Hasil untuk "Consciousness. Cognition"

Francesco Romeo, Jacopo Settino

We introduce Extreme Quantum Cognition Machines, a class of quantum learning architectures for deliberative decision making that is tolerant to noisy and contradictory training data. Inspired by the quantum cognition paradigm, Extreme Quantum Cognition Machines are closely related to quantum extreme learning and quantum reservoir computing, where fixed quantum dynamics generates a nonlinear feature map and learning is confined to a linear readout. A dynamical attention mechanism, implemented through an input-dependent interaction term in the Hamiltonian, modulates the quantum evolution and biases the resulting feature embedding toward task-relevant correlations. The approach is validated on linguistic classification tasks, which serve as paradigmatic examples of deliberative inference. Hardware-compatible quantum implementations of the proposed framework are discussed, together with potential applications in symbolic inference, sequence analysis, anomaly detection, and automatic diagnosis, with direct relevance to domains such as biology, forensics, and cybersecurity.

Xiaoxing Wang, Ning Liao, Shikun Wei et al.

Autonomous agent frameworks still struggle to reconcile long-term experiential learning with real-time, context-sensitive decision-making. In practice, this gap appears as static cognition, rigid workflow dependence, and inefficient context usage, which jointly limit adaptability in open-ended and non-stationary environments. To address these limitations, we present AutoAgent, a self-evolving multi-agent framework built on three tightly coupled components: evolving cognition, on-the-fly contextual decision-making, and elastic memory orchestration. At the core of AutoAgent, each agent maintains structured prompt-level cognition over tools, self-capabilities, peer expertise, and task knowledge. During execution, this cognition is combined with live task context to select actions from a unified space that includes tool calls, LLM-based generation, and inter-agent requests. To support efficient long-horizon reasoning, an Elastic Memory Orchestrator dynamically organizes interaction history by preserving raw records, compressing redundant trajectories, and constructing reusable episodic abstractions, thereby reducing token overhead while retaining decision-critical evidence. These components are integrated through a closed-loop cognitive evolution process that aligns intended actions with observed outcomes to continuously update cognition and expand reusable skills, without external retraining. Empirical results across retrieval-augmented reasoning, tool-augmented agent benchmarks, and embodied task environments show that AutoAgent consistently improves task success, tool-use efficiency, and collaborative robustness over static and memory-augmented baselines. Overall, AutoAgent provides a unified and practical foundation for adaptive autonomous agents that must learn from experience while making reliable context-aware decisions in dynamic environments.

Srijaya Nandi, Aesha Lahiri, Tuhin Subhra Pal et al.

Abstract Free-ranging dogs (FRDs) constitute approximately 80% of the global dog population. They are freely breeding and live without direct human supervision, making them ideal for studying how factors such as the lack of supervision, unmanaged breeding, and variable human contact shape dog-human relationships. Living in proximity to humans, FRDs in India frequently interact with people, and previous studies suggest humans to be a crucial part of their social environment. Positive reinforcement in the form of food and petting is commonly received from humans. In this study, we investigated which reward, food or petting, is preferred more during short-term and repeated interactions. Field trials were conducted on 61 adult FRDs. During the familiarization phase (Days 1 to 5), two unfamiliar individuals each provided either food or petting to the dogs. This was followed by a series of choice tests (Days 1 to 10), in which dogs could choose between the two individuals. On the first day, dogs significantly preferred the food provider. However, from the second day onward, preference was no different from chance, suggesting that the strength of food as a reward was reduced. These findings suggest that while food is a stronger short-term motivator, repeated interactions involving either food or petting contribute equally to the formation of positive social associations over time. This study sheds light on the development of the dog-human relationship in Indian FRD populations and highlights the nuanced role of different rewards in fostering affiliative associations.

Songlin Xu, Xinyu Zhang

In this paper, we introduce an AI-mediated framework that can provide intelligent feedback to augment human cognition. Specifically, we leverage deep reinforcement learning (DRL) to provide adaptive time pressure feedback to improve user performance in a math arithmetic task. Time pressure feedback could either improve or deteriorate user performance by regulating user attention and anxiety. Adaptive time pressure feedback controlled by a DRL policy according to users' real-time performance could potentially solve this trade-off problem. However, the DRL training and hyperparameter tuning may require large amounts of data and iterative user studies. Therefore, we propose a dual-DRL framework that trains a regulation DRL agent to regulate user performance by interacting with another simulation DRL agent that mimics user cognition behaviors from an existing dataset. Our user study demonstrates the feasibility and effectiveness of the dual-DRL framework in augmenting user performance, in comparison to the baseline group.

