Human Processes Research Articles

The Evolution and Architecture of Multimodal AI Systems

This technical article explores the evolution, architecture, and implementation challenges of multimodal AI systems, which represent a significant advancement in artificial intelligence. The article explores how these systems integrate multiple input modalities to achieve comprehensive understanding and analysis capabilities, mirroring human cognitive processes. Through detailed analysis of system architectures, performance metrics, and implementation strategies, we investigate the current state of multimodal AI across various applications, from virtual assistants to healthcare analytics. The article covers core technical components, data synchronization challenges, resource optimization techniques, and future directions in the field, providing insights into both theoretical frameworks and practical implementations.

BERT-assisted behavioral profiling of polysemy: contrastive analysis of HONG in Chinese and RED in English

Abstract This study conducts a corpus-based contrastive analysis of the basic color term “red” in Chinese (HONG) and English (RED), innovatively employing the AI model BERT to triangulate, validate, and enrich the findings of the behavioral profile (BP) analysis. Results illustrate HONG’s more variational semasiological structure than RED, with both exhibiting unique usage patterns among similarities. The triangulation with BERT embeddings visualizes the performance of each subjectively annotated BP variable, and further demonstrates the semantic extending process in both HONG and RED. The underlying cognitive and socio-cultural factors are lastly discussed. Theoretically, this study bridges frequency-based AI models and usage-based Cognitive Linguistics by simulating human cognitive processes to abstract linguistic knowledge from extensive data, advancing our understanding of language through AI technology. Methodologically, this study is the first to integrate AI models in assisting BP analysis, with BERT embeddings offering objective corroborations and deeper insights for conclusions drawn from subjectively labeled BP data.

Global precedence effect in fear generalization and the role of trait anxiety and intolerance of uncertainty.

Fear overgeneralization is widely considered to be a core feature of pathological anxiety, and the excessive spread of fear can be a great burden on patients. Although perceptual processing is a basic process of human cognition, the impact of visual perception recognition on fear generalization has not yet been fully explored. In this study, 69 subjects were recruited to explore the effects of the hierarchical features of stimuli on fear generalization and examine the roles of trait anxiety (TA) and intolerance of uncertainty (IU) in this process. The results reveal that compared with the local features, the subjects showed stronger fear responses to global features of the stimuli, and the weakest fear response was to the reversed features. Furthermore, the results preliminarily suggest that in generalization dominated by global features, TA intensifies the fear response; while in generalization dominated by local features, along with TA, IU makes a unique contribution to increasing the magnitude of fear generalization. These results confirm new potential pathways for fear generalization, specifically the configurational hierarchical features of stimuli. This is explained through object recognition and cognitive bias, implying that cognitive processes and personality traits have interacting effects on fear generalization.

Advancing explainability of adversarial trained Convolutional Neural Networks for robust engineering applications

The great success of Convolutional Neural Networks has driven their widespread use in Industry 5.0, especially in safety-critical applications where model robustness is essential. While adversarial training is commonly used to make models more robust, it often changes how the model fuses features, leading to complex trade-offs and making its behavior harder to understand. This lack of transparency contradicts the demand for interpretable and trustworthy artificial intelligence systems in modern industry, where applications like autonomous driving and intelligent medical systems require model interpretability to assign post-accident responsibility and support legal and liability decisions. Our work introduces an innovative approach to enhance the interpretability of adversarially trained Convolutional Neural Networks, providing detailed and quantifiable insights through a six-dimensional semantic framework. This framework, which examines objects, parts, scenes, materials, textures, and colors, offers fine-grained explanations that closely mirror human cognitive processes. We conduct extensive experiments across multiple classical models, and the results reveal that adversarial training induces structure-dependent changes in feature preferences, offering insights into the trade-off between standard and robust accuracy in adversarially trained Convolutional Neural Networks. These findings provide rational support for the use of artificial intelligence in real-world applications and promote the development and implementation of explainable artificial intelligence in modern industries.

