Human Cognition Research Articles

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, and separated the aperiodic and oscillatory components of the signals to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

What Can Conjuring Tell Us About Cognition? The Future of the Science of Magic

During the past 20 years a number of articles have appeared within a field that has become known as the “science of magic.” This subdiscipline of psychology posits that the ancient art of conjuring can be used to develop psychological theories and knowledge, and more than 150 peer-reviewed articles have now been published. In the current article, we critically evaluate what can be considered as the three central tenets of the discipline: that the methods used in magic can be incorporated within experimental research, that magicians have particular insight into human cognition and behavior, and that the deconstruction of magic tricks can uncover novel psychological principles and mechanisms. We also discuss the problem that secrecy in conjuring creates for experimentalists and posit several recommendations that future scientists of magic might want to consider.

The Scientific Approach to Divine Parables on Creation (SADPC):

The primary goal of this research is to introduce instances of a scientific approach to divine parables on creation SADPC as insights into empirical research, entrepreneurial, and intellectual development. The research also examines the relationship between divine parables’ engagement and cognitive science enhancement for the selected case samples of university students in Uganda and Malaysia. The deductive and descriptive correlational analyses were used. On the one hand, the deductive approach used the selected Qur’anic parables as benchmarks for methods, procedures, analyses, and SADPC generalizations. On another hand, the descriptive correlational methods were used to analyze the perceived relationship between divine parables’ engagement and cognitive science enhancement using the two samples’ statistics in question. The deductive findings demonstrated the novelty, comprehensiveness, and relevance of the divine parables’ insights into human scientific cognition. The correlational analyses yielded the rejection of (H0) and installed a (Ha) that there is a significant relationship between divine parables’ engagement and cognitive science enhancement for the selected samples. This research introduced both the Islamic intellectual development model (IIDM) and the Islamic entrepreneurial planning model (IEPM) using SADPC. The SADPC significantly demonstrated an insight into empirical research, entrepreneurial, and intellectual development outlook for future Islamic science research.

The Balance of Ketoacids α-Ketoglutarate and α-Ketoglutaramate Reflects the Degree of the Development of Hepatoencephalopathy in Rats

Hepatoencephalopathy (HE) is a liver disease that can lead to brain pathology and the impairment of human cognitive abilities. The objective assessment of HE disease severity is difficult due to the lack of reliable diagnostic markers. This paper examines the background to the emergence of HE markers and provides a brief overview of research results indicating the diagnostic value of potential markers isolated from a wide range of metabolites analyzed. It has been suggested that metabolites of the glutamate–glutamine (Glu-Gln) cycle, α-ketoglutarate (αKG), and α-ketoglutaramate (αKGM) can act as such markers of HE. The informative value of these markers was revealed during a comparative analysis of the distribution of αKG and αKGM in samples of the blood plasma and tissues (liver, kidneys, and brain) of rats exposed to the strong hepatotoxin thioacetamide (TAA). A comparative analysis of the balance of αKG and αKGM, as well as their ratio (αKG/αKGM) in the examined samples of blood plasma and animal tissues in these models, revealed their diagnostic value for assessing the severity of HE and/or monitoring the recovery process.

Advancements in Smart Wearable Mobility Aids for Visual Impairments: A Bibliometric Narrative Review

Research into new solutions for wearable assistive devices for the visually impaired is an important area of assistive technology (AT). This plays a crucial role in improving the functionality and independence of the visually impaired, helping them to participate fully in their daily lives and in various community activities. This study presents a bibliometric analysis of the literature published over the last decade on wearable assistive devices for the visually impaired, retrieved from the Web of Science Core Collection (WoSCC) using CiteSpace, to provide an overview of the current state of research, trends, and hotspots in the field. The narrative focuses on prominent innovations in recent years related to wearable assistive devices for the visually impaired based on sensory substitution technology, describing the latest achievements in haptic and auditory feedback devices, the application of smart materials, and the growing concern about the conflicting interests of individuals and societal needs. It also summarises the current opportunities and challenges facing the field and discusses the following insights and trends: (1) optimization of the transmission of haptic and auditory information while multitasking; (2) advance research on smart materials and foster cross-disciplinary collaboration among experts; and (3) balance the interests of individuals and society. Given the two essential directions, the low-cost, stand-alone pursuit of efficiency and the high-cost pursuit of high-quality services that are closely integrated with accessible infrastructure, the latest advances will gradually allow more freedom for ambient assisted living by using robotics and automated machines, while using sensor and human–machine interaction as bridges to promote the synchronization of machine intelligence and human cognition.

