Hasil untuk "Consciousness. Cognition"

Joseph E LeDoux, Richard Brown

Geoffrey Whittle-Walls, Geoffrey Whittle-Walls

Human reasoning is traditionally modeled through rational-order frameworks that assume stability, separability, and coherence. Yet across judgment, valuation, perception, and social decision-making, empirical work consistently reveals patterned violations of these assumptions. These deviations intensify in real-world contexts shaped by institutional constraints, identity commitments, and collective narratives, where reasoning must navigate incompatible interpretive frames and interdependent evaluative pressures. Existing theories typically treat such phenomena as noise, bias, or bounded rationality, leaving no formal account of how rational-order rules interact with the variability inherent in social domains. This article proposes a structural framework that explains why these two regimes diverge and how their interaction produces systematic mismatches. Building on quantum probability theory, not as a physical metaphor but as a representational tool, it formalizes evaluative states that remain indeterminate until elicited, transform under contextual modulation, and become relationally coupled across agents and domains. Whereas, existing quantum-cognition models primarily address task-level effects such as conjunction errors or order dependence, this framework scales quantum principles to socially and institutionally embedded reasoning. The account identifies contradiction, interference, entanglement, and resolution as recurrent properties of real-world cognition and shows how quantum formalisms provide a coherent vocabulary for capturing these phenomena. To support cumulative progress, the article outlines a research agenda with empirically testable designs for distinguishing incompatible bases, assessing inseparability, modeling context-driven transformations, and integrating multi-level reasoning environments. This program positions quantum and classical approaches within a unified architecture and advances a broader science of reasoning.

Eleni Mitsea, A. Drigas, C. Skianis

The purpose of the current review study is to shed light on the relationship between respiration and attention. Specifically, we examined whether and how respiration can provoke alterations in attentional states. Secondly, we investigated the benefits of breath-control training techniques in attention. Furthermore, we explored the effectiveness of virtual reality breathing training in improving respiration along with attention. Finally, we discussed the role of metacognition in the conscious synchronization of respiration with attention. It was revealed that every time respiration loses stability, attention fluctuates. Every time respiration is stabilized, attention finds steadiness. The results confirmed that respiration, attention, and as a result consciousness are characterized by bidirectional synchronization since their systems present coupling functions and dynamical interactions. It was also confirmed that breath control practices guarantee instant as well as long-lasting attentional improvements regulating all those mechanisms that strengthen mental alertness firing either the pathways of relaxation or excitation. Conscious-breath training is a practice of mindful metacognition that supports the coupling of attention with respiration. Metacognition in breathing utilizes respiration to improve attention but at the same time attention is used as a tool to consciously control breathing. Finally, breathing control training in virtual reality not only improves attention along with respiration but may finetune internal and external attention. This study concludes that breathing and attention are two ‘giant’ mechanisms that go hand-in-hand and carry on their ‘shoulders’ the whole cognition. Training conscious breathing via advanced technologies should be incorporated into the dialogue of education as methods for brain rewiring assuring better inclusion and academic achievement, especially for those with disabilities.

Jason Bell, Zachary Howard, Stephen Pond et al.

Abstract Technological advances mean that it is now possible to represent the entire 360° view of the horizon to a submarine periscope operator simultaneously, in strips on a single display, as opposed to the restricted view offered through a conventional periscope aperture. Initial research showing performance improvements for such panoramic displays is promising. However, that research has yet to consider the importance of alignment between the visual representation of the environment on the periscope display and the operator themselves (i.e. the visual field compatibility principle). Using a simulated periscope operator task, the current study assessed whether the degree of display-operator alignment influences periscope operator geospatial situation awareness (SA). Four increasingly misaligned display configurations and three different operator orientations (relative to simulated Ownship travel) were assessed. Trained novices (N = 83) were tasked with judging the position of contacts on their display by pointing a joystick at their “real-world” location to measure geospatial SA. Results revealed a strong influence of display-operator alignment on geospatial SA: an aligned display representing contacts in front of an operator at the top of the display and contacts behind an operator at the bottom of the display, produced better geospatial SA (faster, more accurate responses) than other, less aligned display configurations. Diffusion modelling indicated that greater display alignment improved geospatial SA by both increasing information-processing speed and decreasing the amount of evidence required to make decisions. We conclude that geospatial SA can be facilitated by panoramic designs that maximise the alignment of the display to the external world.

