Human Brain Processes Research Articles

Time courses of brain plasticity underpinning visual motion perceptual learning

Visual perceptual learning (VPL) refers to a long-term improvement of visual task performance through training or experience, reflecting brain plasticity even in adults. In human subjects, VPL has been mostly studied using functional magnetic resonance imaging (fMRI). However, due to the low temporal resolution of fMRI, how VPL affects the time course of visual information processing is largely unknown. To address this issue, we trained human subjects to perform a visual motion direction discrimination task. Their behavioral performance and magnetoencephalography (MEG) signals responding to the motion stimuli were measured before, immediately after, and two weeks after training. Training induced a long-lasting behavioral improvement for the trained direction. Based on the MEG signals from occipital sensors, we found that, for the trained motion direction, VPL increased the motion direction decoding accuracy, reduced the motion direction decoding latency, enhanced the direction-selective channel response, and narrowed the tuning profile. Following the MEG source reconstruction, we showed that VPL enhanced the cortical response in early visual cortex (EVC) and strengthened the feedforward connection from EVC to V3A. These VPL-induced neural changes co-occurred in 160–230 ms after stimulus onset. Complementary to previous fMRI findings on VPL, this study provides a comprehensive description on the neural mechanisms of visual motion perceptual learning from a temporal perspective and reveals how VPL shapes the time course of visual motion processing in the adult human brain.

THE EVOLUTION OF METAPHOR IN THE DIACHRONIC ASPECT ON THE MATERIAL OF ENGLISH POETRY

Metaphor is a linguistic tool used to express ideas or concepts through comparison. In the diachronic aspect, metaphor can be observed over time as it changes and evolves in a language.The term "diachronic" refers to the study of language evolution over time, and in this context, the article is focused on the evolution of metaphor in English poetry. The use of metaphor has been a consistent feature of poetry throughout history, and this article analyzes the ways in which metaphor has evolved over time in the English language.Different types of English poetry are analyzed in the article, ranging from medieval to contemporary, and tracing the evolution of metaphorical language in each era. This analysis involves identifying specific metaphors or themes thatwere popular in each era, as well as examining how the use of metaphor has changed over time [1, p.24]. Such methods as literary analysis, descriptive analysis, and comparative historical method were used to study metaphor in the diachronic aspect.By analyzing the use of metaphor in English poetry across different time periods, researchers gain insight into the historical and cultural contexts that shaped language use. It also provides insights into the cognitive processes involved in metaphorical thinking and language use. By examining how poets have used metaphor to convey meaning and evoke emotions over time, researchers gain a deeper understanding of how the human brain processes and interprets language.In summary, the study of the evolution of metaphor in the diachronic aspect on the material of English poetry is scientifically significant because it sheds light on the historical and cultural contexts of language use, provides insights into the cognitive processes involved in metaphorical thinking, and has practical applications in fields such as language teaching and natural language processing.

Correlating physico-chemical properties of analytes with Hansen solubility parameters of solvents using machine learning algorithm for predicting suitable extraction solvent

Artificial neural networks (ANNs) are biologically inspired algorithms designed to simulate the way in which the human brain processes information. In sample preparation for bioanalysis, liquid–liquid extraction (LLE) represents an important step with the extraction solvent selection is the key laborious step. In the current work, a robust and reliable ANNs model for LLE solvent prediction was generated which could predict the suitable solvent for analyte extraction. The developed ANNs model takes a set of chosen descriptors for the cited analyte as an input and predicts the corresponding Hansen solubility parameters of the suitable extraction solvent as a model output. Then, from the solvent combination’s appendix, the analyst can identify the proposed extraction solvents' combination for the cited analyte easily and efficiently. For the experimental validation of the model prediction capabilities, twenty structurally diverse drugs belonging to different pharmacological classes were extracted from human plasma. The extraction process was performed using the predicted extraction solvent combination for each drug and quantitively estimated by HPLC/UV methods to assess their extraction recovery. The developed LLE solvent prediction model is in- line with the global trend towards green chemistry since it limits the consumption of organic solvents.