Big brother: the effects of surveillance on fundamental aspects of social vision.

Despite the dramatic rise of surveillance in our societies, only limited research has examined its effects on humans. While most research has focused on voluntary behaviour, no study has examined the effects of surveillance on more fundamental and automatic aspects of human perceptual awareness and cognition. Here, we show that being watched on CCTV markedly impacts a hardwired and involuntary function of human sensory perception-the ability to consciously detect faces. Using the method of continuous flash suppression (CFS), we show that when people are surveilled (N = 24), they are quicker than controls (N = 30) to detect faces. An independent control experiment (N = 42) ruled out an explanation based on demand characteristics and social desirability biases. These findings show that being watched impacts not only consciously controlled behaviours but also unconscious, involuntary visual processing. Our results have implications concerning the impacts of surveillance on basic human cognition as well as public mental health.

Abstracting Injustice

In this paper, we explore how the neorepublican concepts of domination and antipower can contribute to the surveillance studies literature and a more democratic and participatory approach to technology development and deployment within the criminal justice system. We frame the neorepublican approach as an alternative to the predominant liberal paradigm, arguing that normative surveillance studies scholarship should emphasize the dominating potential of surveillance practices rather than merely trying to limit actual interference in peoples’ lives. To illustrate, we focus on the use of surveillance technologies that capture images of individuals within the US criminal justice system for recognition and/or identification. Facial or other biometric recognition technologies (FRTs) are increasingly built on artificial intelligence and machine learning algorithms (“AI”). Often seen as a faster, more accurate, and less labor-intensive alternatives to human cognition, AI-powered biometric and facial recognition and other image capture technologies have become widely used within public law enforcement agencies around the world. The deployment of these technologies within the US criminal justice system has produced significant forms of injustice, including faulty identification and the subsequent arrest, detention, and incarceration of innocent individuals. These forms of data injustice are often opaque, hidden behind secretive law enforcement practices or commercial secrecy agreements. We draw from neorepublican conceptions of domination and antipower to frame this legal and technological opacity as an abstraction of injustice. We argue that handing important criminal justice decision-making over to code and algorithms designed, owned, and maintained by private interests exacerbates the potential for the public deployment of unjust systems that subject individuals and communities to unwarranted, arbitrary, and uncontrolled state power. Such government interference represents clear forms of data injustice and domination.

Parameters of oculomotor activity and cerebral hemodynamics in students with different types of autonomic reactivity while performing cognitive tasks

BACKGROUND: At the current state of human development, with the high rates of knowledge accumulation and accelerated social and technical evolution, studying the psychophysiological features of cognitive activity within a limited time is essential. AIM: To examine oculomotor activity and cerebral hemodynamics parameters in students with different types of autonomic reactivity while performing cognitive tasks. MATERIALS AND METHODS: The study enrolled 110 students of the Northern (Arctic) Federal University named after M.V. Lomonosov (mean age, 19.0±0.5 years). To identify specific features of cognitive activity in various timing conditions, the type of autonomic nervous system reactivity was determined. Eye movements were tracked when solving cognitive tasks at various timing conditions. A rheoencephalogram was simultaneously recorded to assess cerebral hemodynamics. The efficiency of cognitive activity was assessed in various timing conditions with consideration of the identified individual typological features. RESULTS: The participants demonstrated different efficiency levels of cognitive activity when performing cognitive tasks at various timing conditions. Students with a sympathetic-tone type of autonomic reactivity revealed the most efficient and rapid processing of visual information under a time-constraint setting owing to the presence of a more stable relationship between oculomotor activity and cerebral hemodynamics parameters. Participants with parasympathetic-tone reactivity showed the lowest success rates in cognitive activity and the least stable relationships between oculomotor activity and cerebral hemodynamics. CONCLUSIONS: Young individuals with different types of autonomic reactivity exhibited general and specific alterations in cerebral hemodynamics and oculomotor activity when performing cognitive tasks under a time-constraint setting. They showed different efficiency levels of cognitive activity. Thus, human cognitive abilities under stress depend on the reactivity type of the nervous system. The specific changes in oculomotor reactions and cerebral hemodynamics parameters may be considered success markers of cognitive activity. The most successful cognitive activity under limited time conditions is ensured with a more stable statistical relationship between cerebral hemodynamics and oculomotor activity parameters. This is reflected in a stable factor model of visual cognitive activity, regardless of time limitations.

