Hasil untuk "Consciousness. Cognition"

Manat Kanagatov, Tatyana Seryozhkina, Zukhra Ismagambetova et al.

The purpose of this study was to identify the ontological and cultural foundations of the shamanic tradition in the Turkic culture of Kazakhstan through the concept of altered states of consciousness (ASC). The research focused on how ASC structured the shamanic worldview, shaped ritual practices, and transformed under post-traditional social conditions. The methodological framework combined philosophical analysis of consciousness, culturological interpretation of mythological and ritual structures, and the analysis of archaeological and ethnographic data. An interdisciplinary synthesis integrating philosophy, ethnology, archaeology, and symbolic analysis was applied, alongside sociocultural analysis and interpretative culturology to examine contemporary transformations of shamanic practice. The study established that ASC functioned as a normative and regulated mode of interaction with a multi-level reality. It operated as a tool of diagnosis, sacred cognition, and social regulation, grounded in stable symbolic forms. The shaman acted as a mediator between sacred and social dimensions, integrating individual experience with collective knowledge. Spatial and material elements of ritual preserved strictly defined symbolism rooted in a mythopoetic worldview. In modern contexts, shamanic tradition has transformed into a more individualized psycho-spiritual practice while retaining core symbolic and ritual codes. Archetypal structures of shamanism continue to persist in folklore, cultural memory, and representations of Kazakh identity. The practical significance of the study lies in its applicability to the interpretation of sacred practices within the Turkic tradition, culturally oriented approaches in ethnopsychology and symbolic anthropology, and the preservation of intangible cultural heritage.

Arnon Lotem, Joseph Y. Halpern

The evolution of cognition is frequently discussed as the evolution of cognitive abilities or the evolution of some neuronal structures in the brain. However, since such traits or abilities are often highly complex, understanding their evolution requires explaining how they could have gradually evolved through selection acting on heritable variations in simpler cognitive mechanisms. With this in mind, making use of a previously proposed theory, here we show how the evolution of cognitive abilities can be captured by the fine-tuning of basic learning mechanisms and, in particular, chunking mechanisms. We use the term chunking broadly for all types of non-elemental learning, claiming that the process by which elements are combined into chunks and associated with other chunks, or elements, is critical for what the brain can do, and that it must be fine-tuned to ecological conditions. We discuss the relevance of this approach to studies in animal cognition, using examples from animal foraging and decision-making, problem solving, and cognitive flexibility. Finally, we explain how even the apparent human-animal gap in sequence learning ability can be explained in terms of different fine-tunings of a similar chunking process.

Assma Habadi, Milos Zefran, Lijuan Yin et al.

Background and Objectives: This paper focuses on using AI to assess the cognitive function of older adults with mild cognitive impairment or mild dementia using physiological data provided by a wearable device. Cognitive screening tools are disruptive, time-consuming, and only capture brief snapshots of activity. Wearable sensors offer an attractive alternative by continuously monitoring physiological signals. This study investigated whether physiological data can accurately predict scores on established cognitive tests. Research Design and Methods: We recorded physiological signals from 23 older adults completing three NIH Toolbox Cognitive Battery tests, which assess working memory, processing speed, and attention. The Empatica EmbracePlus, a wearable device, measured blood volume pulse, skin conductance, temperature, and movement. Statistical features were extracted using wavelet-based and segmentation methods. We then applied supervised learning and validated predictions via cross-validation, hold-out testing, and bootstrapping. Results: Our models showed strong performance with Spearman's ρof 0.73-0.82 and mean absolute errors of 0.14-0.16, significantly outperforming a naive mean predictor. Sensor roles varied: heart-related signals combined with movement and temperature best predicted working memory, movement paired with skin conductance was most informative for processing speed, and heart in tandem with skin conductance worked best for attention. Discussion and Implications: These findings suggest that wearable sensors paired with AI tools such as supervised learning and feature engineering can noninvasively track specific cognitive functions in older adults, enabling continuous monitoring. Our study demonstrates how AI can be leveraged when the data sample is small. This approach may support remote assessments and facilitate clinical interventions.

Rui Mao, Qian Liu, Xiao Li et al.