IoT-based trusted wireless communication framework by machine learning approach

The traditional Radio-Frequency Systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system.

PLD‐Grown Semi‐Insulating Ga2O3 Thin Film‐Based Optoelectronic Artificial Synaptic Devices for Neuromorphic Computing Applications

AbstractDevelopment of artificial opto‐electronic synaptic devices plays a crucial role for the practical implementation of energy‐efficient, parallel processing of human brain. In this article, two terminal inter‐digitated devices are fabricated on Gallium oxide (Ga2O3) thin films grown on sapphire substrates by pulsed laser deposition (PLD) technique to study its ability to mimic biological synaptic behaviors. The layers are found to exhibit long persistent photo‐conductivity (PPC) effect, which is identified to be the key parameter to replicate the behavior of biological synapses. Channel resistance and PPC time constants should also be optimized to improve the efficiency of response and energy consumption of synaptic devices. It has been observed that both conductivity and the PPC decay time of Ga2O3‐films can be controlled by varying oxygen pressure and growth temperature (TG). These devices demonstrate their ability to perform paired pulse facilitation (PPF) at very low applied bias in mV‐range. They can mimic biological synapses showing short‐to‐long‐term memory transition (STM‐to‐LTM) and learning‐forgetting behavior. One of these devices is found to show synaptic behavior with the energy consumption of as low as 71fJ electrical and 21nJ optical per synaptic event. These findings thus strengthen the candidature of Ga2O3 films for the development of next‐generation opto‐electronic neuromorphic devices and systems.

Cognitive Security in a Changing World: Citizen Perceptions During Finland's NATO Joining Process

Contemporary conflicts are multifaceted and no longer fit the traditional war-and-peace dichotomy due to digital dimensions and the role of the human mind. The concept of warfare has transformed significantly: it's no longer solely reliant on physical capabilities but increasingly fought within digital environments and individuals' minds. These persistent, intertwined crises and psychological information influence present challenges to cognitive security. Psychological influence shapes opinions, attitudes, emotions, behaviors, and decision-making in individuals, groups, and societies using various methods, often involving digital tools to manipulate cognitive processes. It aims to shape the human mind, going beyond altering information to influence how the human brain processes received information. To safeguard human cognition, cognitive security is crucial. It involves the capability to detect, recognize, control, and counter negative psychological information influence aimed at an individual. Cognitive security plays a critical role in enabling individuals and society to recognize, understand, and manage a wide range of threats and risks. The rapidly changing world, driven by technology, politics, and the environment, poses new challenges for citizens' cognitive security. As warfare evolves, individuals struggle to understand the complex threats, including cyber and information influence. Hence, this study aims to ascertain whether individuals' feeling of security has changed and if they are perceiving psychological information influence. The study investigates the sense of security among Finnish people using survey data collected during two significant time periods: after Finland announced its intention to join NATO (N = 1080) and after it officially became a NATO member (N = 1047). Additionally, whether an increase in hostile online influence and disruptions in the cyber environment was noticed by Finnish people during these same time frames is being investigated. The results indicate a statistically significant decrease in the feeling of security and a significant increase in the awareness of hostile influences. This implies that these phenomena warrant further investigation to gain a better understanding of citizens' cognitive security status and to explore ways to improve it.

Learning of Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching

This article reviews the psychological and neuroscience achievements in concept learning since 2010 from the perspectives of individual learning and social learning, and discusses several issues related to concept learning, including the assistance of machine learning about concept learning. In terms of individual learning, current evidence shows that the brain tends to process concrete concepts through typical features (shared features); and for abstract concepts, semantic processing is the most important cognitive way. In terms of social learning, interpersonal neural synchrony (INS) is considered the main indicator of efficient knowledge transfer (such as teaching activities between teachers and students), but this phenomenon only broadens the channels for concept sources and does not change the basic mode of individual concept learning. Ultimately, this article argues that the way the human brain processes concepts depends on the concept’s own characteristics, so there are no “better” strategies in teaching, only more “suitable” strategies.