Muhammad Rezki, Uun Zulfiana

Tantrums are a common emotional response in children, often occurring when they struggle to regulate their emotions. This study focused on the use of filial therapy in improving emotional regulation and reducing tantrum frequency in a 9-year-old boy. Filial therapy was chosen for its emphasis on parental involvement, strengthening the parent-child bond, and teaching emotional regulation skills. The study was a single subject research design, that began with an initial assessment through interviews, observations, and psychological tests. This was followed by an 11-session filial therapy. The results showed a significant improvement in the participant’s emotional regulation, leading to a reduction in tantrum behavior. The ERQ scores during the pretest were 25 (moderate category), increasing to 47 (high category) in the post-test, and further rising to 55 (high category) in the follow-up assessment. These findings highlight the essential role of parents in supporting their child's emotional development and the use of filial therapy to improve parent-child dynamics and reduce tantrums.

Katharina Kühne, Alex Miklashevsky, Anastasia Malyshevskaya

The space-time congruency effect indicates faster processing of past-/future-related words with the left/right response key, suggesting the presence of the horizontal Mental Time Line (MTL). Typically, this effect is observed in the tasks with high temporal demand (i.e., past versus future categorization), but not in those with the low relevance of the time dimension (i.e., sensicality judgments). However, it remains unclear whether intermediate levels of temporal demand are sufficient to activate the MTL. To address this, we conducted three experiments in which participants categorized the same set of temporal words based on their relation to living entities (Experiment 1), space (Experiment 2), and general time (Experiment 3). In individual analyses of the experiments, the space-time congruency effect was absent in Experiment 1. In Experiment 2, the effect emerged in reaction times but not in accuracy. In Experiment 3, it was observed in both measures. Subsequent comparisons across experiments suggested reliable differences between Experiments 2 and 3 in reaction times and between Experiment 3 and the other two experiments in accuracy. Our results provide evidence that MTL activation depends on the level of temporal demand required by the task. The findings support the notion that mental representations are context-sensitive rather than fixed.

ZHOU Chenxi, LIN Beilei, TANG Shangfeng, ZHANG Zhenxiang, WANG Xiaoxuan, JIANG Hu, ZHANG Dudu, LIU Bowen, LI Xin

Background The incidence of stroke is increasing year by year, and behavioral control is a direct and effective intervention means to prevent stroke. Proactive health medical model improves the initiative and accessibility of chronic disease prevention and control, while the concept of proactive health behavior in stroke patients remains to be explored. Objective To explore the level of proactive health cognition and behavior in stroke patients, and construct the conceptual framework of proactive health behavior in stroke patients. Methods From August to October 2023, a total of 26 inpatients with stroke in the Department of Neurology of the Second Affiliated Hospital of Zhengzhou University were selected as the study objects by means of purposive sampling method. Following the grounded theory methodology of interpretivism, 26 patients with stroke were interviewed by semi-structured method, and the data were analyzed by coding and persistence comparison methods. Results The 10 main categories and 4 core categories of the theme "proactive health behavior of stroke patients" were separated out, including 3 intrinsic behaviors of "health motivation, health responsibility and mental health", 1 habitual behavior of "lifestyle management", 3 social behaviors of "active compliance with doctors, social relations and information seeking", and 3 intervention conditions of "consciousness awakenings, self-control and resource availability". And establish the conceptual framework. Conclusion The conceptual framework of proactive health behavior in stroke patients includes intrinsic behavior, habitual behavior, social behavior and intervention conditions. This framework may be helpful for the further development of assessment tools and the formulation of personalized intervention measures, and has guiding significance for promoting the research and practice of proactive health behavior in stroke patients.

Ariella Rodrigues Cordeiro Rozales, Marcos Gonçalves Santana, Shawn D. Youngstedt et al.

Introduction Sleeping pills are assumed to be the most efficacious means of treating acute insomnia, but their use has associated risks. Exercise could provide a healthy alternative treatment for insomnia, particularly if it could be shown to have comparable efficacy to sleeping pills.

Raj Sanjay Shah, Khushi Bhardwaj, Sashank Varma

Recent studies show evidence for emergent cognitive abilities in Large Pre-trained Language Models (PLMs). The increasing cognitive alignment of these models has made them candidates for cognitive science theories. Prior research into the emergent cognitive abilities of PLMs has largely been path-independent to model training, i.e., has focused on the final model weights and not the intermediate steps. However, building plausible models of human cognition using PLMs would benefit from considering the developmental alignment of their performance during training to the trajectories of children's thinking. Guided by psychometric tests of human intelligence, we choose four sets of tasks to investigate the alignment of ten popular families of PLMs and evaluate their available intermediate and final training steps. These tasks are Numerical ability, Linguistic abilities, Conceptual understanding, and Fluid reasoning. We find a striking regularity: regardless of model size, the developmental trajectories of PLMs consistently exhibit a window of maximal alignment to human cognitive development. Before that window, training appears to endow "blank slate" models with the requisite structure to be poised to rapidly learn from experience. After that window, training appears to serve the engineering goal of reducing loss but not the scientific goal of increasing alignment with human cognition.