Cognitive Science has profoundly shaped disciplines such as Artificial Intelligence (AI), Philosophy, Psychology, Neuroscience, Linguistics, and Culture. Many breakthroughs in AI trace their roots to cognitive theories, while AI itself has become an indispensable tool for advancing cognitive research. This reciprocal relationship motivates a comprehensive review of the intersections between AI and Cognitive Science. By synthesizing key contributions from both perspectives, we observe that AI progress has largely emphasized practical task performance, whereas its cognitive foundations remain conceptually fragmented. We argue that the future of AI within Cognitive Science lies not only in improving performance but also in constructing systems that deepen our understanding of the human mind. Promising directions include aligning AI behaviors with cognitive frameworks, situating AI in embodiment and culture, developing personalized cognitive models, and rethinking AI ethics through cognitive co-evaluation.

Daria D. Tyurina, Sergey V. Stasenko, Konstantin V. Lushnikov et al.

This study introduces a novel method for predicting cognitive age using psychophysiological tests. To determine cognitive age, subjects were asked to complete a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. Based on the tests completed, the average completion time, proportion of correct answers, average absolute delta of the color campimetry test, number of guessed words in the Münsterberg matrix, and other parameters were calculated for each subject. The obtained characteristics of the subjects were preprocessed and used to train a machine learning algorithm implementing a regression task for predicting a person's cognitive age. These findings contribute to the field of remote screening using mobile devices for human health for diagnosing and monitoring cognitive aging.

Andrei Khrennikov, Atsushi Iriki, Irina Basieva

Quantum-like (QL) modeling, one of the outcomes of the quantum information revolution, extends quantum theory methods beyond physics to decision theory and cognitive psychology. While effective in explaining paradoxes in decision making and effects in cognitive psychology, such as conjunction, disjunction, order, and response replicability, it lacks a direct link to neural information processing in the brain. This study bridges neurophysiology, neuropsychology, and cognitive psychology, exploring how oscillatory neuronal networks give rise to QL behaviors. Inspired by the computational power of neuronal oscillations and quantum-inspired computation (QIC), we propose a quantum-theoretical framework for coupling of cognition/decision making and neural oscillations - {\it QL oscillatory cognition.} This is a step, may be very small, towards clarification of the relation between mind and matter and the nature of perception and cognition. We formulate four conjectures within QL oscillatory cognition and in principle they can checkedAsanoexperimentally. But such experimental tests need further theoretical and experimental elaboration. One of the conjectures (Conjecture 4) is on resolution of the binding problem by exploring QL states entanglement generated by the oscillations in a few neuronal networks. Our findings suggest that fundamental cognitive processes align with quantum principles, implying that humanoid AI should process information using quantum-theoretic laws. Quantum-Like AI (QLAI) can be efficiently realized via oscillatory networks performing QIC.

Rick den Otter, Gabriel Weindel, Sjoerd Stuit et al.

Understanding the sequence of cognitive operations that underlie decision-making is a fundamental challenge in cognitive neuroscience. Traditional approaches often rely on group-level statistics, which obscure trial-by-trial variations in cognitive strategies. In this study, we introduce a novel machine learning method that combines Hidden Multivariate Pattern analysis with a Structured State Space Sequence model to decode cognitive strategies from electroencephalography data at the trial level. We apply this method to a decision-making task, where participants were instructed to prioritize either speed or accuracy in their responses. Our results reveal an additional cognitive operation, labeled Confirmation, which seems to occur predominantly in the accuracy condition but also frequently in the speed condition. The modeled probability that this operation occurs is associated with higher probability of responding correctly as well as changes of mind, as indexed by electromyography data. By successfully modeling cognitive operations at the trial level, we provide empirical evidence for dynamic variability in decision strategies, challenging the assumption of homogeneous cognitive processes within experimental conditions. Our approach shows the potential of sequence modeling in cognitive neuroscience to capture trial-level variability that is obscured by aggregate analyses. The introduced method offers a new way to detect and understand cognitive strategies in a data-driven manner, with implications for both theoretical research and practical applications in many fields.

Ziwei Cheng, Amelia D. Moser, Jenna Jones et al.

Depression is a prevalent psychiatric condition that commonly emerges in adolescence and young adulthood and is associated with reward processing abnormalities. The Probabilistic Reward Task (PRT) is widely used to investigate the impact of depression on reward processing, but prior studies have not comprehensively addressed the reinforcement learning and decision-making mechanisms involved in the task. In 726 adolescents and young adults with varying levels of depression, we collected PRT data and applied a novel computational model with response-outcome learning and evidence accumulation processes to provide new insights into the cognitive processes implicated in depression. Compared to participants with no history of psychopathology, those with depressive disorders showed reduced impact of learned response values on decision bias toward the more frequently rewarded action. In addition, higher levels of anhedonia were associated with slower evidence accumulation during decision-making. Together, these findings improved our understanding of the reinforcement learning and decision-making mechanisms assessed by the PRT and their associations with depression.

