management Acceptance and Usage Model that considers antecedent factors in micro and small enterprises. Methodology/approach: A survey analyzed through structural equation modeling was conducted with managers of micro and small Brazilian companies. Originality/Relevance: The use of social networks as a source of the process of monitoring, analysis and content generation for business strategy is recent and very relevant for the sustainability of micro and small businesses. Main results: Although some hypotheses were not validated, the study fills important gaps in empirical research on micro and small businesses. Replicating this study in different cultural contexts could offer further insights. Theoretical/methodological contributions: The positive results indicate that, despite some hypotheses being rejected, the study confirms the relevance of "Customer Information Processing Capacity" and its influence on the "Use of Social CRM," highlighting the importance of social interactions and engagement capacity for value co-creation in micro and small businesses. Socia /management contributions: Managers can leverage the relationship between the “Ability to Engage the Customer Via Social Media” and “Social Influence” to enhance their social media strategies and improve customer engagement.

Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that male and female mice achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals. In support of this hypothesis, we find that policy complexity suppresses midbrain dopamine responses to reward outcomes. Furthermore, neural and behavioral reward sensitivity were positively correlated across sessions. Our results suggest that policy compression shapes basic mechanisms of reinforcement learning in the brain.Significance statement Decision making relies on memory to store information about which actions to produce in which situations. This memory has limited capacity, which means that some information will be lost. The signatures of this information loss can be found in patterns of behavioral bias and randomness. However, relatively little is known about the neural mechanisms which ensure that actions achieve the highest possible reward given the limited capacity of decision memory. In this paper, we show that the neuromodulator dopamine is sensitive to the costs of memory, as predicted by a computational model of capacity-limited learning.

A Legal-Dogmatic View on Freedom of Expression in Social Networks

The freedom of the press and the right to free speech also apply in the digital sphere. Every citizen with Internet access has the ability to easily share and receive information and opinions with a global audience. The hope is that unfiltered free expression and diversity of opinion will lead to more democratic and pluralistic societies. This utopia, however, fails due to humans’ limited information processing capacity. No one is able to take note of all or even a fraction of the opinions and information presented on the Internet, process them and use them as a basis for their own opinions and actions. As a result, the Internet has been “platformized”. Platforms bring together various user groups and frequently create a closed ecosystem of various services and information for them. The purpose of this paper is to provide answers on questions, among other, what lies behind social networks function as information intermediaries and what rights social media users have against the deletion of their posts.

Large language models surpass human experts in predicting neuroscience results.

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs indicated high confidence in their predictions, their responses were more likely to be correct, which presages a future where LLMs assist humans in making discoveries. Our approach is not neuroscience specific and is transferable to other knowledge-intensive endeavours.

Numerical Simulations and Bifurcation of Ca2+ Oscillatory Behaviour in the Connection of Neurons and Astrocytes.

Extensive research has demonstrated that astrocytes actively participate in the regulation of synaptic communication. To examine the dynamic behavior of the model, a neuron-astrocyte model has been solved, and a bifurcation analysis has been performed. This paper uses the equilibrium point, stability theory, and the center manifold theorem to theoretically investigate the dynamical analysis of Ca2+ oscillations in the cytosol. The connections at tripartite synapses between the cells have been modeled using IP3 and 2-AG. A mathematical model is used to depict the overall framework of bifurcation and induced Ca2+ dynamics. The differences in the presence and disappearance of Ca2+ oscillations are partially explained by two subcritical Hopf bifurcation points, according to the results. Communication between the cells occurs through the oscillations of Ca2+ concentration. Furthermore, numerical simulations are conducted to confirm the efficacy of the suggested approach. Thus, our findings imply that neuron-astrocyte crosstalk plays a fundamental role in generating a variety of neuronal activities, thereby improving the brain's capacity for information processing.

Role of coherence in many-body Quantum Reservoir Computing

Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state exploration. Yet, the relation between the performance of reservoirs based on complex and many-body quantum systems and non-classical state features is not established. Through an extensive analysis of QRC based on a transverse-field Ising model we show how different quantum effects, such as quantum coherence and correlations, contribute to improving the performance in temporal tasks, as measured by the Information Processing Capacity. Additionally, we critically assess the impact of finite measurement resources and noise on the reservoir’s dynamics in different regimes, quantifying the limited ability to exploit quantum effects for increasing damping and noise strengths. Our results reveal a monotonic relationship between reservoir performance and coherence, along with the importance of quantum effects in the ergodic regime.

