Human Processes Research Articles

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.

Research on similarity bias in dual objective visual search based on nuclear power human-machine interface icons

Using icons from nuclear power interface as the research object, this study explored how icon similarity affected the performance of dual objective visual search. Firstly, the process of generating similarity bias was described from the perspective of human cognitive processing. The feature attributes of nuclear power icons were extracted, and then associated and mapped with similarity bias attributes. Secondly, a total of 16 instruction icons, device icons, and component icons were selected to propose icon coding logic for different similarity dimensions, and similarity experimental materials were designed. Finally, a dual objective search experiment with a 4 × 4 matrix was conducted to explore the impact of graph similarity on search performance and to determine the priority of perceptual similarity, semantic similarity, and memory similarity. High-level (H) similarity between the two targets resulted in superior visual search performance (p = 0.01 between the response times of similarity high and medium/low). Improving experiential familiarity enhanced search performance in cases of low-level (L) (p = 0.021) and medium-level (M) (p ≤ 0.009) icon similarity, but had no significant impact on search performance in cases of high-level (H) similarity (p ≥ 0.269). Compared to semantic similarity, enhancing perceptual similarity was more likely to improve search performance(p = 0.024).

Indeterminacy and the Immateriality of Thought: Ross on Natural and Formal Structures

This paper addresses a debate on the immateriality of thought, focusing on James Ross’s argument regarding the indeterminacy of physical processes with respect to pure functions. Ross posits that some human cognitive processes, particularly logical reasoning and mathematical functions, exhibit a formal determinacy that no physical process can have, challenging physicalist accounts of mind. A critical response by Peter Dillard, known as the “schmitosis objection,” attempts to undermine Ross’s argument by drawing a parallel between biological processes like mitosis and formal functions such as addition. This paper defends Ross’s position by highlighting an overlooked distinction between transcendent and formal determinacy. By demonstrating that biological processes like mitosis are transcendentally determinate while pure functions are formally determinate, the paper refutes Dillard’s objection and supports Ross’s claim of the partial non-physicality of thought. The argument contributes to ongoing debates in the philosophy of mind and metaphysics by clarifying the distinct ways physical and non-physical processes relate to determinacy.

Towards a Computational Theory of Perceptions under Neutrosophic Environment

Computing and reasoning with perceptions in AI are shifting from traditional methods to those that mimic human cognitive processes. Humans intuitively measure and evaluate without explicit computations, using sensory input and past experiences for quick judgments. The theory of perceptions can enhance AI's ability to replicate human-like assessment by providing a framework for handling the nuances of human reasoning. Traditionally, information granularity is either crisp (c-granular) or fuzzy (f-granular), focusing on measurement-based and perception-based information. This paper outlines a neutrosophic-based computational theory of perceptions (n-CTP), introducing neutrosophic granular (n-granular) information and neutrosophic constraint-centered semantics of natural languages (n-CSNL). Based on the neutrosophy theory, which deals with truth, falsity, and indeterminacy, managing uncertainties and vagueness in real-world data can be done more effectively than with traditional methods. The n-CTP has the potential to enhance AI's ability to process and reason with complex, uncertain, and ambiguous information.

Role of Artificial Intelligence in Forensics

Artificial Intelligence (AI) is a critical domain within software engineering, focusing on computer processes capable of emulating human behaviors and cognitive processes such as learning, reasoning, adaptation and self –correction. Concurrently forensic science applies scientific methodologies to criminal investigations, specifically concentrating on analyzing evidence within criminal cases. The conventional methods of conducting autopsies and forming opinions exhibit numerous limitations, many of which can be effectively addressed through AI integration. Digital forensics stands out as one of the most rapidly advancing technologies, profoundly influencing the techniques and tools employed for analyzing, monitoring and visualizing crime scenes to mitigate emerging threats and cyber-attacks. Moreover, the realm encompasses forensic medicine, which applies medical expertise to administer justice in Legal proceedings, thereby resolving associate legal complexities. Leveraging the capabilities of AI enables the swift and through sorting of files without manual intervention, significantly expediting investigative processes. Furthermore,AI plays a pivotal role in the identification of mutilated bodies, facilitating the development of tools to reconstruct full facial features. AI generated images can provide remarkable resemblances, aiding in accurately depicting facial features to aid identification efforts. Integrating AI into forensic practices enhances efficiency, accuracy and overall effectiveness in resolving criminal cases