IMPLEMENTASI METODE SPEECH RECOGNITION DALAM AL QURAN DIGITAL UNTUK MENINGKATKAN PELAFALAN

Enhancing the pronunciation proficiency of the Quran is of paramount importance in comprehending its content. Pronunciation errors can alter the meaning of Quranic verses, making accurate pronunciation a key to the correct understanding of Islamic teachings, particularly the Quran. Through the implementation of speech recognition methods in a digital Quranic application, individuals are aided in correctly reciting Quranic verses, thus providing an interactive learning medium. In this research, the author developed data using Neural Networks algorithms that mimic the way the human brain processes and retains information. The results of this study demonstrate that the utilization of speech recognition technology in Quranic learning can significantly improve pronunciation skills. It is hoped that this application can serve as a supportive system facilitating Quranic education.

Интерфейсы «мозг–компьютер» и «мозг–мозг» в структуре конвергирующих технологий (на основе экспериментальных данных НИТЦ нейротехнологий ЮФУ)

The article presents a conceptual analysis of the main directions and methods of studying the human psyche, consciousness and brain in contemporary domestic and foreign research. The concept of “converging technologies” is considered in context of the creation of Artificial General Intelligence (AGI). It describes programs for the neuroscientific study of organization of psychic processes in the field of visualization of brain processes and the mapping method, which helps to understand how different parts of the brain can be structurally and functionally separated from each other. The publication pays special attention to understanding the possible use of Brain-Computer Interfaces (BCI) and Brain-to-Brain Interfaces (BMI) in the creation of AGI, “hybrid intelligence”. Brain interfaces are presented as fundamentally new channels of communication and control. An example of a biohybrid odor recognition system developed at the Research Center of Neurotechnology of the Southern Federal University is given. We are talking about the implementation of control through the BMI and mental commands of a bio­logical object – a rat. From the position of a body-oriented approach this work reveals the prob­lem of co-evolution of the body, consciousness, technology and social environment in the post-non-classical paradigm. It is concluded that modern neuroscience has a significant impact on understanding the functioning of the human brain, consciousness and cognitive processes. The feasibility of studying the possible use of BCI and BMI technologies in stimulating brain areas to create sensations equivalent to touch, etc. is noted. We are also talking about the use of biohybrid systems controlled through BMI technology as systems that combine the advan­tages of natural and artificial intelligence (with further elaboration of ethical issues).

The Time-Course of Food Representation in the Human Brain.

Humans make decisions about food every day. The visual system provides important information that forms a basis for these food decisions. Although previous research has focused on visual object and category representations in the brain, it is still unclear how visually presented food is encoded by the brain. Here, we investigate the time-course of food representations in the brain. We used time-resolved multivariate analyses of electroencephalography (EEG) data, obtained from human participants (both sexes), to determine which food features are represented in the brain and whether focused attention is needed for this. We recorded EEG while participants engaged in two different tasks. In one task, the stimuli were task relevant, whereas in the other task, the stimuli were not task relevant. Our findings indicate that the brain can differentiate between food and nonfood items from ∼112 ms after the stimulus onset. The neural signal at later latencies contained information about food naturalness, how much the food was transformed, as well as the perceived caloric content. This information was present regardless of the task. Information about whether food is immediately ready to eat, however, was only present when the food was task relevant and presented at a slow presentation rate. Furthermore, the recorded brain activity correlated with the behavioral responses in an odd-item-out task. The fast representation of these food features, along with the finding that this information is used to guide food categorization decision-making, suggests that these features are important dimensions along which the representation of foods is organized.

Using unsupervised capsule neural network reveal spatial representations in the human brain.