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.

Theory Is All You Need: AI, Human Cognition, and Causal Reasoning

Scholars argue that artificial intelligence (AI) can generate genuine novelty and new knowledge and, in turn, that AI and computational models of cognition will replace human decision making under uncertainty. We disagree. We argue that AI’s data-based prediction is different from human theory-based causal logic and reasoning. We highlight problems with the decades-old analogy between computers and minds as input–output devices, using large language models as an example. Human cognition is better conceptualized as a form of theory-based causal reasoning rather than AI’s emphasis on information processing and data-based prediction. AI uses a probability-based approach to knowledge and is largely backward looking and imitative, whereas human cognition is forward-looking and capable of generating genuine novelty. We introduce the idea of data–belief asymmetries to highlight the difference between AI and human cognition, using the example of heavier-than-air flight to illustrate our arguments. Theory-based causal reasoning provides a cognitive mechanism for humans to intervene in the world and to engage in directed experimentation to generate new data. Throughout the article, we discuss the implications of our argument for understanding the origins of novelty, new knowledge, and decision making under uncertainty.

Human adult neurogenesis loss corresponds with cognitive decline during epilepsy progression.

Mesial temporal lobe epilepsy (MTLE) is a syndromic disorder presenting with seizures and cognitive comorbidities. Although seizure etiology is increasingly understood, the pathophysiological mechanisms contributing to cognitive decline and epilepsy progression remain less recognized. We have previously shown that adult hippocampal neurogenesis dramatically declines in MTLE patients with increased disease duration. Here, we investigate when multiple cognitive domains become affected during epilepsy progression and how human neurogenesis levels contribute to it. We find that intelligence, verbal learning, and memory decline at a critical period of 20 years disease duration. In contrast to rodents, the number of human immature neurons positively associates with auditory verbal, rather than visuospatial, learning and memory. Moreover, this association does not apply to mature granule neurons. Our study provides cellular evidence of how adult neurogenesis corresponds with human cognition and signifies an opportunity to advance regenerative medicine for patients with MTLE and other cognitive disorders.

On Communal Transgressive Behaviour; A Moral Dilemma Variation on the Classic Asch Paradigm

Research suggests that communal violence constitutes an integral component of human social cognition under conditions in which in-group membership is highly desirable and uncertain for most in-group members. However, little remains known about the effects of social conformity dynamics on moral decision-making under these conditions. In this study, we adapt the Asch paradigm to study these effects in a cohort of cadets at the Czech University of Defence. The data suggests that social conformity dynamics strongly affect moral decision-making (rpb = .564, n = 87) and may be stronger in males than females. These findings highlight the need for specialized training programs to provide military personnel with the mental tools to recognize, deter and resist communal acts of unjustifiable violence.

Investigating the Triple Code Model in numerical cognition using stereotactic electroencephalography.