Psychological Approach in Islamic Studies

The integration of psychological approaches in Islamic studies has gained significant attention in recent years, emphasizing the understanding of human behavior, mental processes, and emotions through both scientific and Islamic perspectives. This paper explores how Islamic teachings, particularly from the Quran and Hadith, align with modern psychological principles, offering a comprehensive framework for mental health, personal development, and emotional well-being. The application of concepts such as nafs (self/soul), fitrah (innate nature), and ruh (spirit) are discussed in relation to psychological theories of personality, motivation, and mental health. By bridging these fields, the study seeks to demonstrate how psychological tools and therapeutic techniques can be adapted within an Islamic framework to enhance spiritual and mental well-being, addressing issues like anxiety, depression, and personal growth. This interdisciplinary approach not only enhances the relevance of Islamic teachings in contemporary mental health discussions but also offers new perspectives for understanding human nature through a spiritual and scientific lens.

EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer

Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer. First, the Multi-Scale Dynamic CNN is used to extract complex spatial and spectral features from raw EEG signals, which not only avoids information loss but also reduces computational costs associated with the time-frequency conversion of signals. Then, the Gated Transformer Encoder is utilized to capture global dependencies of EEG signals. This encoder focuses on specific regions of the input sequence while reducing computational resources through parallel processing with the improved multi-head self-attention mechanisms. Third, the Temporal Convolution Network is used to extract temporal features from the EEG signals. Finally, the extracted abstract features are fed into a classification module for emotion recognition. The proposed method was evaluated on three publicly available datasets: DEAP, SEED, and SEED_IV. Experimental results demonstrate the high accuracy and efficiency of the proposed method for emotion recognition. This approach proves to be robust and suitable for various practical applications. By addressing challenges posed by existing methods, the proposed method provides a valuable and effective solution for the field of Brain-Computer Interface (BCI).

Artificial systems for the diagnosis of Meniere’s disease: A review

Meniere’s Disease (MD) is a complex, multifactorial inner ear disorder characterized by episodes of spontaneous vertigo, unilateral fluctuating sensorineural hearing loss, aural fullness, and tinnitus. Although endolymphatic hydrops (EH) is often considered a histopathological hallmark of MD, the 2015 diagnostic guidelines emphasize that its presence is not essential for diagnosis. Magnetic Resonance Imaging (MRI) has emerged as a valuable tool for detecting EH, though it remains in a developmental phase. Recently, artificial intelligence (AI)—a rapidly advancing field that simulates human cognitive processes—has garnered significant attention in the study of MD. This paper reviews the current literature on the application of AI and deep learning in the diagnosis, monitoring, and treatment of Meniere’s Disease. Our review encompasses seven relevant studies sourced from PubMed, Scopus, Web of Science, and ScienceDirect. Among these, four articles focus on the use of MRI to detect and quantify endolymphatic hydrops. We present these findings within the context of a development trajectory, discuss the limitations of current methodologies, and outline potential avenues for future advancements.

The Role of Natural Science in HRM at Industry 4.0 Era

Natural sciences—particularly psychology and biology—can be applied to better understand human behavior, cognitive processes, stress responses, and other factors influencing workplace dynamics and employee well-being. Natural science relies on empirical evidence gathered through observation, experimentation, and data analysis. It aims to formulate theories and laws that explain the natural world and predict future outcomes. The emergence of Industry 4.0 has transformed the landscape of Human Resource Management (HRM) by introducing advanced technologies, including artificial intelligence (AI), machine learning, and big data analytics. These innovations have enhanced HRM processes such as recruitment, training, performance evaluation, and employee engagement. Natural sciences play a critical role in understanding the dynamics of these technological advancements, offering insights into human behavior, cognitive processes, and organizational ecosystems. This article explores how principles of natural science, including biology, psychology, and neuroscience, integrate with modern HRM practices in the 4.0 era. Through a qualitative approach, we examine case studies to illustrate the application of natural science in HR strategies, highlighting the advantages and challenges of adopting a scientifically-informed HRM framework.