Humans can extract high-level spatial features from visual signals, but spatial representations in the brain are complex and remain unclear. The unsupervised capsule neural network (U-CapsNet) is sensitive to the spatial location and relationship of the object, contains a special recurrent mechanism and uses a self-supervised generation strategy to represent images, which is similar to the computational principle in the human brain. Therefore, we hypothesized that U-CapsNet can help us understand how the human brain processes spatial information. First, brain activities were studied using functional magnetic resonance imaging during spatial working memory in which participants had to remember the locations of circles for a short time. Then, U-CapsNet served as a computational model of the brain to perform tasks that are identical to those performed by humans. Finally, the representational models were used to compare the U-CapsNet with the brain. The results showed that some human-defined spatial features naturally emerged in the latent space of U-CapsNet. Moreover, representations in U-CapsNet captured the response structure of two types of brain regions during different activity patterns, as well as important factors associated with human behavior. Together, our study not only provides a computationally feasible framework for modeling how the human brain encodes spatial features but also provides insights into the representational format and goals of the human brain.

Intracortical recordings reveal vision-to-action cortical gradients driving human exogenous attention

Exogenous attention, the process that makes external salient stimuli pop-out of a visual scene, is essential for survival. How attention-capturing events modulate human brain processing remains unclear. Here we show how the psychological construct of exogenous attention gradually emerges over large-scale gradients in the human cortex, by analyzing activity from 1,403 intracortical contacts implanted in 28 individuals, while they performed an exogenous attention task. The timing, location and task-relevance of attentional events defined a spatiotemporal gradient of three neural clusters, which mapped onto cortical gradients and presented a hierarchy of timescales. Visual attributes modulated neural activity at one end of the gradient, while at the other end it reflected the upcoming response timing, with attentional effects occurring at the intersection of visual and response signals. These findings challenge multi-step models of attention, and suggest that frontoparietal networks, which process sequential stimuli as separate events sharing the same location, drive exogenous attention phenomena such as inhibition of return.

Moving toward reality: Electrocortical reactivity to naturalistic multimodal emotional videos.

While previous research has investigated the effects of emotional videos on peripheral physiological measures and conscious experience, this study extends the research to include electrocortical measures, specifically the steady-state visual-evoked potential (ssVEP). A carefully curated set of 45 videos, designed to represent a wide range of emotional and neutral content, were presented with a flickering border. The videos featured a continuous single-shot perspective, natural soundtrack, and excluded elements associated with professional films, to enhance realism. The results demonstrate a consistent reduction in ssVEP amplitude during emotional videos which strongly correlates with the rated emotional intensity of the clips. This suggests that narrative audiovisual stimuli have the potential to track dynamic emotional processing in the cortex, providing new avenues for research in affective neuroscience. The findings highlight the potential of using realistic video stimuli to investigate how the human brain processes emotional events in a paradigm that increases ecological validity. Future studies can further develop this paradigm by expanding the video set, targeting specific cortical networks, and manipulating narrative predictability. Overall, this study establishes a foundation for investigating emotional perception using realistic video stimuli and has the potential to expand our understanding of real-world emotional processing in the human brain.

Neural correlates of thermal stimulation during active touch.

Thermal feedback technologies have been explored in human-computer interaction to provide secondary information and enhance the overall user experience. Unlike fast-response haptic modalities such as vibration and force feedback, the human brain's processes associated with thermal feedback are not fully understood. In this study, we utilize electroencephalography (EEG) brain imaging to systematically examine the neural correlates associated with a wide range of thermal stimuli, including 9, 15, 32, and 42°C, during active touch at the fingertip. A custom experimental setup is developed to provide thermal stimulation at the desirable temperature levels. A total of 30 participants are recruited to experience the four levels of thermal stimulation by actively touching a thermal stimulation unit with the index finger while recording brain activities via EEG. Time-frequency analysis and power spectral density (PSD) of the EEG data are utilized to analyze the delta, theta, alpha, beta, and gamma frequency bands. The results show that the delta, theta, and alpha PSDs of 9 and 15°C stimuli are significantly higher than the PSDs of 32 and 42°C in the right frontal area during the early stage of the stimulation, from 282 ms up to 1,108 ms (One-way ANOVA test, Holm-Bonferroni correction, p < 0.05). No significant differences in PSDs are found between 9 and 15°C thermal stimuli or between 32 and 42°C thermal stimuli. The findings of this study inform the development of thermal feedback system in human-computer interaction.