Denise Fernandes Barbosa, Carmen Cristina Carvalho Falcon, Maria de Lourdes Rabelo Guimarães et al.

This study aims to characterize dentists in Brazil who understand and practice Sleep Dentistry (SD), focusing on diagnosing and managing sleep-related breathing disorders (SRBD), such as snoring and obstructive sleep apnea. Our goal is to identify gaps in their training and emphasize the need to integrate dental sleep into the Brazilian system of education. We conducted a descriptive study with 161 Brazilian sleep dentists. Participants answered questionnaires with demographics, educational background, clinical experience, and perspectives on recognizing SD as a specialty. Our findings show that 57.8% of participants graduated from public universities. Many expressed a strong interest in receiving training on SD, highlighting difficulties in effectively managing SRBD. Additionally, they reported the importance of healthcare professionals in improving treatment outcomes for SRBD. This study highlights the need to incorporate sleep health education into dental training programs in Brazil. Aligning these educational improvements with the United Nations in promoting health and well-being, we urge policymakers to formally recognize SD as a key specialty, fostering stronger collaboration between dentists and physicians to enhance patient care and public health.

Monika Lohani, Monika Lohani, Wei Wei et al.

The climate change crisis continues to have interrelated health, economic, and societal consequences; yet how people psychologically manage these challenges remains underexplored. Specifically, people may have distinct ways of dealing with the realities of climate change, which can impact their wellbeing and influence their engagement in climate action. Thus, the current work aimed to evaluate how people manage their cognition and behavior specific to climate change. We developed and validated a new comprehensive measure called ECO-SHADOW to assess regulated responses to climate change. The existing literature on climate change was integrated with theoretical perspectives from affect regulation literature to generate potential strategies for managing cognitions and behaviors. Based on data collected (N = 566), exploratory factor analysis identified nine affect regulation factors underlying nearly 60 strategies: Eco-consciousness, Conflict, Outcast, Spirituality, Hope, Apathy, Doom, Overplay, and Withdrawal. The ECO-SHADOW inventory is a reliable, valid, and currently the most exhaustive measure of the wide-ranging cognitive and behavioral regulation strategies employed to process climate change challenges, with some being more adaptive toward climate action (such as eco-consciousness and hope), while others being maladaptive (including apathy, withdrawal, doom, or overplay). Further work is needed to examine how affect regulation efforts relate to addressing the climate change crisis. We hope that the ECO-SHADOW inventory inspires future research promoting effective affect regulation and its connections to sustainable climate action.

Sirinun Chaipunko, Watthanaree Ammawat, Keerathi Oanmun et al.

As cognitive interventions for older adults evolve, modern technologies are increasingly integrated into their development. This study investigates the efficacy of augmented reality (AR)-based physical-cognitive training using an interactive game with Kinect motion sensor technology on older individuals at risk of mild cognitive impairment. Utilizing a pretest-posttest experimental design, twenty participants (mean age 66.8 SD. = 4.6 years, age range 60-78 years) underwent eighteen individual training sessions, lasting 45 to 60 minutes each, conducted three times a week over a span of 1.5 months. The training modules from five activities, encompassing episodic and working memory, attention and inhibition, cognitive flexibility, and speed processing, were integrated with physical movement and culturally relevant Thai-context activities. Results revealed significant improvements in inhibition, cognitive flexibility, accuracy, and reaction time, with working memory demonstrating enhancements in accuracy albeit not in reaction time. These findings underscore the potential of AR interventions to bolster basic executive enhancement among community-dwelling older adults at risk of cognitive decline.

Adán Flores-Ramírez, Ángel Mario Alarcón-López, Sofía Vaca-Narvaja et al.

Beyond conventional productivity metrics, human interaction and collaboration dynamics merit careful consideration in our increasingly digital workspace. This research proposes a conjectural neuro-adaptive room that enhances group interactions by adjusting the physical environment to desired internal states. Drawing inspiration from previous work on collective consciousness, the system leverages computer vision and machine learning models to analyze physiological and behavioral cues, such as facial expressions and speech analysis, to infer the overall internal state of occupants. Environmental conditions of the room, such as visual projections, lighting and sound, are actively adjusted to create an optimal setting for inducing the desired state, including focus or collaboration. Our goal is to create a dynamic and responsive environment to support group needs, fostering a sense of collective consciousness and improving workplace well-being.

