Hasil untuk "Consciousness. Cognition"

Zhiye Jin, Yibai Li, K. D. Joshi et al.

This study presents the development of the PsyCogMetrics AI Lab (psycogmetrics.ai), an integrated, cloud-based platform that operationalizes psychometric and cognitive-science methodologies for Large Language Model (LLM) evaluation. Framed as a three-cycle Action Design Science study, the Relevance Cycle identifies key limitations in current evaluation methods and unfulfilled stakeholder needs. The Rigor Cycle draws on kernel theories such as Popperian falsifiability, Classical Test Theory, and Cognitive Load Theory to derive deductive design objectives. The Design Cycle operationalizes these objectives through nested Build-Intervene-Evaluate loops. The study contributes a novel IT artifact, a validated design for LLM evaluation, benefiting research at the intersection of AI, psychology, cognitive science, and the social and behavioral sciences.

Miriam Kuspiel, Arnout Lindeman, Marc Naguib et al.

Abstract Animals use vocal signals to provide information across a wide range of contexts. However, it is more complex to identify the information content when the same vocalizations are produced in different contexts, such as against predators and conspecific competitors. This raises the question whether information about the caller or context can be coded in relatively subtle variation within the call type and whether receivers respond differentially to such variation. Using playbacks of the general ‘chatter’ alarm call of Eurasian magpies (Pica pica), we tested whether or not territorial magpies respond differently to variation in call duration and rate. We show that magpies responded acoustically faster to chatters of longer duration, which may signal a greater motivation or urgency of territorial intruders. Alternatively, a delay in chatter response to shorter calls may reflect a period of hesitation and risk avoidance. Interestingly, magpies did not approach the loudspeaker more closely in response to longer calls and the total chatter response did not differ either. This suggests that after the short initial response, the persistence of the signal over time as well as visual information on the level or type of danger become essential for more differentiated response behaviours. Taken together, our results show that magpies perceive and respond differentially to variation in alarm call duration, suggesting that such variation encodes meaningful information.

Dishari Dasgupta, Arnab Banerjee, Akash Dutta et al.

Abstract Urbanization has drastically altered natural habitats, forcing non-human primates (NHPs) to adapt to human-modified environments. This study examines one such adaptation undertaken by free-ranging Hanuman langurs residing in Dakshineswar, a temple area and a tourist hub in West Bengal. Our observations reveal that they have come up with solicitation behaviors resembling human begging wherein they ask for food from nearby humans using various gestures. Notably, we identified seven distinct ‘begging’ gestures, with those involving embracing human legs and pulling their clothes having strong correlation with successful outcome. Moreover, adult female langurs predominantly initiate such gestures, with successful solicitation events peaking during evening sessions. The findings underscore langurs' adaptive capacity to exploit anthropogenic resources, where they have started to associate human beings as their food source. This study sheds light on primate behavior in urban landscapes, adding further evidence to the complex dynamics of human-monkey interactions.

Jonathan W. Pool, Wendy L. Magee, Richard J. Siegert et al.

Severe brain injuries in children and young people can result in disorders of consciousness. This can pose significant challenges for the brain injury survivor as they may struggle to show awareness; for their family, who want to help their child to recover consciousness; and for the team providing treatment and care for them, who need an assessment that will inform optimal treatment and care planning. Currently, there is a paucity of fully validated behavioral tools to assess consciousness in 2–18-year-olds. Assessing awareness across this age range is challenging and complex due to neurodevelopmental changes that occur during maturation. This study evaluated the face validity of a music-based behavioral assessment for children and young people with disorders of consciousness. This is known as the Music therapy Sensory Instrument for Cognition, Consciousness and Awareness (MuSICCA). The study recruited 20 participants to compose a mixed cohort of music therapists, non-music therapy healthcare professionals and family members with lived experience of caring for a child or young person with a disorder of consciousness. These participants reviewed the MuSICCA and evaluated its suitability as an assessment of consciousness for use with children and young people. They provided feedback by rating their level of agreement with two statements and they also described the perceived strengths and limitations of the MuSICCA. The results showed substantial agreement among raters that the MuSICCA appears to be an assessment of consciousness and awareness, and that the MuSICCA appears to be suitable for use with children and young people. Its strengths include being rigorous, comprehensive, providing guidance and opportunity for caregiver involvement, its use of salience in stimulation, and its utility in supporting the wider clinical and care teams. The findings suggest that the MuSICCA may be a valuable assessment tool in providing treatment and care for children and young people with disorders of consciousness and their families.