An Evaluative Baseline for Sentence-Level Semantic Division

Semantic folding theory (SFT) is an emerging cognitive science theory that aims to explain how the human brain processes and organizes semantic information. The distribution of text into semantic grids is key to SFT. We propose a sentence-level semantic division baseline with 100 grids (SSDB-100), the only dataset we are currently aware of that performs a relevant validation of the sentence-level SFT algorithm, to evaluate the validity of text distribution in semantic grids and divide it using classical division algorithms on SSDB-100. In this article, we describe the construction of SSDB-100. First, a semantic division questionnaire with broad coverage was generated by limiting the uncertainty range of the topics and corpus. Subsequently, through an expert survey, 11 human experts provided feedback. Finally, we analyzed and processed the feedback; the average consistency index for the used feedback was 0.856 after eliminating the invalid feedback. SSDB-100 has 100 semantic grids with clear distinctions between the grids, allowing the dataset to be extended using semantic methods.

Development of a neural network­based transcription method

In today's ever-evolving digital era, the utilization of audio and video materials has become increasingly prevalent in various spheres, including educational sectors, research initiatives, technological developments, and daily communication. This trend is driven by the convenience and efficiency of these mediums, offering quick access to information without the distractions often associated with more traditional methods like manual writing or typing, especially in scenarios where such activities are impractical, such as when traveling in a vehicle. However, this shift towards audio-visual content has introduced the challenge of converting these dynamic formats into text for a range of purposes. This need arises for several reasons. Firstly, editing and refining spoken content is often easier when it's transformed into a written format, allowing for a more thorough review and adjustment process. Secondly, in situations where individuals prefer or require written documentation — for instance, in academic or professional settings — transcription becomes a critical tool. Additionally, the clarity of audio and video content can vary greatly, with factors like diction, accent, background noise, and recording quality affecting the comprehensibility of the material. To address these challenges, transcription — the process of translating audio and video content into text — has become a valuable solution. It involves a meticulous process of listening, interpreting, and typing out the content, ensuring that the essence and nuances of the original material are accurately captured. This task demands not only attention to detail but also a deep understanding of the context and subject matter to ensure precision and reliability. In the realm of technology, advancements such as neural networks have revolutionized the transcription process. Neural networks, complex algorithmic structures that emulate human brain functioning, can learn, adapt, and process diverse types of data. In transcription, they are employed to recognize and interpret various speech patterns, accents, and languages, significantly enhancing the accuracy and speed of converting spoken words into written text. This integration of advanced technology in transcription not only streamlines the process but also opens new possibilities for accessibility, research, and data analysis, making it an indispensable tool in the modern world.

Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms

With the increase in computing power and the availability of large amounts of data, deep learning techniques, especially convolutional neural networks (CNNS) and recurrent neural networks (RNNS), have become important tools for processing complex signals. These methods show excellent performance in speech recognition, image processing, natural language processing and so on. In this paper, we explore the application of recurrent neural network (RNN) sequence algorithms in the field of computer digital signal processing, highlighting current artificial intelligence techniques and their capabilities in solving complex signal processing problems. First, the paper reviews the basic principles and development of deep learning and RNN sequence algorithms, highlighting the advances these advanced technologies have made in simulating the way the human brain processes information. The practical application and effect of RNN sequence algorithm in computer digital signal processing are demonstrated through experimental data. By comparing with traditional algorithms, we demonstrate the efficiency and accuracy of RNN in processing complex signals, such as speech recognition in noisy environments and real-time video data processing. The experimental data not only demonstrate the effectiveness of RNNS in this field, but also highlight the unique advantages of deep learning methods when dealing with large and high-dimensional data. Through these empirical studies, this paper aims to provide researchers and engineers with an in-depth understanding of the potential of RNNS in digital signal processing applications, and looks forward to the future development direction of artificial intelligence technology in this field.