Artem Sukhobokov, Evgeny Belousov, Danila Gromozdov et al.

The article identified 42 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of blocks is not found in any of the existing architectures, the article proposes a new cognitive architecture for intelligent systems approaching AGI in their capabilities. As one of the key solutions within the framework of the architecture, a universal method of knowledge representation is proposed, which allows combining various non-formalized, partially and fully formalized methods of knowledge representation in a single knowledge base, such as texts in natural languages, images, audio and video recordings, graphs, algorithms, databases, neural networks, knowledge graphs, ontologies, frames, essence-property-relation models, production systems, predicate calculus models, conceptual models, and others. To combine and structure various fragments of knowledge, archigraph models are used, constructed as a development of annotated metagraphs. As components, the cognitive architecture being developed includes machine consciousness, machine subconsciousness, blocks of interaction with the external environment, a goal management block, an emotional control system, a block of social interaction, a block of reflection, an ethics block and a worldview block, a learning block, a monitoring block, blocks of statement and solving problems, self-organization and meta learning block.

Daekyung Jung

This article explores the potential emergence of religious behavior in artificial intelligence (AI) through the lens of embodied cognition, which asserts that cognitive functions are deeply intertwined with bodily experiences. It examines the convergence of AI, soft robotics, and religious cognitive behaviors and suggests that AI, once it attains human-level intelligence and self-awareness, might exhibit religious behaviors as a cognitive strategy to confront and transcend finitude. Drawing on neuroscientific, philosophical, and religious discussions, with particular reference to the works of Kingson Man, Antonio Damasio, Uffe Schjødt, and William Sims Bainbridge, this article investigates how religious behaviors could arise in AI equipped with a vulnerable artificial body inclined towards homeostasis and self-preservation. The outcomes of this exploration extend beyond theoretical debates, as they provide insights into the physicalist understanding of consciousness and the naturalistic study of religious behaviors while also considering some technological constraints in the context of AI advancements.

Yaochen Liu, Qiuchi Li, Benyou Wang et al.

Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a typical example of such domains. In the last few years, by leveraging the modeling power of quantum probability (a non-classical probability stemming from quantum mechanics methodology) and deep neural networks, a range of novel quantum-cognitively inspired models for sentiment analysis have emerged and performed well. This survey presents a timely overview of the latest developments in this fascinating cross-disciplinary area. We first provide a background of quantum probability and quantum cognition at a theoretical level, analyzing their advantages over classical theories in modeling the cognitive aspects of sentiment analysis. Then, recent quantum-cognitively inspired models are introduced and discussed in detail, focusing on how they approach the key challenges of the sentiment analysis task. Finally, we discuss the limitations of the current research and highlight future research directions.

Dominic Widdows, Jyoti Rani, Emmanuel Pothos

This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed in a survey affects whether participants answer 'yes' or 'no', so the population that answers 'yes' to both questions cannot be modeled as the intersection of two fixed sets. It can, however, be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020s, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, by representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations.

Shimon Komarovsky

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. In addition, it is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances.

Rémy A. Furrer, Karen Schloss, Gary Lupyan et al.

Abstract In the United States the color red has come to represent the Republican party, and blue the Democratic party, in maps of voting patterns. Here we test the hypothesis that voting maps dichotomized into red and blue states leads people to overestimate political polarization compared to maps in which states are represented with continuous gradations of color. We also tested whether any polarizing effect is due to partisan semantic associations with red and blue, or if alternative hues produce similar effects. In Study 1, participants estimated the hypothetical voting patterns of eight swing states on maps with dichotomous or continuous red/blue or orange/green color schemes. A continuous gradient mitigated the polarizing effects of red/blue maps on voting predictions. We also found that a novel hue pair, green/orange, decreased perceived polarization. Whether this effect was due to the novelty of the hues or the fact that the hues were not explicitly labeled “Democrat” and “Republican” was unclear. In Study 2, we explicitly assigned green/orange hues to the two parties. Participants viewed electoral maps depicting results from the 2020 presidential election and estimated the voting margins for a subset of states. We replicated the finding that continuous red/blue gradient reduced perceived polarization, but the novel hues did not reduce perceived polarization. Participants also expected their hypothetical vote to matter more when viewing maps with continuous color gradations. We conclude that the dichotomization of electoral maps (not the particular hues) increases perceived voting polarization and reduces a voter’s expected influence on election outcomes.