An Introduction to the Modular Forensic Handwriting Method

ABSTRACTThe forensic examination of handwriting utilizes complex human perceptual and cognitive processes that can be difficult to accurately and validly describe since, for the most part, these processes are unseen. The series of articles published in this WIREs special issue present the agreed general approach that government practitioners in Australia use in the majority of the handwriting comparisons that they carry out. The systematic modular approach is informed by research and developments in the fields of motor control of handwriting, human factors, validation of handwriting examination, and evidence evaluation and reporting. The method is based around a process outline which structures the comparison process and provides the reader with a guide to the tasks to undertake and issues to consider at the different stages commonly worked through in practical handwriting examinations. Where decision or assessment points occur within the course of the method, a series of modules have been developed which describe the nature of the matter under consideration and address relevant theoretical and practical issues. Each module (published as an article in this special issue) is, as far as is practical, independent of other modules in the method. This will facilitate changes in the process over time that may result from theoretical, practical or technological advances in the field. The document as a whole can be used by practitioners actively involved in casework, or individuals such as researchers, academics, and legal professionals with an interest in understanding or exploring aspects of the practice of forensic handwriting examination.

An integrated framework for eye tracking-assisted task capability recognition of air traffic controllers with machine learning

To effectively address the continuously increasing demands of air transport, air traffic management (ATM) systems are evolving towards a human-artificial intelligence (AI) hybrid automation paradigm. In this paradigm, air traffic controllers (ATCOs) play a crucial role in ensuring safe and efficient operations. Recognizing ATCOs’ task capability is essential for evaluating performance, optimizing task assignments, and personalizing training strategies. Physiological signals, such as eye movements, offer objective insights into human behavior and cognitive processes, making them valuable for identifying the task capability of ATCOs. In this study, an integrated framework leveraging machine learning is proposed to achieve this goal. First, a Transformer-attentional long short-term memory (LSTM) network is developed to analyze eye movement patterns, capturing both global and long-term dependencies for precise skill level detection. Next, manual parameters are extracted from the raw eye tracking data and the SHAP (SHapley Additive exPlanation) method is utilized to determine the importance of these parameters, aiding in the selection of relevant performance metrics. Furthermore, a radar chart is implemented to intuitively visualize and compare performance metrics across different skill levels based on the selected parameters. A case study and extensive experiments are conducted to validate the effectiveness of the proposed framework. This research advances task capability recognition for ATCOs in a human-in-the-loop scenario, with a focus on expertise level detection, parameter importance identification, and performance metric comparison.

The Rise of Deep Learning in Cyber Security: Bibliometric Analysis of Deep Learning and Malware

Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. This method is critical in the development of identifying certain objects, and provides the computational intelligence required to identify multiple objects and distinguish it between object A or Object B. On the other hand, malware is defined as malicious software that seeks to harm or disrupt computers and systems. Its main categories include viruses, worms, Trojan horses, spyware, adware, and ransomware. Hence, many deep learning researchers apply deep learning in their malware studies. However, few articles still investigate deep learning and malware in a bibliometric approach (productivity, research area, institutions, authors, impact journals, and keyword analysis). Hence, this paper reports bibliometric analysis used to discover current and future trends and gain new insights into the relationship between deep learning and malware. This paper’s discoveries include: Deployment of deep learning to detect domain generation algorithm (DGA) attacks; Deployment of deep learning to detect malware in Internet of Things (IoT); The rise of adversarial learning and adversarial attack using deep learning; The emergence of Android malware in deep learning; The deployment of transfer learning in malware research; and active authors on deep learning and malware research, including Soman KP, Vinayakumar R, and Zhang Y.