The Influence of Neurosciences on Understanding the Bodily Conditioning of Cognitive Processes: a Socio-Anthropological Aspect

The study presents a conceptual analysis of the main approaches to the study of the human brain and consciousness from the standpoint of modern domestic and foreign neuroscience. Relevant interpretations of such problematic issues and concepts as “the boundary of the human body”, “embodied knowledge”, “general artificial intelligence”, “self”, etc. From the standpoint of a body-oriented approach, the problem of co-evolution of the body, consciousness, technology and social environment is considered. The idea of the body as an artifact is updated in the context of technological change in human nature, which entails the elimination of the living human body. On the example of the Homeric epic, it is shown that attempts to overcome vulnerability demonstrate one of the fundamental human needs. The realization of the need to expand and push the boundaries of the body in the anthropotechnological environment is presented in the form of a cyborg. It is suggested that, in terms of content, the idea of the “embodied mind”, as well as the project of creating a Hybrid reality and pan-communication, stem from the immanent human need for bodily involvement in the surrounding world. The features of the creation of Artificial Intelligence and Artificial General Intelligence at the present stage are revealed. The expediency of studying the results of neuroscientific comprehension of the organization of mental processes, first of all, in the field of visualization of brain processes and the mapping method, is noted. The main modern projects for the study of the brain, as well as the most effective methods for obtaining data on brain structures, are considered. The actual results of the research are described in such areas of neuroscience as: the study of mirror neurons and their systems; “reading the brain”, the study of neural correlates of consciousness. The results of studies of representatives of domestic neuroscience are presented. The human “I” is considered as a unique structure of the Ego-system of the brain (“self”). In conclusion, a conclusion is made about the significant influence of modern neuroscience on understanding the features of the functioning of the human brain, consciousness and cognitive processes; the effectiveness of applying the body-oriented approach to comprehending the integrity of “body-consciousness-technologies-social environment” is revealed.

Integration of Ibn Sina’s Universal Language Theory and Neuroscience in the Development of Learning Media

The current research attempted to integrate Ibn Sina's universal language theory and neuroscience in the development of learning media. It adopted a qualitative library method, with data collected from literature related to universal language theory, neuroscience, and the development of learning materials of Ibn Sina through manual and digital searches for relevant references. The data collected was analyzed by using content analysis which encompassed the data reduction, presentation, and the synthesis of new and comprehensive conceptual frameworks. The results showed that Ibn Sina’s universal language theory offered valuable insights into the significance of language in communication and cognition. Neuroscience provided an understanding of how the human brain processes information and learns. The concepts associated with Ibn Sina's universal language theory and neuroscience, including elements, such as signs, meaning, and relationship between language and reality, were explored to examine their influence on learning media development. The understanding of the principles of human brain-based learning, based on the results of neuroscience, served as a foundation for effective learning media development. By integrating these attributes, the development of learning materials incorporated the conveyance of information in harmony with universal language comprehension and brain-based learning principles. Therefore, learning material developers and teachers should create instructional methods in accordance with advancements in science and technology.

Separable processes for live “in-person” and live “zoom-like” faces

Abstract It has long been understood that the ventral visual stream of the human brain processes features of simulated human faces. Recently, specificity for real and interactive faces has been reported in lateral and dorsal visual streams, raising new questions regarding neural coding of interactive faces and lateral and dorsal face-processing mechanisms. We compare neural activity during two live interactive face-to-face conditions where facial features and tasks remain constant while the social contexts (in-person or on-line conditions) are varied. Current models of face processing do not predict differences in these two conditions as features do not vary. However, behavioral eye-tracking measures showed longer visual dwell times on the real face and also increased arousal as indicated by pupil diameters for the real face condition. Consistent with the behavioral findings, signal increases with functional near infrared spectroscopy, fNIRS, were observed in dorsal-parietal regions for the real faces and increased cross-brain synchrony was also found within these dorsal-parietal regions for the real In-person Face condition. Simultaneously, acquired electroencephalography, EEG, also showed increased theta power in real conditions. These neural and behavioral differences highlight the importance of natural, in-person, paradigms and social context for understanding live and interactive face processing in humans.