The ability to conceptualize numerical quantities is an essential human trait. According to the "Triple Code Model" in numerical cognition, distinct neural substrates encode the processing of visual, auditory, and non-symbolic numerical representations. While our contemporary understanding of human number cognition has benefited greatly from advances in clinical imaging, limited studies have investigated the intracranial electrophysiological correlates of number processing. In this study, 13 subjects undergoing stereotactic electroencephalography for epilepsy participated in a number recognition task. Drawing upon postulates of the Triple Code Model, we presented subjects with numerical stimuli varying in representation type (symbolic vs. non-symbolic) and mode of stimuli delivery (visual vs. auditory). Time-frequency spectrograms were dimensionally reduced with principal component analysis and passed into a linear support vector machine classification algorithm to identify regions associated with number perception compared to inter-trial periods. Across representation formats, the highest classification accuracy was observed in the bilateral parietal lobes. Auditory (spoken and beeps) and visual (Arabic) number formats preferentially engaged the superior temporal cortices and the frontoparietal regions, respectively. The left parietal cortex was found to have the highest classification for number dots. Notably, the putamen exhibited robust classification accuracies in response to numerical stimuli. Analyses of spectral feature maps revealed that non-gamma frequency, below 30 Hz, had greater-than-chance classification value and could be potentially used to characterize format specific number representations. Taken together, our findings obtained from intracranial recordings provide further support and expand on the Triple Code Model for numerical cognition.

Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect

AbstractIn many scientific fields, sparseness and indirectness of empirical evidence pose fundamental challenges to theory development. Theories of the evolution of human cognition provide a guiding example, where the targets of study are evolutionary processes that occurred in the ancestors of present-day humans. In many cases, the evidence is both very sparse and very indirect (e.g., archaeological findings regarding anatomical changes that might be related to the evolution of language capabilities); in other cases, the evidence is less sparse but still very indirect (e.g., data on cultural transmission in groups of contemporary humans and non-human primates). From examples of theoretical and empirical work in this domain, we distill five virtuous practices that scientists could aim to satisfy when evidence is sparse or indirect: (i) making assumptions explicit, (ii) making alternative theories explicit, (iii) pursuing computational and formal modelling, (iv) seeking external consistency with theories of related phenomena, and (v) triangulating across different forms and sources of evidence. Thus, rather than inhibiting theory development, sparseness or indirectness of evidence can catalyze it. To the extent that there are continua of sparseness and indirectness that vary across domains and that the principles identified here always apply to some degree, the solutions and advantages proposed here may generalise to other scientific domains.

Computational Locked and Unlocked Strategies and Dissipative Brains The Abductive Eco-Cognitive Perspective

<p dir="ltr"><span>Eco-cognitive computationalism is a cognitive science perspective that views computing in context, focusing on embodied, situated, and distributed cognition. It emphasizes the role of Turing in the development of the Logical Universal Machine and the concept of machines as “domesticated ignorant entities”. This perspective explains how machines can be dynamically active in distributed physical entities, allowing data to be encoded and decoded for appropriate results. In this perspective, we can clearly see that the concept of computation evolves over time due to historical and contextual factors, and it allows for the emergence of new types of computations that exploit new substrates. Taking advantage of this eco-cognitive framework I will also illustrate the concepts of “locked and unlocked strategies in deep learning systems, indicating different inference routines for creative results. Locked abductive strategies are characterized by poor hypothetical creative cognition due to the lack of what I call eco-cognitive openness, while unlocked human cognition involves higher kinds of creative abductive reasoning. This special kind of “openness” is physically rooted in the fundamental character of the human brain as an open system constantly coupled with the environment (that is an “open” or dissipative system): its activity is the uninterrupted attempt to achieve the equilibrium with the environment in which it is embedded, and this interplay can never be switched off without producing severe damage to the brain. Brain cannot be conceived as deprived of its physical quintessence which is its openness. In the brain, contrary to the computational case, ordering is not derived from the outside thanks to what I have called in a recent book “computational domestication of ignorant entities”, but it is the direct product of an “internal” open dynamical process of the system.</span></p><div><span><br /></span></div>

To die is ‘to run (away)’

Abstract Proto-Germanic *daw-ja-, the ultimate source of English to die, is here argued to have originally been a polysemous verb meaning ‘to run; to die’, corroborating its current etymological analysis as a reflex of the Proto-Indo-European root *dheu̯- ‘to run’. The proposal is supported by both well-known and previously unnoticed reflexes of the verb *daw-ja-in Gothic, Old Icelandic, and Old English, as well as by further Germanic lexical items and figurative expressions. Further support is provided by a series of semantic parallels in several Indo-European traditions, which, together with the Germanic material, reflect a well-known conceptual metaphor rooted in universal human cognition.