Yiming Liu, Yuran Liu, Wei Liang

Abstract The aim of this study was to investigate the ability of barn swallows (Hirundo rustica) to recognize humans. A field study was conducted in Caoyang Village, Zhanjiang City, Guangdong Province, South China. We assessed the responses of female barn swallows to the recognition of different types of human individuals by measuring their flight initiation distance (FID) when they incubated eggs in the nests. Our results demonstrated that barn swallows can identify the householder where their nest is located, displaying lower FID when the householder approaches, compared to an unfamiliar experimenter. Furthermore, there was no significant difference in FID between swallows reacting to householders who were rarely at home versus those who were frequently present, suggesting that barn swallows may possess the capability to recognize and retain memory of individual humans over time. Our findings provide evidence that barn swallows exhibit remarkable cognitive abilities. The long-standing symbiotic relationship between barn swallows and humans provides a unique model for studying the adaptation of species to environments with close human interactions. Research on their behavior and survival strategies can offer insights into the influence of symbiotic relationships on species adaptability and evolution.

Laha Ale

Human cognition spans perception, memory, intuitive judgment, deliberative reasoning, action selection, and social inference, yet these capacities are often explained through distinct computational theories. Here we present a unified mathematical framework in which diverse cognitive processes emerge from a single geometric principle. We represent the cognitive state as a point on a differentiable manifold endowed with a learned Riemannian metric that encodes representational constraints, computational costs, and structural relations among cognitive variables. A scalar cognitive potential combines predictive accuracy, structural parsimony, task utility, and normative or logical requirements. Cognition unfolds as the Riemannian gradient flow of this potential, providing a universal dynamical law from which a broad range of psychological phenomena arise. Classical dual-process effects--rapid intuitive responses and slower deliberative reasoning--emerge naturally from metric-induced anisotropies that generate intrinsic time-scale separations and geometric phase transitions, without invoking modular or hybrid architectures. We derive analytical conditions for these regimes and demonstrate their behavioural signatures through simulations of canonical cognitive tasks. Together, these results establish a geometric foundation for cognition and suggest guiding principles for the development of more general and human-like artificial intelligence systems.

Herman Cappelen, Josh Dever

This work defends the 'Whole Hog Thesis': sophisticated Large Language Models (LLMs) like ChatGPT are full-blown linguistic and cognitive agents, possessing understanding, beliefs, desires, knowledge, and intentions. We argue against prevailing methodologies in AI philosophy, rejecting starting points based on low-level computational details ('Just an X' fallacy) or pre-existing theories of mind. Instead, we advocate starting with simple, high-level observations of LLM behavior (e.g., answering questions, making suggestions) -- defending this data against charges of metaphor, loose talk, or pretense. From these observations, we employ 'Holistic Network Assumptions' -- plausible connections between mental capacities (e.g., answering implies knowledge, knowledge implies belief, action implies intention) -- to argue for the full suite of cognitive states. We systematically rebut objections based on LLM failures (hallucinations, planning/reasoning errors), arguing these don't preclude agency, often mirroring human fallibility. We address numerous 'Games of Lacks', arguing that LLMs do not lack purported necessary conditions for cognition (e.g., semantic grounding, embodiment, justification, intrinsic intentionality) or that these conditions are not truly necessary, often relying on anti-discriminatory arguments comparing LLMs to diverse human capacities. Our approach is evidential, not functionalist, and deliberately excludes consciousness. We conclude by speculating on the possibility of LLMs possessing 'alien' contents beyond human conceptual schemes.