Diederik Aerts, Roberto Leporini, Sandro Sozzo

We have proved in both human-based and computer-based tests that natural concepts generally 'entangle' when they combine to form complex sentences, violating the rules of classical compositional semantics. In this article, we present the results of an innovative video-based cognitive test on a specific conceptual combination, which significantly violates the Clauser-Horne-Shimony-Holt version of Bell's inequalities ('CHSH inequality'). We also show that collected data can be faithfully modeled within a quantum-theoretic framework elaborated by ourselves and a 'strong form of entanglement' occurs between the component concepts. While the video-based test confirms previous empirical results on entanglement in human cognition, our empirical approach surpasses language barriers and eliminates the need for prior knowledge, enabling universal accessibility. Finally, this transformative methodology allows one to unravel the underlying connections that drive our perception of reality. As a matter of fact, we provide a novel explanation for the appearance of entanglement in both physics and cognitive realms.

Marian E. Berryhill

Grace Blackburn, Benjamin J. Ashton, Alex Thornton et al.

Abstract Despite considerable research into the structure of cognition in non-human animal species, there is still much debate as to whether animal cognition is organised as a series of discrete domains or an overarching general cognitive factor. In humans, the existence of general intelligence is widely accepted, but less work has been undertaken in animal psychometrics to address this question. The relatively few studies on non-primate animal species that do investigate the structure of cognition rarely include tasks assessing social cognition and focus instead on physical cognitive tasks. In this study, we tested 36 wild Western Australian magpies (Gymnorhina tibicen dorsalis) on a battery of three physical (associative learning, spatial memory, and numerical assessment) and one social (observational spatial memory) cognitive task, to investigate if cognition in this species fits a general cognitive factor model, or instead one of separate physical and social cognitive domains. A principal component analysis (PCA) identified two principal components with eigenvalues exceeding 1; a first component onto which all three physical tasks loaded strongly and positively, and a second component onto which only the social task (observational spatial memory) loaded strongly and positively. These findings provide tentative evidence for separate physical and social cognitive domains in this species, and highlight the importance of including tasks assessing both social and physical cognition in cognitive test batteries.

Laure Cauchard, Pierre Bize, Blandine Doligez

Abstract Although problem-solving tasks are frequently used to assess innovative ability, the extent to which problem-solving performance reflects variation in cognitive skills has been rarely formally investigated. Using wild breeding great tits facing a new non-food motivated problem-solving task, we investigated the role of associative learning in finding the solution, compared to multiple other non-cognitive factors. We first examined the role of accuracy (the proportion of contacts made with the opening part of a string-pulling task), neophobia, exploration, activity, age, sex, body condition and participation time on the ability to solve the task. To highlight the effect of associative learning, we then compared accuracy between solvers and non-solvers, before and after the first cue to the solution (i.e., the first time they pulled the string opening the door). We finally compared accuracy over consecutive entrances for solvers. Using 884 observations from 788 great tits tested from 2010 to 2015, we showed that, prior to initial successful entrance, solvers were more accurate and more explorative than non-solvers, and that females were more likely to solve the task than males. The accuracy of solvers, but not of non-solvers, increased significantly after they had the opportunity to associate string pulling with the movement of the door, giving them a first cue to the task solution. The accuracy of solvers also increased over successive entrances. Our results demonstrate that variations in problem-solving performance primarily reflect inherent individual differences in associative learning, and are also to a lesser extent shaped by sex and exploratory behaviour.