Leave Your Weapons at the Door: Introduction to the Special Issue on Modern Behavior Analyses of Human Language and Cognition

Leave Your Weapons at the Door: Introduction to the Special Issue on Modern Behavior Analyses of Human Language and Cognition

Smart In-Process Inspection in Human–Cyber–Physical Manufacturing Systems: A Research Proposal on Human–Automation Symbiosis and Its Prospects

This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely endorsed as the future of human–automation research, emphasizes closer partnership and mutually beneficial collaboration between human and automation agents. In addition, to maintain high product quality and enable the in-time feedback of process issues for advanced manufacturing, in-process inspection is an efficient strategy that manufacturers adopt. In this regard, this paper outlines a research framework combining smart in-process inspection and human–automation symbiosis, enabling real-time defect identification and process optimization with cognitive intelligence. Smart in-process inspection studies the effective automation of real-time inspection and defect mitigation using data-driven technologies and intelligent agents to foster adaptability in complex production environments. Concurrently, human–automation symbiosis focuses on achieving a symbiotic human–automation relationship through cognitive task allocation and behavioral nudges to enhance human–automation collaboration. It promotes a human-centered manufacturing paradigm by integrating the studies in advanced manufacturing systems, cognitive engineering, and human–automation interaction. This paper examines critical technical challenges, including defect inspection and mitigation, human cognition modeling for adaptive task allocation, and manufacturing nudging design and personalization. A research roadmap detailing the technical solutions to these challenges is proposed.

Icon similarity model based on cognition and deep learning

Human-computer cooperation guided by natural interaction, intelligent interaction, and human–computer integration is gradually becoming a new trend in human–computer interfaces. An icon is an indispensable pictographic symbol in an interface that can convey pivotal semantics between humans and computers. Research on similar icons’ cognition in humans and the discrimination of computers can reduce misunderstandings and facilitate transparent cooperation. Therefore, this research focuses on images of icons, extracted contours, and four features, including the curvature, proportion, orientation, and line of the contour, step by step. By manipulating the feature value change to obtain 360 similar icons, a cognitive experiment was conducted with 25 participants to explore the boundary values of the feature dimensions that cause different levels of similarity. Its boundary values were applied to deep learning to train a discrimination algorithm model that included 1500 similar icons. This dataset was used to train a Siamese neural network using a 16-layer network branch of a visual geometry group. The training process used stochastic gradient descent. This method of combining human cognition and deep learning technology is meaningful for establishing a consensus on icon semantics, including content and emotions, by outputting similarity levels and values. Taking icon similarity discrimination as an example, this study explored the analysis and simulation methods of computer vision for human visual cognition. The accuracy evaluated is 90.82%. The precision was evaluated as 90% for high, 80.65% for medium, and 97.30% for low. Recall was evaluated as 100% for high, 89.29% for medium, and 83.72% for low. It has been verified that it can compensate for fuzzy cognition in humans and enable computers to cooperate efficiently.

Bridging quantum physics and human cognition with Nishida’s logic of basho

This article aims to bridge quantum physics, human cognition, and Kitarō Nishida's logic of basho. The primary claim is that the indeterminacy underlying both quantum and cognitive phenomena is synonymous with Nishida’s “absolute nothingness”, the ontological basis of the logic. By interpreting Nishida’s soku hi dialectic in terms of phenomenal appearances in awareness, Nishida’s term “basho” is equated with this place where awareness actualises thereby allowing Nishida’s three basho to be framed in terms of the degree of subjectivity present within awareness. Connections between logic of basho and the theory of enactive cognition are set out with emphasis on how cognitive representation can be placed in the human’s enactive engagement with reality. As the logic of basho accounts for the dialectical nature of both quantum reality and cognitive reality, the article concludes that his logic can be seen as encompassing physical reality as well as the reality of (non-)subjective awareness.