A REVIEW OF CONCEPTUAL METONYMY - METHODS, THEORY AND DESCRIPTION

This paper systematically reviews the main content and academic contributions of Conceptual Metonymy - Methods, Theory and Description. As Volume 60 of the "Human Cognitive Processing" series, this book collects research findings on metonymy from the 12th International Cognitive Linguistics Conference. Through detailed analysis, this paper highlights the book's significant value in several aspects: First, the metonymy corpus and its 14-field description system established by Barcelona's team laid the methodological foundation for metonymy typology research; Second, the book's in-depth discussion of metonymy properties reveals characteristics such as the contingency and indexicality of metonymic reasoning, as well as the dual role of metonymic source in conceptual integration; Third, through multilingual empirical research, it demonstrates the universality of metonymy in constructional development, emotion construction, lexical morphological changes, and sign language construction, while highlighting metonymy's emotional, evaluative and social functions. This book not only advances the theoretical depth of metonymy research but also points out directions for future research, such as metonymy inhibiting factors and cross-linguistic metonymic features that still require further exploration. KEYWORDS: conceptual metonymy; metonymy corpus; metonymy properties; metonymic reasoning; pragmatic rhetorical functions; cognitive linguistics

What Can One See if One Brings the Linguistic Concept Into a Panchronic View?

This paper takes a panchronic perspective to show that the evolution of a linguistic concept in a worldview embraces this concept’s diachronic depth and this concept’s diachronic variation. The diachronic depth of a concept is an archaic image (modal mental representation) that was prerequisite for this concept (amodal mental representation) to emerge; this image had formed perceptually in the human mind, through the direct visual experience humans had in perceiving the world, which accounts for this concept’s embodiment. Mental image at this concept’s diachronic depth and embodiment of this concept are the two facts that this paper assumes one can see when viewing this concept panchronically. This paper treats panchrony as the combination of diachrony of language with universal processes of human cognition, and suggests that the panchronic mechanism behind linguistic semiosis is the modal-to-amodal conversion that takes place in the human mind and has the inner form of the word as its panchronic product and the image-driven interpretation of the word as its emergent product. This mechanism operates at each stage in language evolution, determines the genesis of the word as that of a sign-symbol, and is universal for the speakers of language by virtue of their embodiment. This paper’s theoretical commitments find application in the case of SIN in the English worldview: archaic image at SIN’s diachronic depth is reconstructed using methodologies of etymological research, as the inner form of the word sin in English; SIN’s content in diachronic variation is reconstructed using methodologies of cognitive linguistic research.

Exploring the interplay between AI and human logic in mathematical problem-solving

This paper investigates the dynamic interplay between Artificial Intelligence (AI) and human logic in the domain of mathematical problem-solving. By critically examining a series of case studies, we compare the efficacy of AI-generated solutions, particularly those offered by ChatGPT, against traditional human problem-solving methods. The study employs various mathematical challenges, ranging from abstract logical puzzles to applied numerical problems, to evaluate AI's problem-solving approach and alignment with human cognitive processes. Our analysis highlights instances where AI's computational strategies complement or diverge from human reasoning, shedding light on AI's potential and limitations in deciphering mathematical problems. Furthermore, we explore the implications of integrating AI tools in educational contexts, specifically their role in enhancing students' mathematical problem-solving skills. The paper aims to contribute to the ongoing discourse on the optimal utilization of AI in education, proposing a balanced approach that leverages AI's computational power while fostering the depth and creativity of human logic. Through this comparative study, we advocate for a collaborative model where AI and human reasoning merge to enrich the educational landscape, particularly in the teaching and learning of mathematics.

A hatékony operátori tevékenység kulcstényezői

Aim: The aim of the study is to explore the role and significance of human factors and cognitive processes in the operator activities of surveillance systems. Methodology: The authors discuss important human factors related to effective video surveillance and the environmental conditions necessary for efficient work, drawing observations from various research studies and standards. Findings: The quality of operator activities is strongly influenced by the operator's sensory, perceptual, and attentional abilities. The efficient operation of surveillance systems largely depends on the operators' intuition, expertise, and the appropriate work environment. Value: The study contributes to a better understanding of the role of human factors in video surveillance systems and allows us to formulate guidelines and recommendations that can improve the efficiency of operator activities.