Exploring Brain Activity in Different Mental Cognitive Workloads

Objective: Understanding neural mechanisms underlying cognitive workload is crucial for advancing our knowledge of human cognition and mental processes. In this study, we utilized electroencephalography (EEG) analysis to investigate brain activity associated with varying mental cognitive workloads from a psychological perspective. Method: We employed a publicly accessible EEG dataset consisting of a cohort of 36 healthy volunteers (75% female), aged 18 to 26 years, while the participants were at rest or engaged in an arithmetic task to explore mental cognitive workload. After preprocessing to reduce noise and various artifacts and to obtain a clean signal for every subject, functional connectivity and complexity features were calculated from EEGs through the coherence and permutation entropy algorithms, respectively. Then, repeated measures analysis of variance (ANOVA) was conducted to assess the differences in complexity and connectivity measures across various brain regions between the rest and task states. Results: Brain sites showed significant within-subject effects, and the interaction between states and channels was significant for connectivity values (F = 3.68, P = 0.034). Post hoc comparisons indicated that FP1-F7, FP1-F8 and FP1-Fz connectivity were significantly lower during the task state compared to the rest state (P < 0.05). Moreover, F4-P3, F4-P4, FP1-O1, FP2-O2, F3-O1, F4-O1, F8-O1, C4-O1, F3-O2, F4-O2, F7-O2, F8-O2, Fz-O1, Fz-O2, Cz-O1 and Fz-P4 connectivity were significantly higher during the arithmetic task state (P < 0.05). Furthermore, brain sites showed significant within-subject effects and the interaction between states and channels was significant for entropy values (F = 3.50, P = 0.041). Post hoc comparisons indicated that the permutation entropy was significantly higher in the FP1, T3, T4, P4 and Pz channels during the arithmetic task compared to the rest state (P < 0.05). Conclusion: During arithmetic tasks, the increased connectivity in the frontoparietal and frontooccipital networks and heightened complexity in the prefrontal, temporal and parietal lobes reflect the collaborative engagement of brain areas specialized in numerical processing, attention, working memory, cognitive control, and visual-spatial cognition. These changes in connectivity and complexity facilitate the integration of multiple cognitive processes essential for effective arithmetic problem-solving.

The post-Cartesian dilemma: Reuniting the mind and body through psychoneuroimmunology

While today, it might seem absurd to hear anyone claim that stress does not alter all aspects of the human experience, including behavioral, cognitive, affective, and physiological processes. Dr. Janice Kiecolt-Glaser started her career at a time when stress was primarily considered a neuroendocrine response with cardiovascular repercussions. She was part of a small group of innovative scientists who began to push the boundaries of stress research – many contemporary immunologists and virologist disputed their early results in 1980s and 90s – and, yet, they persevered by connecting psychological stress to altered immune function via stress-related neuroendocrine changes. As a clinical psychologist, she focused mainly on human research studies to advance the field of psychoneuroimmunology throughout her career. Her research demonstrates how adversity and psychosocial aspects of human experience alter physiological functioning, primarily immune, and health or, in other words, the embodiment of our lived experiences. This short review is a contextualized synthesis of Dr. Kiecolt-Glaser’s key contributions to the fields of psychoneuroimmunology and health psychology and her influence on my present day thinking and research approaches, as well as potential steps forward in our post-pandemic world.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall: A 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 (SN), default mode (DMN), and frontoparietal (FPN) networks, provides a framework for understanding these interactions. We analyzed intracranial 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.