Margaret M. Doheny, Nelson A. Roque, Nichole R. Lighthall

IntroductionAlthough it is understood that previous betrayals affect future trust decisions, the degree to which this is true remains unclear in terms of frequency and severity. Additionally, it is currently unknown whether this relationship between the frequency and magnitude of received actions and subsequent trust decisions is mirrored when individuals experience acts of generosity. Prospect theory proposes that losses are weighed more heavily than gains, but the comparison between frequent, minor losses or gains and infrequent major losses or gains has yet to be explored.MethodsThe current study (n = 123) utilizes an adapted version of an economic trust game to examine the effects of minimum and maximum offerings on both reciprocations and perceptions of trust. Participants played the game with two partners: one who offered a maximum or minimum offer (extreme) and one who did not (stable), in either a high or low offer condition where all offers from the stable partner were above or below the median amount, respectively.ResultsThe results align with prospect theory in that minimum offers had a greater impact on both behavior and perceptions than equivalent gains (maximum offers).DiscussionThis study highlights complexities between trust, reciprocity, and perceptions of fairness, with implications for understanding social behavior in real-world settings.

Nataliia Laba, Nataliya Roman, John H. Parmelee

This study addresses how AI-generated images of war are changing the making of memory. Instead of asking how AI-generated images affect individual recall, we focus on how they communicate specific representations, recognising that such portrayals can cultivate particular assumptions and beliefs. Drawing on memory of the multitude, visual social semiotics, and cultivation/desensitisation theories, we analyse how visual generative AI mediates the representation of the Russia-Ukraine war. Our corpus includes 200 images of the Russia-Ukraine war generated from 23 prompts across proprietary and open-source visual generative AI systems. The findings indicate that visual generative AI tends to present a sanitised view of the war. Critical aspects, such as death, injury, and suffering of children and refugees are often excluded. Furthermore, a disproportional focus on urban areas misrepresents the full scope of the war. Visual generative AI, we argue, introduces a new dimension to memory making in that it blends documentation with speculative fiction by synthesising the multitude embedded within the visual memory of war archives, historical biases, representational limitations, and commercial risk aversion. By foregrounding the socio-technical and discursive dimensions of synthetic war content, this study contributes to an interdisciplinary dialogue on collective memory at the intersection of visual communication studies, media studies, and memory studies by providing empirical insights into how generative AI mediates the visual representation of war through human-archival-mechanistic entanglements.

Luca Tarasi, Margherita Covelli, Chiara Tabarelli de Fatis et al.

Decisional confidence refers to the subjective evaluation of the accuracy of a decision based on sensory information. While these judgments are typically grounded in the strength of evidence leading to a decision, they are also subjected to influence from top-down factors such as prior expectations. Previous research has highlighted the impact of prior information on decision parameters such as reaction times and decision criteria placement. However, a comprehensive understanding of how prior information shapes confidence ratings is still lacking. In this study, we manipulate prior knowledge by inducing varying levels of target probability expectation (low: 33%, random: 50%, high: 67%) in a perceptual detection task. In each trial both type-1 (detection) and type-2 (confidence) responses were recorded. First, we replicate previous findings, demonstrating that decisional priors impact decision criteria but not task sensitivity. Secondly, we reveal the strong effect that prior expectations exert on type-2 decisions, with this influence being moderated by a congruency effect between the given prior, the actual stimulus presented, and the provided response. Moreover, we find that confidence is higher in correct compared to incorrect responses, with low-probability trials leading to higher confidence judgments in correct choices compared to random and liberal trials. Finally, we unveil that prior-dependent modulation rates in criterion and confidence were positively associated. These results underscore the intricate interplay between prior expectations, decision-making, and confidence levels, demonstrating that what we perceive is not solely a data-driven phenomenon but can be already shaped by the available information about the state of the world.

Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang et al.

Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning. To understand this gap, we synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, representations for organizing reasoning & knowledge, and transformation operations. We introduce a fine-grained evaluation framework and conduct the first large-scale empirical analysis of 192K traces from 18 models across text, vision, and audio, complemented by 54 human think-aloud traces, which we make publicly available. We find that models under-utilize cognitive elements correlated with success, narrowing to rigid sequential processing on ill-structured problems where diverse representations and meta-cognitive monitoring are critical. Human traces show more abstraction and conceptual processing, while models default to surface-level enumeration. Meta-analysis of 1.6K LLM reasoning papers reveals the research community concentrates on easily quantifiable elements (sequential organization: 55%, decomposition: 60%) but neglecting meta-cognitive controls (self-awareness: 16%) that correlate with success. Models possess behavioral repertoires associated with success but fail to deploy them spontaneously. Leveraging these patterns, we develop test-time reasoning guidance that automatically scaffold successful structures, improving performance by up to 66.7% on complex problems. By establishing a shared vocabulary between cognitive science and LLM research, our framework enables systematic diagnosis of reasoning failures and principled development of models that reason through robust cognitive mechanisms rather than spurious shortcuts, while providing tools to test theories of human cognition at scale.