Key Factors of Efficient Operator Activities

Aim: The study aims to explore the role and significance of human factors and cognitive processes in the operator activities of surveillance systems. Methodology: The authors discuss important human factors related to effective video surveillance and the environmental conditions necessary for efficient work, drawing observations from various research studies and standards. Findings: The sensory, perceptual, and attentional abilities of the operator have a significant impact on the quality of their activities. The efficient operation of surveillance systems largely depends on the operators’ intuition, expertise, and the appropriate work environment. Value: The study contributes to a better understanding of the role of human factors in video surveillance systems and allows us to formulate guidelines and recommendations that can improve the efficiency of operator activities.

Integrating Historical Learning and Multi-View Attention with Hierarchical Feature Fusion for Robotic Manipulation.

Humans typically make decisions based on past experiences and observations, while in the field of robotic manipulation, the robot's action prediction often relies solely on current observations, which tends to make robots overlook environmental changes or become ineffective when current observations are suboptimal. To address this pivotal challenge in robotics, inspired by human cognitive processes, we propose our method which integrates historical learning and multi-view attention to improve the performance of robotic manipulation. Based on a spatio-temporal attention mechanism, our method not only combines observations from current and past steps but also integrates historical actions to better perceive changes in robots' behaviours and their impacts on the environment. We also employ a mutual information-based multi-view attention module to automatically focus on valuable perspectives, thereby incorporating more effective information for decision-making. Furthermore, inspired by human visual system which processes both global context and local texture details, we have devised a method that merges semantic and texture features, aiding robots in understanding the task and enhancing their capability to handle fine-grained tasks. Extensive experiments in RLBench and real-world scenarios demonstrate that our method effectively handles various tasks and exhibits notable robustness and adaptability.

A Novel and Powerful Dual-Stream Multi-Level Graph Convolution Network for Emotion Recognition.

Emotion recognition enables machines to more acutely perceive and understand users' emotional states, thereby offering more personalized and natural interactive experiences. Given the regularity of the responses of brain activity to human cognitive processes, we propose a powerful and novel dual-stream multi-level graph convolution network (DMGCN) with the ability to capture the hierarchies of connectivity between cerebral cortex neurons and improve computational efficiency. This consists of a hierarchical dynamic geometric interaction neural network (HDGIL) and multi-level feature fusion classifier (M2FC). First, the HDGIL diversifies representations by learning emotion-related representations in multi-level graphs. Subsequently, M2FC integrates advantages from methods for early and late feature fusion and enables the addition of more details to final representations from EEG samples. We conducted extensive experiments to validate the superiority of our model over numerous state-of-the-art (SOTA) baselines in terms of classification accuracy, the efficiency of graph embedding and information propagation, achieving accuracies of 98.73%, 95.97%, 72.74% and 94.89% for our model as well as increases of up to 0.59%, 0.32%, 2.24% and 3.17% over baselines on the DEAP-Arousal, DEAP-Valence, DEAP and SEED datasets, respectively. Additionally, these experiments demonstrated the effectiveness of each module for emotion recognition tasks.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall revealed through multi-experiment iEEG replication.

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience network (SN), default mode network (DMN), and frontoparietal network (FPN), provides a framework for understanding these interactions. We analyzed intracranial electroencephalography (EEG) recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting state, highlighting the AI's task-specific role in coordinating large-scale network interactions. This pattern persisted across externally driven memory encoding and internally governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI's unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI's critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall revealed through multi-experiment iEEG replication

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience network (SN), default mode network (DMN), and frontoparietal network (FPN), provides a framework for understanding these interactions. We analyzed intracranial electroencephalography (EEG) recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting state, highlighting the AI’s task-specific role in coordinating large-scale network interactions. This pattern persisted across externally driven memory encoding and internally governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI’s unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI’s critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

Methodology for Object-Level Change Detection in Post-Earthquake Building Damage Assessment Based on Remote Sensing Images: OCD-BDA

Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-level change detection methods is significantly dependent on the consistency between pre- and post-disaster building images, particularly regarding variations in resolution, viewing angle, and lighting conditions; in object-level feature recognition methods, the low richness of semantic details of damaged buildings in images leads to a poor detection accuracy. This paper proposes a novel method, OCD-BDA (Object-Level Change Detection for Post-Disaster Building Damage Assessment), as an alternative to pixel-level change detection and object-level feature recognition methods. Inspired by human cognitive processes, this method incorporates three key steps: an efficient sample acquisition for object localization, labeling via HGC (Hierarchical and Gaussian Clustering), and model training and prediction for classification. Furthermore, this study establishes a change detection dataset based on Google Earth imagery of regions in Hatay Province before and after the Turkish earthquake. This dataset is characterized by pixel inconsistency and significant differences in photographic angles and lighting conditions between pre- and post-disaster images, making it a valuable test dataset for other studies. As a result, in the experiments of comparative generalization capabilities, OCD-BDA demonstrated a significant improvement, achieving an accuracy of 71%, which is twice that of the second-ranking method. Moreover, OCD-BDA exhibits superior performance in tasks with small sample amounts and rapid training time. With only 1% of the training samples, it achieves a prediction accuracy exceeding that of traditional transfer learning methods with 60% of samples. Additionally, it completes assessments across a large disaster area (450 km²) with 93% accuracy in under 23 min.

The Neural Basis and Computational Models of Metacognition

Abstract: The ability to reflect on ones own thinking is what makes human cognition "meta." Metacognition, the capability to assess, reflect on, and control first-order cognitive processes, is essential for flexible and adaptive behaviors across various contexts. This review explores the neural mechanisms and computational models underpinning metacognition. The involvement of brain regions, including the insula, precuneus, medial prefrontal cortex, and dorsolateral prefrontal cortex in metacognitive judgments is examined. How distinct regions support both domain-general and domain-specific metacognitive processes is also explored. Furthermore, the neural correlates of metacognitive executive functions, such as error monitoring and cognitive control, are investigated, with a focus on the prefrontal and anterior cingulate cortex and their roles in regulating working memory and performance monitoring. This review also discusses the Bayesian models of human metacognitive processes proposed by Fleming and Daw. Studies on human metacognition have significant implications for the development of artificial intelligence, evidenced by the H-CogAff architecture, revealing how integrating metacognitive frameworks could enhance AIs transparency, reasoning, adaptability, and perception. The findings suggest that investigating the neural mechanisms and computational models of metacognition is crucial not only for understanding human cognitive processes but also for improving the resilience and flexibility of AI systems. Future studies in this field should expand the scope by integrating broader and more qualitative dimensions, such as affective self-assessment and social cognition, while maintaining the precision of current evaluation approaches.

Synergizing Organizational Values and Psychological Capital for Wellbeing: Insights from Sri Lankan Public Administration

The role of human cognitive processes and constructs such as psychological capital (PsyCap), wellbeing, and values in encouraging positive behavior has been extensively examined in the existing body of knowledge, especially capturing the attention of scholars and practitioners in the domains of management and organization studies. However, how these concepts interactively create positive behavior is rarely taken into consideration in scholarly discussions. Thus, the present paper aims to explore how the values professed by an organization (i.e. espoused values) could lead employees towards positive behaviors through PsyCap and wellbeing. The research setting is Sri Lankan public administrative organization (SLPAO) and the study participants are top-level managers. This study takes an interpretivist approach with a qualitative inquiry by employing a series of in-depth interviews and organizational documents, where the initial findings on espoused values of SLPAO were then subjected to a secondary qualitative framework analysis based on the theoretical lenses of CHOSE and PERMA models of PsyCap and well-being. The findings of the present study show that the public administrators’ perceived espoused values carry reflections of wellbeing and encompass the dimensions of PsyCap. Accordingly, positive emotions are reflected by the values independence and humanity & morality; engagement by service boundedness and duty consciousness; positive relations by fairness & justice and concern on employees; meaning by public inter-est and effectiveness; and accomplishment by efficiency, expertise and formality & order. The findings clearly highlight how the espoused values become a source of PsyCap, encompassing hope and optimism while also tending to the collective well-being, and thereby enhancing positive behaviors overall. The findings suggest the potential application of PsyCap and well-being to leverage espoused values as a precursor of positive behavior among public servants. Nonetheless, the study emphasizes the necessity for further scholarly endeavors to explore the intricate connections between organizational values, PsyCap, wellbeing, and positive organizational behavior.