Explaining Crisis Situations via a Cognitive Model of Attention

Decision making in critical situations is a complex process. There are many processes to consider. This paper describes a theoretical approach to researching attentional processes and automatic unconscious processes in terms of metacognition. An application of the approach is presented to explain decision making and metacognition as a solution for ineffective cognitive biases during a crisis situation. Evidence is presented from studies on neuropsychology, cognitive control, and cognitive architectures. An application of the recently formulated semiotic methodology is implemented that allows the design of conceptual models of Attention as Action. The formulation of a general model of attentional processes is based on a set of rules. The crisis phenomenon, as the crisis situation trigger, is semiotically described and applied as insight for a crisis information system design that prompts its users toward self-aware internal decision making. The research conducted evidently shows how the approach can explain the design of several cognitive architectures. Pointing toward metacognition as a solution to a crisis phenomenon and cognitive biases, the paper shows that understanding human cognitive and behavioral processes can significantly improve management in a critical infrastructure crisis situation.

Sentinel mechanism for visual semantic graph-based image captioning

Image captioning aims to generate a description of a given image. However, inherent representation differences between images and sentences make it difficult to align semantic meanings for captioning. Inspired by the human cognitive processes of understanding and describing images, a visual semantic sentinel mechanism based image captioning framework is proposed in this paper. Specifically, we introduce attribute nodes to enable a more comprehensive description of the objects and model the high-level relationships within a visual semantic graph. Then, the visual semantic sentinel mechanism is proposed to simulate the process of sentence generation. visual semantic graphs, visual features and previous language information in generated words are integrated with a semantic sentinel mechanism to align vision-language information and get contextually relevant descriptions of images. Comprehensive experiments on the challenging MS-COCO demonstrate our method outperforms the previous state-of-the-art methods. The code is publicly available at https://github.com/superatops/SSVSG.

Toward Explainable Multiparty Learning: A Contrastive Knowledge Sharing Framework.

Multiparty learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multiparty learning approaches are confronted with obstacles, such as system heterogeneity, statistical heterogeneity, and incentive design. Determining how to deal with these challenges and further improve the efficiency and performance of multiparty learning has become an urgent problem to be solved. In this article, we propose a novel contrastive multiparty learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication and analogize multiparty learning as a many-to-one knowledge-sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets.

Determining the Speed Reaction Time to the Lower Limbs in a Women’ a Volleyball Team (CSU Medicina Tg. Mures)

Speed reaction is an important concept in various fields like physiology, psychology and sports science. It refers to the time it takes to respond to a specific stimulus visual, auditory, tactile. Reaction speed is an important and fundamental measure in the study of human performance and cognitive processes. Methods. The testing was conducted on the female volleyball players, members of the CSU Medicine Târgu Mureș team. This testing took place at the beginning of the 2023-2024 competitive season. Objective. The objectives of this study are to identify the reaction time values obtained during the testing of the CSU Medicine Târgu Mureș team. Results. The analysis of the results was conducted after downloading the data from the Optojump software as follows: Test 1 Range: 0.532 - 0.393 = 0.139 seconds, test 2. Range: 0.531 - 0.425 = 0.106 seconds Test 3 Range: 0.750 - 0.597 = 0.153 seconds Conclusion. This statement suggests that, based on the data collected from the three tests, there is consistency in the performance of individual subjects. Some subjects consistently have low reaction speed values across all three tests, while others consistently have high reaction speed values. Keywords: Volleyball, Optojump, Speed Reaction

Psychomatics-A Multidisciplinary Framework for Understanding Artificial Minds.

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services

Eye gaze contains rich information about human attention and cognitive processes. This capability makes the underlying technology, known as gaze tracking, a critical enabler for many ubiquitous applications and has triggered the development of easy-to-use gaze estimation services. Indeed, by utilizing the ubiquitous cameras on tablets and smartphones, users can readily access many gaze estimation services. In using these services, users must provide their full-face images to the gaze estimator, which is often a black box. This poses significant privacy threats to the users, especially when a malicious service provider gathers a large collection of face images to classify sensitive user attributes. In this work, we present PrivateGaze, the first approach that can effectively preserve users' privacy in black-box gaze tracking services without compromising gaze estimation performance. Specifically, we proposed a novel framework to train a privacy preserver that converts full-face images into obfuscated counterparts, which are effective for gaze estimation while containing no privacy information. Evaluation on four datasets shows that the obfuscated image can protect users' private information, such as identity and gender, against unauthorized attribute classification. Meanwhile, when used directly by the black-box gaze estimator as inputs, the obfuscated images lead to comparable tracking performance to the conventional, unprotected full-face images.