Edervaldo Melo

This paper presents the Nemosine Framework, a modular cognitive architecture designed to support assisted reasoning, structured thinking, and systematic analysis. The model operates through functional cognitive modules ("personas") that organize tasks such as planning, evaluation, cross-checking, and narrative synthesis. The framework combines principles from metacognition, distributed cognition, and modular cognitive systems to offer an operational structure for assisted problem-solving and decision support. The architecture is documented through formal specification, internal consistency criteria, and reproducible structural components. The goal is to provide a clear conceptual basis for future computational implementations and to contribute to the study of symbolic-modular architectures for reasoning.

Lixiang Xu, Xianwei Ding, Xin Yuan et al.

Knowledge Tracing (KT) involves monitoring the changes in a student's knowledge over time by analyzing their past responses, with the goal of predicting future performance. However, most existing methods primarily focus on feature enhancement, while overlooking the deficiencies in cognitive representation and the ability to express cognition-issues often caused by interference from non-cognitive factors such as slipping and guessing. This limitation hampers the ability to capture the continuity and coherence of the student's cognitive process. As a result, many methods may introduce more prediction bias and modeling costs due to their inability to maintain cognitive continuity and coherence. Based on the above discussion, we propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model. This model em ploys a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them. This approach ensures that the cognitive representation aligns with the student's cognitive patterns, maintaining overall continuity and coherence. As a result, it provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states. Additionally, the CRDP-KT model performs partitioned optimization of cognitive representations to enhance the reliability of the optimization process. Furthermore, it improves its ability to express the student's cognition through a weighted fusion of optimized record representations and re lationships learned from a bipartite graph. Finally, experiments conducted on three public datasets validate the effectiveness of the proposed CRDP-KT model.

Serge Dolgikh

This study explores the emergence of counter-inferential behavior in natural and artificial cognitive systems, that is, patterns in which agents misattribute empirical success or suppress adaptation, leading to epistemic rigidity or maladaptive stability. We analyze archetypal scenarios in which such behavior arises: reinforcement of stability through reward imbalance, meta-cognitive attribution of success to internal superiority, and protective reframing under perceived model fragility. Rather than arising from noise or flawed design, these behaviors emerge through structured interactions between internal information models, empirical feedback, and higher-order evaluation mechanisms. Drawing on evidence from artificial systems, biological cognition, human psychology, and social dynamics, we identify counter-inferential behavior as a general cognitive vulnerability that can manifest even in otherwise well-adapted systems. The findings highlight the importance of preserving minimal adaptive activation under stable conditions and suggest design principles for cognitive architectures that can resist rigidity under informational stress.

Thomas Van Hoey, Xiaoyu Yu, Tung-Le Pan et al.

A well-known method of studying iconic words is through the collection of subjective ratings. We collected such ratings regarding familiarity, iconicity, imagery/imageability, concreteness, sensory experience rating (SER), valence and arousal for Mandarin ABB words. This is a type of phrasal compound consisting of a prosaic syllable A and a reduplicated BB part, resulting in a vivid phrasal compound, for example, wù-mángmáng 雾茫茫 ‘completely foggy’. The correlations between the newly collected ABB ratings are contrasted with two other sets of prosaic word ratings, demonstrating that variables that characterize ABB words in an absolute sense may not play a distinctive role when contrasted with other types of words. Next, we provide another angle for looking at ABB words, by investigating to what degree rating data converges with corpus data. By far, the variable that characterizes ABB items consistently throughout these case studies is their high score for imageability, showing that they are indeed rightfully characterized as vivid. Methodologically, we show that it pays off to not take rating data at face value but to contrast it with other comparable datasets of a different phenomenon or data about the same phenomenon compiled in an ontologically different manner.