Xin Li

Intelligence is fundamentally non-ergodic: it emerges not from uniform sampling or optimization from scratch, but from the structured reuse of prior inference trajectories. We introduce Memory-Amortized Inference (MAI) as a formal framework in which cognition is modeled as inference over latent cycles in memory, rather than recomputation through gradient descent. MAI systems encode inductive biases via structural reuse, minimizing entropy and enabling context-aware, structure-preserving inference. This approach reframes cognitive systems not as ergodic samplers, but as navigators over constrained latent manifolds, guided by persistent topological memory. Through the lens of delta-homology, we show that MAI provides a principled foundation for Mountcastle's Universal Cortical Algorithm, modeling each cortical column as a local inference operator over cycle-consistent memory states. Furthermore, we establish a time-reversal duality between MAI and reinforcement learning: whereas RL propagates value forward from reward, MAI reconstructs latent causes backward from memory. This inversion paves a path toward energy-efficient inference and addresses the computational bottlenecks facing modern AI. MAI thus offers a unified, biologically grounded theory of intelligence based on structure, reuse, and memory. We also briefly discuss the profound implications of MAI for achieving artificial general intelligence (AGI).

Arya Adyasha, Anushka Sanjay Shelke, Haroon R Lone

Social anxiety disorder (SAD) is associated with heightened physiological arousal in social-evaluative contexts, but it remains unclear whether such autonomic reactivity extends to non-evaluative cognitive stressors. This study investigated electrodermal activity (EDA) patterns in socially anxious (SA) and non-socially anxious (NSA) individuals during an emotionally salient 2-back working memory task using facial expressions. 50 participants (25 SA, 25 NSA) completed both a baseline rest period and the task while EDA data were collected via the Shimmer3 GSR+ sensor. A range of EDA features, such as tonic and phasic components, number and amplitude of skin conductance responses, and sympathetic activation estimates, were analyzed using a standardized, interval-based approach. Results revealed significant increases in EDA across all participants from baseline to task, indicating elevated autonomic arousal during cognitive load. However, no significant group differences were found between SA and NSA individuals. These findings suggest that cognitive-emotional stress, in the absence of social-evaluative threat, elicits comparable physiological responses regardless of social anxiety status. The results underscore the context-dependent nature of anxiety-related autonomic reactivity and advocate for the inclusion of social-evaluative or recovery phases in future research to detect more nuanced group effects.

Weichen Dai, Yuxuan Huang, Li Zhu et al.

Humans possess a remarkable capacity for spatial cognition, allowing for self-localization even in novel or unfamiliar environments. While hippocampal neurons encoding position and orientation are well documented, the large-scale neural dynamics supporting spatial representation, particularly during naturalistic, passive experience, remain poorly understood. Here, we demonstrate for the first time that non-invasive brain-computer interfaces (BCIs) based on electroencephalography (EEG) can decode spontaneous, fine-grained egocentric 6D pose, comprising three-dimensional position and orientation, during passive viewing of egocentric video. Despite EEG's limited spatial resolution and high signal noise, we find that spatially coherent visual input (i.e., continuous and structured motion) reliably evokes decodable spatial representations, aligning with participants' subjective sense of spatial engagement. Decoding performance further improves when visual input is presented at a frame rate of 100 ms per image, suggesting alignment with intrinsic neural temporal dynamics. Using gradient-based backpropagation through a neural decoding model, we identify distinct EEG channels contributing to position -- and orientation specific -- components, revealing a distributed yet complementary neural encoding scheme. These findings indicate that the brain's spatial systems operate spontaneously and continuously, even under passive conditions, challenging traditional distinctions between active and passive spatial cognition. Our results offer a non-invasive window into the automatic construction of egocentric spatial maps and advance our understanding of how the human mind transforms everyday sensory experience into structured internal representations.

Yin Jun Phua

The search for reliable indicators of consciousness has fragmented into competing theoretical camps (Global Workspace Theory (GWT), Integrated Information Theory (IIT), and Higher-Order Theories (HOT)), each proposing distinct neural signatures. We adopt a synthetic neuro-phenomenology approach: constructing artificial agents that embody these mechanisms to test their functional consequences through precise architectural ablations impossible in biological systems. Across three experiments, we report dissociations suggesting these theories describe complementary functional layers rather than competing accounts. In Experiment 1, a no-rewire Self-Model lesion abolishes metacognitive calibration while preserving first-order task performance, yielding a synthetic blindsight analogue consistent with HOT predictions. In Experiment 2, workspace capacity proves causally necessary for information access: a complete workspace lesion produces qualitative collapse in access-related markers, while partial reductions show graded degradation, consistent with GWT's ignition framework. In Experiment 3, we uncover a broadcast-amplification effect: GWT-style broadcasting amplifies internal noise, creating extreme fragility. The B2 agent family is robust to the same latent perturbation; this robustness persists in a Self-Model-off / workspace-read control, cautioning against attributing the effect solely to $z_{\text{self}}$ compression. We also report an explicit negative result: raw perturbational complexity (PCI-A) decreases under the workspace bottleneck, cautioning against naive transfer of IIT-adjacent proxies to engineered agents. These results suggest a hierarchical design principle: GWT provides broadcast capacity, while HOT provides quality control. We emphasize that our agents are not conscious; they are reference implementations for testing functional predictions of consciousness theories.