Göran Sonesson

Cognitive semiotics has been characterized as the pooling together of theories, methods, models, and findings from semiotics and cognitive science. Those who have taken up the challenge of combining these two research traditions have done so, however, in rather different ways. Nevertheless, within both traditions, and predominantly in the second one, there are those who espouse some kind of reductionism, in which meaning and consciousness are non-existent or mere epiphenomena. The present contribution argues that there can be no study of either meaning or consciousness that does not start by recognizing that these are real phenomena, and that all other methods, such as, notably, experiments, while very useful, are only limited and indirect ways of approaching meaning and consciousness. We start by pointing out that linguistics, and therefore semiotics, doesn’t fit into the classical division between Geisteswissenschaften and Naturwissenschaften, but shares properties with both. To begin the building of an epistemology for cognitive semiotics, we hark back to Vico’s notion of Verum Factum, unwittingly explicated by the semiotician Luis Prieto, as well as to the phenomenological method, as it has been defined by both Husserl and Peirce, and taken further by Gurwitsch and Merleau-Ponty.

Mario J. Baldassari, Kara N. Moore, Ira E. Hyman et al.

Abstract Research on eyewitness identification often involves exposing participants to a simulated crime and later testing memory using a lineup. We conducted a systematic review showing that pre-event instructions, instructions given before event exposure, are rarely reported and those that are reported vary in the extent to which they warn participants about the nature of the event or tasks. At odds with the experience of actual witnesses, some studies use pre-event instructions explicitly warning participants of the upcoming crime and lineup task. Both the basic and applied literature provide reason to believe that pre-event instructions may affect eyewitness identification performance. In the current experiment, we tested the impact of pre-event instructions on lineup identification decisions and confidence. Participants received non-specific pre-event instructions (i.e., “watch this video”) or eyewitness pre-event instructions (i.e., “watch this crime video, you’ll complete a lineup later”) and completed a culprit-absent or -present lineup. We found no support for the hypothesis that participants who receive eyewitness pre-event instructions have higher discriminability than participants who receive non-specific pre-event instructions. Additionally, confidence-accuracy calibration was not significantly different between conditions. However, participants in the eyewitness condition were more likely to see the event as a crime and to make an identification than participants in the non-specific condition. Implications for conducting and interpreting eyewitness identification research and the basic research on instructions and attention are discussed.

Christine Lohmeier, Christian Pentzold

Sylvain Penaud, Delphine Yeh, Alexandre Gaston-Bellegarde et al.

Abstract Recent studies suggest that the human body plays a critical role in episodic memory. Still, the precise relationship between bodily self-consciousness (BSC) and memory formation of specific events, especially in real-life contexts, remains a topic of ongoing research. The present study investigated the relationship between BSC and episodic memory (EM) using immersive virtual reality (VR) technology. Participants were immersed in an urban environment with naturalistic events, while their visuomotor feedback was manipulated in three within-subjects conditions: Synchronous, Asynchronous, and No-body. Our results show that asynchronous visuomotor feedback and not seeing one’s body, compared to synchronous feedback, decrease the sense of self-identification, self-location and agency, and sense of presence. Moreover, navigating in the Asynchronous condition had a detrimental impact on incidental event memory, perceptual details, contextual association, subjective sense of remembering, and memory consolidation. In contrast, participants in the No-Body condition were only impaired in egocentric spatial memory and the sense of remembering at ten-day delay. We discuss these findings in relation to the role of bodily self-representation in space during event memory encoding. This study sheds light on the complex interplay between BSC, sense of presence, and episodic memory processes, and strengthens the potential of embodiment and VR technology in studying and enhancing human cognition.

Beatrice Valentini, Kim Uittenhove, Evie Vergauwe

Working memory is a cognitive system responsible for maintaining information. It is often assumed to contain different states of accessibility of information, which is highest for an item held in the focus of attention. Evidence for this heightened accessibility usually comes from item-recognition tasks, in which a memory list is followed by a probe to be judged as being present in or absent from the list. Probes corresponding to the last-presented list item are usually recognized faster than probes corresponding to any other list item (i.e., the last-presented benefit), an effect that is often explained by the last-presented item being in the focus of attention. The last-presented benefit usually disappears when a long retention interval is inserted between the presentation of the list items and the probe. This raises the question of how long the last-presented item remains in the focus of attention. The present study gradually manipulates the retention interval between the presentation of the list of items and the probe in an item-recognition task in order to pinpoint when the focus of attention switches away from the last-presented list item. The results show that the last-presented benefit decreases over time when the retention interval is gradually extended from 0 ms to 200 ms, 400 ms and 500 ms, and completely disappears as of 750 ms. The cognitive mechanisms that may be involved in the time course of the last-presented benefit are discussed.