Simulated synapse loss induces depression-like behaviors in deep reinforcement learning.

Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network-simulating the effect of dendritic spine loss as observed in major depressive disorder (MDD). Surprisingly, this simulated spine loss is sufficient to induce a variety of MDD-like behaviors in the artificially intelligent agent, including anhedonia, increased temporal discounting, avoidance, and an altered exploration/exploitation balance. Furthermore, simulating alternative and longstanding reward-processing-centric conceptions of MDD (dysfunction of the dopamine system, altered reward discounting, context-dependent learning rates, increased exploration) does not produce the same range of MDD-like behaviors. These results support a conceptual model of MDD as a reduction of brain connectivity (and thus information-processing capacity) rather than an imbalance in monoamines-though the computational model suggests a possible explanation for the dysfunction of dopamine systems in MDD. Reversing the spine-loss effect in our computational MDD model can lead to rescue of rewarding behavior under some conditions. This supports the search for treatments that increase plasticity and synaptogenesis, and the model suggests some implications for their effective administration.

Sustainability Service Chain Capabilities in the Oil and Gas Industry: A Fuzzy Hybrid Approach SWARA-MABAC

This study aims to redefine the capabilities of the service supply chain in the Iranian oil and gas industry, where the concept of service chains has remained underdeveloped due to a traditional focus on conventional service chains. It seeks to compare the capabilities of the oil and gas service supply chain to those in other sectors and explore whether applied research is necessary to improve service chain performance. The study was conducted in three stages, involving ten petroleum and natural gas experts. The first stage focused on identifying key capabilities, yielding seven significant capabilities and 28 critical sub-capabilities. In the second stage, the SWARA fuzzy hybrid approach was employed to weigh and prioritize these capabilities, while the fuzzy MABAC methodology was used for strategic decision-making. Finally, the model’s sensitivity was assessed using fuzzy methods to validate the findings. The results highlight the highest priority capability for service providers: the ability to utilize information for updating information processing capacity in making decisions within the service supply chain. The selected location, G1, emerged as a key area of focus. This approach presents a novel method for optimizing service supply chain locations within the oil and gas sector. This paper introduces a unique approach to selecting optimal service supply chain locations in the oil and gas industry, addressing critical gaps in previous research. Employing advanced fuzzy hybrid methodologies provides valuable insights into improving service chain capabilities, offering a competitive advantage for companies operating in this sector.

Ambiguity, information processing, and financial intermediation

This paper incorporates ambiguity and information processing constraints into the He and Krishnamurthy (2012) model of intermediary asset pricing. Financial intermediaries possess greater information processing capacity than households. In response, households optimally choose to delegate their investment decisions. The contractual relationship between households and intermediaries is subject to a moral hazard friction, which results in a financial constraint. We show that ambiguity aversion not only amplifies households' incentives to delegate but also tightens the financial constraint. The calibrated model can quantitatively explain both the unconditional and time-varying moments of observed asset prices while endogenously generating an empirically consistent crisis frequency.

Relationship between subjective cognitive functioning and fluid and crystallized cognitive abilities in bipolar disorder.

People with bipolar disorder (BD) often show inaccurate subjective ratings of their objective cognitive function. However, it is unclear what information individuals use to formulate their subjective ratings. This study evaluated whether people with BD are likely using information about their crystallized cognitive abilities (which involve an accumulated store of verbal knowledge and skills and are typically preserved in BD) or their fluid cognitive abilities (which involve the capacity for new learning and information processing in novel situations and are typically impaired in BD) to formulate their subjective cognitive ratings. Eighty participants diagnosed with BD and 55 control volunteers were administered cognitive tests assessing crystallized and fluid cognitive abilities. Subjective cognitive functioning was assessed with the Cognitive Failures Questionnaire (CFQ), daily functioning was rated using the Multidimensional Scale of Independent Functioning (MSIF) and the Global Assessment of Functioning Scale (GAF), and quality of life was assessed with the Quality of Life in Bipolar Disorder scale (QoL.BD). The BD group exhibited considerably elevated subjective cognitive complaints relative to controls. Among participants with BD, CFQ scores were associated with fluid cognitive abilities including measures of memory and executive function, but not to crystallized abilities. After controlling for objective cognition and depression, higher cognitive complaints predicted poorer psychosocial outcomes. Cognitive self-reports in BD may represent a metacognitive difficulty whereby cognitive self-appraisals are distorted by a person's focus on their cognitive weaknesses rather than strengths. Moreover, negative cognitive self-assessments are associated with poorer daily functioning and diminished quality of life.