Yu Peiyun

This article explores the importance of intercultural historical thinking in the age of globalization and its significance to the historical study of civilization. The article points out that in the context of globalization, human beings are still faced with different understandings of heterogeneity, otherness and homogeneity. Historical research is an important way for societies to realize their cultural functions and cultural orientations, while intercultural historical thinking and historical research are particularly valuable because of their practical significance. The article emphasizes the major role of historical consciousness in intercultural historical thinking, and believes that historical consciousness is not only about the cognition of the past, but also a historical interpretation of the present. Through the mutual communication of historical consciousness, we can better understand the conceptual type and cultural background of others, so as to rebuild the consensus of the community of humanity in the age of globalization. In addition, the article also explores the ontological construction of historical consciousness, regarding that this helps to introduce grand concepts such as “civilization” into a concrete and practical context, and promote mutual understanding and mutual learning among different civilizations.

Zhaofeng Shi, Heqian Qiu, Lanxiao Wang et al.

In this paper, we explore a novel Text-supervised Egocentic Semantic Segmentation (TESS) task that aims to assign pixel-level categories to egocentric images weakly supervised by texts from image-level labels. In this task with prospective potential, the egocentric scenes contain dense wearer-object relations and inter-object interference. However, most recent third-view methods leverage the frozen Contrastive Language-Image Pre-training (CLIP) model, which is pre-trained on the semantic-oriented third-view data and lapses in the egocentric view due to the ``relation insensitive" problem. Hence, we propose a Cognition Transferring and Decoupling Network (CTDN) that first learns the egocentric wearer-object relations via correlating the image and text. Besides, a Cognition Transferring Module (CTM) is developed to distill the cognitive knowledge from the large-scale pre-trained model to our model for recognizing egocentric objects with various semantics. Based on the transferred cognition, the Foreground-background Decoupling Module (FDM) disentangles the visual representations to explicitly discriminate the foreground and background regions to mitigate false activation areas caused by foreground-background interferential objects during egocentric relation learning. Extensive experiments on four TESS benchmarks demonstrate the effectiveness of our approach, which outperforms many recent related methods by a large margin. Code will be available at https://github.com/ZhaofengSHI/CTDN.

Xin Wang, Boyan Gao, Yi Dai et al.

Stress is a pervasive global health issue that can lead to severe mental health problems. Early detection offers timely intervention and prevention of stress-related disorders. The current early detection models perform "black box" inference suffering from limited explainability and trust which blocks the real-world clinical application. Thanks to the generative properties introduced by the Large Language Models (LLMs), the decision and the prediction from such models are semi-interpretable through the corresponding description. However, the existing LLMs are mostly trained for general purposes without the guidance of psychological cognitive theory. To this end, we first highlight the importance of prior theory with the observation of performance boosted by the chain-of-thoughts tailored for stress detection. This method termed Cognition Chain explicates the generation of stress through a step-by-step cognitive perspective based on cognitive appraisal theory with a progress pipeline: Stimulus $\rightarrow$ Evaluation $\rightarrow$ Reaction $\rightarrow$ Stress State, guiding LLMs to provide comprehensive reasoning explanations. We further study the benefits brought by the proposed Cognition Chain format by utilising it as a synthetic dataset generation template for LLMs instruction-tuning and introduce CogInstruct, an instruction-tuning dataset for stress detection. This dataset is developed using a three-stage self-reflective annotation pipeline that enables LLMs to autonomously generate and refine instructional data. By instruction-tuning Llama3 with CogInstruct, we develop CogLLM, an explainable stress detection model. Evaluations demonstrate that CogLLM achieves outstanding performance while enhancing explainability. Our work contributes a novel approach by integrating cognitive theories into LLM reasoning processes, offering a promising direction for future explainable AI research.

Saturnino Luz, Sofia De La Fuente Garcia, Fasih Haider et al.

We present a novel benchmark dataset and prediction tasks for investigating approaches to assess cognitive function through analysis of connected speech. The dataset consists of speech samples and clinical information for speakers of Mandarin Chinese and English with different levels of cognitive impairment as well as individuals with normal cognition. These data have been carefully matched by age and sex by propensity score analysis to ensure balance and representativity in model training. The prediction tasks encompass mild cognitive impairment diagnosis and cognitive test score prediction. This framework was designed to encourage the development of approaches to speech-based cognitive assessment which generalise across languages. We illustrate it by presenting baseline prediction models that employ language-agnostic and comparable features for diagnosis and cognitive test score prediction. The models achieved unweighted average recall was 59.2% in diagnosis, and root mean squared error of 2.89 in score prediction.