Sumbul Ansari, Saurabh Sharma

Objective This study aimed to evaluate the status of sleep, chronotype, and related variables of university athletes with and without chronic low back pain (CLBP), to find the correlation between CLBP, sleep difficulty score (SDS), and chronotype, and to determine if SDS and chronotype predict CLBP.

Richard D. Wright, Amelia C. Pellaers, Ryan T. deKergommeaux

The failure to notice changes to objects is called change blindness, and it is often studied with the flicker task. Observers performing this task see two rapidly alternating but slightly different stimulus displays that are usually photos of real-world scenes. In order to detect the change, they must compare objects in the pre-change scene with objects at the same locations in the post-change scene to determine whether they are the same or different. It has been proposed that change blindness can occur when the memory representation of a pre-change object is incomplete and thereby impairs the same/different comparison with the post-change object at the same location. It has also been proposed that even with intact pre-change object memory representations, failure of same/different comparisons for other reasons can cause change blindness. The goal of the current study was to conduct flicker task experiments to examine both proposals. We conducted the current experiments with non-photographic stimuli, varied the degree of feature-based change of colored lines and found that the greater degree of change, the faster the same/different comparisons, and the faster that changes were detected. We also examined the representation integrity account of change blindness by comparing detection times of target objects that underwent a single feature change with those that underwent multiple sequential feature changes. The latter were detected faster, which suggests that multiple identities of these sequentially changing objects were stored in memory and facilitated change detection. In another experiment we found that objects that underwent multiple sequential feature changes were not detected as fast as those that underwent multiple simultaneous feature changes. This is consistent with the representation account of change blindness and suggests that memories of multiple sequentially changing object identities are transient and may become less complete over time. And more generally that multiple simultaneous and multiple sequential feature-based changes to these stimuli can show the extent to which memory is involved when searching for flicker task targets. The results of the current study indicate that both the comparison failure and the representation integrity proposals can account for change blindness.

Mostyn W. Jones, Tam Hunt

How do brains create all our different colors, pains, and other conscious qualities? These various qualia are the most essential aspects of consciousness. Yet standard neuroscience (primarily based on synaptic information processing) has not found the synaptic-firing codes, sometimes described as the “spike code,” to account for how these qualia arise and how they unite to form complex perceptions, emotions, et cetera. Nor is it clear how to get from these abstract codes to the qualia we experience. But electromagnetic field (versus synaptic) approaches to how qualia arise have been offered in recent years by Pockett, McFadden, Jones, Bond, Ward and Guevera, Keppler and Shani, Hunt and Schooler, et cetera. These EM-field approaches show promise in offering more viable accounts of qualia. Yet, until now, they have not been evaluated together. We review various EM field theories of qualia, highlight their strengths and weaknesses, and contrast these theories with standard neuroscience approaches.

Naomi H. Feldman, Sharon Goldwater, Emmanuel Dupoux et al.

Luyao Chen, Zhiqiang Chen, Longsheng Jiang et al.

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.

Oryan Zacks, S. Ginsburg, E. Jablonka

We discuss the evolution of imagination in vertebrate animals within the framework of an evolutionary-transition approach. We define imaginative consciousness and the cognitive architecture that constitutes it and argue that the evolution of full-fledged imaginative consciousness that enables planning can be regarded as a major transition in the evolution of cognition. We explore the distribution and scope of a core capacity of imaginative cognition in non-human vertebrates — episodic-like memory (ELM) — by examining its behavioural manifestations as well as the organization and connectivity of the hippocampus, a central hub of episodic memory processes in vertebrates. Although the data are limited, we conclude that ELM evolved in parallel several times through the enrichment of minimal consciousness capacities, that there is a general correspondence between enhanced behavioural capacities and the size and complexity of the hippocampus during vertebrate evolution, and that the evolution of prospective, planning-enabling imagination is a major transition in cognition and consciousness.