The Changing Definition Of Human Nature From Traditional to Contemporary Approaches: A View Of Human Nature In The Philosophy Of Informatıon

The issue of what human nature is an important issue for understanding the present and building the future. The place of human beings in the universe as well as their duty, their relationship with society, and their environment has been shaped by the meaning attributed to human nature. This article examines the transformation that the understanding of human nature in traditional philosophy has undergone after the information revolution from the perspective of information philosophy. Information technologies, which have integrated into every area of our lives after the information revolution, has deeply affected our scientific, economic, intellectual, and cultural activities. With the information revolution, human nature has been redefined with its characteristics that participate in design and creation activities, thanks to its interaction abilities, which are information-processing capacities, beyond biological and spiritual characteristics. The understanding of human nature has been addressed in different historical periods since the mythological period. The subject here is to evaluate the understanding of human nature from the traditional to the present. The anthropocentric understanding of humans, traditionally defined in the context of biological, physiological, and spiritual characteristics, is defined in the philosophy of information in a holistic context as an agent with information processing capacity, an informational object. As an entity that carries and processes information, humans are evaluated in the same category as information technologies such as cybernetic systems and artificial intelligence, although they have different characteristics, as informational objects. In addition, this study aims to provide a new perspective to the literature by examining the change process in the understanding of human nature.

Behind the curtain: exploring the inter-governmental collaboration mechanisms for e-governance platforms

PurposeTo fill the gap, this article examines the inter-governmental collaboration mechanisms behind the platform curtain.Design/methodology/approachBehind the curtain is to look at what makes things happen backstage. For collaborative e-governance platforms, scholars have assumed that technological factors and user characteristics are the determinants for platform success. Little attention has been paid to the issue of how multiple governments, acting as platform co-builders and co-operators, interact and collaborate backstage to provide integrated e-services.FindingsBased on data from survey questionnaires sent to government employees, the results show that governments’ information processing capacities cannot directly affect collaboration; however, these capacities can impact collaboration via the mediating variable of horizontal relations. In addition, we found that higher-ranking authorities are better suited to intervene once horizontal relations have been established and that more adaptable organizations are better at forming horizontal relations with peers. For governments participating in collaborative e-governance platforms, our findings are practically applicable.Originality/valueThe research question reads as: How do various government departments acting as platform co-builders and co-operators judge their collaboration performance, and what collaboration mechanisms contribute to it? We study this research question by constructing a conceptual model based on the Organizational Information Processing Theory (OIPT) and the Collaborative Governance Theory (CGT), both suggesting information processing capacities, organizational flexibility, horizontal relations and vertical intervention as indispensable factors influencing collaboration performance in ICT-supported groupwork. We propose and test four hypotheses on the relationships among these four factors to reveal the inter-governmental collaboration mechanisms for cross-government platformisation projects.

Policy complexity suppresses dopamine responses.

Limits on information processing capacity impose limits on task performance. We show that animals achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals. In support of this hypothesis, we find that policy complexity suppresses midbrain dopamine responses to reward outcomes, thereby reducing behavioral sensitivity to these outcomes. Our results suggest that policy compression shapes basic mechanisms of reinforcement learning in the brain.

Comparative effectiveness of open and closed skill exercises on cognitive function in young adults: a fNIRS study