Cortical cognitive processing during reading captured using functional-near infrared spectroscopy

Neuroimaging studies using functional magnetic resonance imaging (fMRI) have provided unparalleled insights into the fundamental neural mechanisms underlying human cognitive processing, such as high-level linguistic processes during reading. Here, we build upon this prior work to capture sentence reading comprehension outside the MRI scanner using functional near infra-red spectroscopy (fNIRS) in a large sample of participants (n = 82). We observed increased task-related hemodynamic responses in prefrontal and temporal cortical regions during sentence-level reading relative to the control condition (a list of non-words), replicating prior fMRI work on cortical recruitment associated with high-level linguistic processing during reading comprehension. These results lay the groundwork towards developing adaptive systems to support novice readers and language learners by targeting the underlying cognitive processes. This work also contributes to bridging the gap between laboratory findings and more real-world applications in the realm of cognitive neuroscience.

Effect of AI on HI

Abstract: This paper explores the profound impact of Artificial Intelligence (AI) on Human Intelligence (HI), examining both the enhancements and challenges AI introduces. AI, defined as the capability of machines to perform tasks requiring humanlike intelligence, has evolved significantly, influencing various domains such as education, healthcare, and the workforce. AI enhances human cognitive processes by assisting in decision-making, problem-solving, and creativity, thereby augmenting HI. In education, AI-driven tools offer personalized learning experiences, while in healthcare; AI improves diagnostics and patient care. However, the integration of AI raises ethical concerns, including privacy, data security, and algorithmic bias. The potential for AI to surpass human intelligence, known as the singularity, poses existential questions about human autonomy. This paper underscores the importance of responsible AI development, emphasizing the need for ethical frameworks to balance technological advancements with human values. As AI continues to advance, fostering effective human-AI collaboration and preparing society for an AI-enhanced future are crucial for maximizing the benefits and minimizing the risks associated with AI's influence on HI.

A sequential multiple attribute three-way group decision-making method to heterogeneous MAGDM problems with unknown weight information

As a decision model to depict the human cognitive process, three-way decision (3WD) provides a reasonable semantic interpretation for solving practical multi-attribute group decision-making (MAGDM) problems. At the same time, there often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the MAGDM problems with heterogeneous information. In this paper, based on 3WD theory and sequential strategy, we propose a sequential multiple attribute three-way group decision-making (SMA3WGD) method for the heterogeneous MAGDM problem with unknown weight information to achieve this goal. At first, we compute the weights of decision-makers by using the consensus degree between each of the decision-makers in a group. Then, we calculate the collective evaluation result of multiple decision-makers by utilizing the mathematical planning method with two objectives of minimum difference and high similarity between group evaluation values and individual evaluation values. Meanwhile, a nested sequence of the set of attributes is constructed according to the attribute values offered by decision-makers that have different degrees of consensus on different attributes. On this basis, a multilevel granular structure for the heterogeneous MAGDM problems is constructed, and then the compute of the cost functions and conditional probability is explored in a single level of granular structure, and the thresholds and three-way decision rules of alternatives are further determined depending on the principle of cost minimization. Furthermore, by using a sequential strategy, we obtain a sequential three-way decision result of the heterogeneous MAGDM problems under the multilevel granular structure. At last, the practicality and advantages of the suggested method are demonstrated via a blockchain service provider selection case study, a parameter sensitivity analysis along with a comparison analysis.