Siyu Wu, Alessandro Oltramari, Jonathan Francis et al.

Resolving the dichotomy between the human-like yet constrained reasoning processes of Cognitive Architectures and the broad but often noisy inference behavior of Large Language Models (LLMs) remains a challenging but exciting pursuit, for enabling reliable machine reasoning capabilities in production systems. Because Cognitive Architectures are famously developed for the purpose of modeling the internal mechanisms of human cognitive decision-making at a computational level, new investigations consider the goal of informing LLMs with the knowledge necessary for replicating such processes, e.g., guided perception, memory, goal-setting, and action. Previous approaches that use LLMs for grounded decision-making struggle with complex reasoning tasks that require slower, deliberate cognition over fast and intuitive inference -- reporting issues related to the lack of sufficient grounding, as in hallucination. To resolve these challenges, we introduce LLM-ACTR, a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making by integrating the ACT-R Cognitive Architecture with LLMs. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations, injects this information into trainable LLM adapter layers, and fine-tunes the LLMs for downstream prediction. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability of our approach, compared to LLM-only baselines that leverage chain-of-thought reasoning strategies.

Bin Shan, Xiang Fei, Wei Shi et al.

The comprehension of text-rich visual scenes has become a focal point for evaluating Multi-modal Large Language Models (MLLMs) due to their widespread applications. Current benchmarks tailored to the scenario emphasize perceptual capabilities, while overlooking the assessment of cognitive abilities. To address this limitation, we introduce a Multimodal benchmark towards Text-rich visual scenes, to evaluate the Cognitive capabilities of MLLMs through visual reasoning and content-creation tasks (MCTBench). To mitigate potential evaluation bias from the varying distributions of datasets, MCTBench incorporates several perception tasks (e.g., scene text recognition) to ensure a consistent comparison of both the cognitive and perceptual capabilities of MLLMs. To improve the efficiency and fairness of content-creation evaluation, we conduct an automatic evaluation pipeline. Evaluations of various MLLMs on MCTBench reveal that, despite their impressive perceptual capabilities, their cognition abilities require enhancement. We hope MCTBench will offer the community an efficient resource to explore and enhance cognitive capabilities towards text-rich visual scenes.

Zhifu Chen, Hengnian Gu, Jin Peng Zhou et al.

Cognitive diagnosis represents a fundamental research area within intelligent education, with the objective of measuring the cognitive status of individuals. Theoretically, an individual's cognitive state is essentially equivalent to their cognitive structure state. Cognitive structure state comprises two key components: knowledge state (KS) and knowledge structure state (KUS). The knowledge state reflects the learner's mastery of individual concepts, a widely studied focus within cognitive diagnosis. In contrast, the knowledge structure state-representing the learner's understanding of the relationships between concepts-remains inadequately modeled. A learner's cognitive structure is essential for promoting meaningful learning and shaping academic performance. Although various methods have been proposed, most focus on assessing KS and fail to assess KUS. To bridge this gap, we propose an innovative and effective framework-CSCD (Cognitive Structure State-based Cognitive Diagnosis)-which introduces a novel framework to modeling learners' cognitive structures in diagnostic assessments, thereby offering new insights into cognitive structure modeling. Specifically, we employ an edge-feature-based graph attention network to represent the learner's cognitive structure state, effectively integrating KS and KUS. Extensive experiments conducted on real datasets demonstrate the superior performance of this framework in terms of diagnostic accuracy and interpretability.

Yaojia Lv, Haojie Pan, Zekun Wang et al.

Cognitive dynamics are pivotal to advance human understanding of the world. Recent advancements in large language models (LLMs) reveal their potential for cognitive simulation. However, these LLM-based cognitive studies primarily focus on static modeling, overlooking the dynamic nature of cognition. To bridge this gap, we propose the concept of the cognitive dynamics of LLMs and present a corresponding task with the inspiration of longitudinal studies. Towards the task, we develop CogBench, a novel benchmark to assess the cognitive dynamics of LLMs and validate it through participant surveys. We also design two evaluation metrics for CogBench, including Authenticity and Rationality. Recognizing the inherent static nature of LLMs, we introduce CogGPT for the task, which features an innovative iterative cognitive mechanism aimed at enhancing lifelong cognitive dynamics. Empirical results demonstrate the superiority of CogGPT over existing methods, particularly in its ability to facilitate role-specific cognitive dynamics under continuous information flows.