While it is widely acknowledged that exercise has positive effects on cognitive function, the specific impacts of different types of exercises, particularly open and closed skill exercises, on cognitive impairment continue to be a debated topic. In this study, we used fNIRS and cognitive psychology tasks to investigate the effects of different types of exercises on cognitive function and brain activity in young adults. We conducted an observational study to assess the cognitive function of participants who had engaged in these exercises for a long period. Additionally, we examined the effects of open skill exercise (badminton) and closed skill exercise (calisthenics) on localized blood flow in the prefrontal lobe of the brain using an experimental research method. Specifically, during the Stroop task, the badminton group exhibited significantly higher △HbO2 in channel 18, corresponding to the dorsolateral prefrontal cortex, compared to the calisthenics group (F = 4.485, P < 0.05, η2 = 0.074). In the 2-back task, the calisthenics group showed significantly higher △HbO2 in channel 17, corresponding to the frontopolar area, dorsolateral prefrontal cortex and inferior prefrontal gyrus, than the badminton group (F = 8.842, P < 0.01, η2 = 0.136). Our findings reveal that open skill exercises are more effective in enhancing cognitive inhibition, thereby increasing attention capacity, self-regulation, and flexibility in response to environmental changes. Conversely, closed skill exercises demonstrate greater efficacy in improving working memory within cognitive functions, showcasing an enhanced capacity for information processing and storage. These data indicate that while both open and closed skill exercises are beneficial for cognitive function, they exhibit significant distinctions in some aspects.

Generative Models Utilizing Padding Can Efficiently Integrate and Generate Multi-Omics Data

Technological advances in information-processing capacity have enabled integrated analyses (multi-omics) of different omics data types, improving target discovery and clinical diagnosis. This study proposes novel artificial intelligence (AI) learning strategies for incomplete datasets, common in omics research. The model comprises (1) a multi-omics generative model based on a variational auto-encoder that learns tumor genetic patterns based on different omics data types and (2) an expanded classification model that predicts cancer phenotypes. Padding was applied to replace missing data with virtual data. The embedding data generated by the model accurately classified cancer phenotypes, addressing the class imbalance issue (weighted F1 score: cancer type &gt; 0.95, primary site &gt; 0.92, sample type &gt; 0.97). The classification performance was maintained in the absence of omics data, and the virtual data resembled actual omics data (cosine similarity mRNA gene expression &gt; 0.96, mRNA isoform expression &gt; 0.95, DNA methylation &gt; 0.96). Meanwhile, in the presence of omics data, high-quality, non-existent omics data were generated (cosine similarity mRNA gene expression: 0.9702, mRNA isoform expression: 0.9546, DNA methylation: 0.9687). This model can effectively classify cancer phenotypes based on incomplete omics data with data sparsity robustness, generating omics data through deep learning and enabling precision medicine.

On Transducing Properties of Kombucha-Proteinoid Complexes.

We investigate the information processing capacities of kombucha-proteinoid proto-brains, focusing on the transducing properties through accommodation spiking, tonic bursting spiking, and optical and acoustic stimulation. We explore self-organization, adaptability, and emergent phenomena in this unconventional proto-architecture. By constructing kombucha-proteinoid networks exposed to diverse audio stimuli, we analyze nonlinear dynamics using time series analysis. Assessing information representation in the presence of extreme noise, we examine the system's resilience. Our results illustrate intricate pathways resulting from the interplay between the synthetic biological substrate and bio-inspired stimulation. The kombucha-proteinoid proto-brains consistently map complex stimuli to distinct activation levels, showcasing their adaptability and potential for information processing without the need for external shaping circuits.

Polarization-frequency multiplexing holograms employing Bi-layered patterned coding metasurfaces

A surge in interest surrounding metasurfaces has been witnessed in recent years, driven by their capability to autonomously manipulate electromagnetic wavefront (EM) in three-dimensional space. While the initial focus in metasurface research has often been on manipulating electromagnetic waves using single polarization and frequency schemes, significant advancements have been made to overcome these limitations. Recent developments have introduced multifunctional metasurfaces that enable simultaneous control over multiple frequencies and polarizations, substantially enhancing their capacity for information processing and communication. Building on these advancements, our work introduces a novel three-channel full-space metasurface that leverages double-face copper-cladded patterns on a single-layer substrate, significantly increasing channel capacity through both frequency and polarization multiplexing. In order to achieve efficient control of the EM wavefront in multi-frequency and polarization scenarios, a super unit cell-based approach is utilized, wherein high-performance meta-atoms are combined to form super unit cells with minimal interferences. Specifically, different functionalities can be achieved in full space under different wave polarizations and frequencies. To validate the concept, a metasurface sample is fabricated and experimentally tested. The experimental results agree with both theoretical predictions and simulations, confirming the effectiveness of a three-channel metasurface holography. The metasurface achieves independent control of the incident EM wave's polarization and frequency, thus enhancing information storage capacity and encryption security.