Constance de Saint Laurent, Gillian Murphy, Karen Hegarty et al.

Abstract Misinformation has been a pressing issue since the beginning of the COVID-19 pandemic, threatening our ability to effectively act on the crisis. Nevertheless, little is known about the actual effects of fake news on behavioural intentions. Does exposure to or belief in misinformation about COVID-19 vaccines affect people’s intentions to receive such a vaccine? This paper attempts to address this question via three preregistered experiments (N = 3463). In Study 1, participants (n = 1269) were exposed to fabricated pro- or anti-vaccine information or to neutral true information, and then asked about their intentions to get vaccinated. In Study 2, participants (n = 646) were exposed to true pro- and anti-vaccine information, while Study 3 (n = 1548) experimentally manipulated beliefs in novel misinformation about COVID-19 vaccines by increasing exposure to the information. The results of these three studies showed that exposure to false information about the vaccines had little effect on participants’ intentions to get vaccinated, even when multiple exposures led them to believe the headlines to be more accurate. An exploratory meta-analysis of studies 1 and 3, with a combined sample size of 2683, showed that exposure to false information both supporting and opposing COVID-19 vaccines actually increased vaccination intentions, though the effect size was very small. We conclude by cautioning researchers against equating exposure to misinformation or perceived accuracy of false news with actual behaviours.

Shradha Verma, Tripti Goel, M Tanveer

In the human brain, essential iron molecules for proper neurological functioning exist in transferrin (tf) and ferritin (Fe3) forms. However, its unusual increment manifests iron overload, which reacts with hydrogen peroxide. This reaction will generate hydroxyl radicals, and irons higher oxidation states. Further, this reaction causes tissue damage or cognitive decline in the brain and also leads to neurodegenerative diseases. The susceptibility difference due to iron overload within the volume of interest (VOI) responsible for field perturbation of MRI and can benefit in estimating the neural disorder. The quantitative susceptibility mapping (QSM) technique can estimate susceptibility alteration and assist in quantifying the local tissue susceptibility differences. It has attracted many researchers and clinicians to diagnose and detect neural disorders such as Parkinsons, Alzheimers, Multiple Sclerosis, and aging. The paper presents a systematic review illustrating QSM fundamentals and its processing steps, including phase unwrapping, background field removal, and susceptibility inversion. Using QSM, the present work delivers novel predictive biomarkers for various neural disorders. It can strengthen new researchers fundamental knowledge and provides insight into its applicability for cognitive decline disclosure. The paper discusses the future scope of QSM processing stages and their applications in identifying new biomarkers for neural disorders.

Zhen Li, Xiaoyu Bao, Yingying Sheng et al.

At present, China’s engineering safety management has developed to a certain level, but the number of casualties caused by construction accidents is still increasing in recent years, and the safety problems in the construction industry are still worrying. For purpose of effectively reducing construction workers’ unsafe behavior and improve the efficiency of construction safety management, based on multi-agent modeling, this paper analyzes the influencing factors during construction workers’ cognitive process from the perspective of safety cognition, constructs the interaction and cognition of the agent under the bidirectional effect of formal rule awareness and conformity mentality model, and set behavior rules and parameters through the Net Logo platform for simulation. The results show that: Unsafe behavior of construction workers is related to the failure of cognitive process, and the role of workers’ psychology and consciousness will affect the cognitive process; The higher the level of conformity intention of construction workers, the easier it is to increase the unsafe behavior of the group; Formal rule awareness can play a greater role only when the management standard is at a high level, and can correct the workers’ safety cognition and effectively correct the workers’ unsafe behavior; Under certain construction site environmental risks, the interaction between formal rule awareness and conformity mentality in an appropriate range is conducive to the realization of construction project life cycle management. This study has certain theoretical and practical significance for in-depth understanding of safety cognition and reducing unsafe behavior of construction team.