B. Baars, Natalie Geld, R. Kozma

In this work, we provide a brief overview of Global Workspace Theory (GWT), along with recent developments and clarifications of modern neuroscientific evidence. GWT started in the 1980s as a purely psychological theory of conscious cognition, and has become a prominent approach in scientific studies of consciousness (Mashour et al., 2020). Based on today’s far more detailed understanding of the brain, GWT has adapted to new waves of evidence. The brain-based version of GWT is called Global Workspace Dynamics (GWD) (Baars et al., 2013; Baars and Geld, 2019) precisely because the cortex is viewed as a “unified oscillatory machine” (Steriade, 1999). GWT therefore joins other theories in viewing consciousness as the product of highly integrated and widespread cortico-thalamic (C-T) activity, following a long trail of evidence (Dehaene et al., 1998). Here we aim to clarify some empirical questions that have been raised, and review evidence that the prefrontal and posterior regions support dynamic global workspace functions, in agreement with several other authors. Static, gross anatomical divisions are superseded by the dynamical connectome of cortex. We aim to correct the following misunderstandings. In a recent paper, Raccah et al. (2021) claimed that the prefrontal cortex (PfC) is not causally involved in enabling consciousness, based on a review of intracranial electrical stimulation (iES) experiments.We will show that Raccah et al.’s claim that the prefrontal cortex (PfC) does not support consciousness is incorrect. The brain evidence is now compelling that PfC indeed participates in the visual conscious stream, for example, and excellent evidence for that has emerged in recent years. We discuss the additional evidence and how that has a direct bearing on the PfC. We also respond to Raccah et al.’s (2021) mistaken claim about the role of the prefrontal cortex and GWT. GWT does not assert that the prefrontal cortex (PfC) is essential for conscious vision, nor does it deny a role for the prefrontal lobe. The 1988 version of GWT made no assertions about the role of cortex in consciousness. These claims are mistaken, and indeed, self contradictory. In addition, this integrated conception of cortex also answers counterclaims about consciousness and metacognition; therefore we address some misunderstandings about metacognition in the Global Workspace “family” of theories (Shea and Frith, 2019). Shea and Frith (2019) proposed that “The Global Workspace Needs Metacognition.” However, in 1988 Baars already described two varieties of metacognition that are implied by GWT (Baars, 1988; Baars and Geld, 2019). Here we have three objectives.

Luzhou Zhang

The Clock-Drawing test is a well known and widely used neuropsychological metric to assess basic cognitive function. My objective is to combine methods of machine learning in computer vision and image analysis to predict a subject's level of cognitive impairment.

Ethan M. Campbell, Garima Singh, Eric D. Claus et al.

Background: Hazardous drinking is associated with maladaptive alcohol-related decision-making. Existing studies have often focused on how participants learn to exploit familiar cues based on prior reinforcement, but little is known about the mechanisms that drive hazardous drinkers to explore novel alcohol cues when their value is not known. Methods: We investigated exploration of novel alcohol and non-alcohol cues in hazardous drinkers (N = 27) and control participants (N = 26) during electroencephalography (EEG). A normative computational model with two free parameters was fit to estimate participants’ weighting of the future value of exploration and immediate value of exploitation. Results: Hazardous drinkers demonstrated increased exploration of novel alcohol cues, and conversely, increased probability of exploiting familiar alternatives instead of exploring novel non-alcohol cues. The motivation to explore novel alcohol stimuli in hazardous drinkers was driven by an elevated relative future valuation of uncertain alcohol cues. P3a predicted more exploratory decision policies driven by an enhanced relative future valuation of novel alcohol cues. P3b did not predict choice behavior, but computational parameter estimates suggested that hazardous drinkers with enhanced P3b to alcohol cues were likely to learn to exploit their immediate expected value. Conclusions: Hazardous drinkers did not display atypical choice behavior, different P3a/P3b amplitudes, or computational estimates to novel non-alcohol cues—diverging from previous studies in addiction showing atypical generalized explore-exploit decisions with non-drug-related cues. These findings reveal that cue-specific neural computations may drive aberrant alcohol-related decision-making in hazardous drinkers—highlighting the importance of drug-relevant cues in studies of decision-making in addiction.

Andrew A. Fingelkurts, Alexander A Fingelkurts

Medical well-regarded policy recommendations for patients with disorders of consciousness (DoC) are almost exclusively relied on behavioural examination and evaluation of higher-order cognition, and largely disregard the patients’ self. This is so because practically establishing the presence of self-awareness or Selfhood is even more challenging than evaluating the presence of consciousness. At the same time, establishing the potential (actual physical possibility) of Selfhood in DoC patients is crucialy important from clinical, ethical, and moral standpoints because Selfhood is the most central and private evidence of being an independent and free agent that unites intention, embodiment, executive functions, attention, general intelligence, emotions and other components within the intra-subjective frame (first-person givenness). The importance of Selfhood is supported further by the observation that rebooting of self-awareness is the first step to recovery after brain damage. It seems that complex experiential Selfhood can be plausibly conceptualized within the Operational Architectonics (OA) of brain-mind functioning and reliably measured by quantitative electroencephalogram (qEEG) operational synchrony.