Hasil untuk "Consciousness. Cognition"

William J. Tyler

Low-intensity transcranial focused ultrasound (tFUS) is rapidly emerging as a transformative non-invasive brain stimulation (NIBS) modality characterized by high spatial resolution and ability to target deep brain circuits. Unlike electromagnetic techniques such as transcranial magnetic stimulation and transcranial direct current stimulation, which are constrained by centimeter-scale resolution and a depth-focality tradeoff, tFUS leverages mechanical pressure waves to modulate both superficial cortical and deep subcortical structures with millimeter precision. This article discusses recent scientific observations and engineering breakthroughs in the advancement of tFUS for next-generation ultrasonic brain-computer interfaces (uBCIs) and human-machine interfaces. These advancements move beyond open-loop systems and demonstrate closed-loop architectures that incorporate real-time electrophysiological feedback to optimize cognitive variables such as attention, learning, trust, and cooperation in various applications. Other advances in the development of ultrasound sensors for sonomyography to decode muscle activation and functional ultrasound to monitor hemodynamic brain activity are discussed as potential elements in bidirectional uBCIs. Together, these advances position ultrasound as a foundational technology for the development of intelligent, adaptive, and bidirectional neural interfaces that will seamlessly integrate human cognition with next-generation automation and robotic systems.

Leonardus Edwin Gandawijaya, Kwartarini Wahyu Yuniarti

Depression and anxiety are strong indicators of difficulties in the process of recentering during emerging adulthood. Although various interventions exist to address these issues, group psychosynthesis offers efficient treatment in developing countries. This case report highlights the experiences of seven university students who participated in Empathic Love Therapy (ELT) group to reduce depression and anxiety, as measured by the Patient Health Questionnaire Anxiety and Depression (PHQ-ADS) scale (d = 1.79; p < .01). The application of psychosynthesis through the Empathic Love Therapy (ELT) module in a group setting demonstrates potential as a cost-effective option for primary healthcare professionals, particularly in addressing the rising number of students at risk of self-harm or suicide.

Noah Britt, Jackie Chau, Hong-jin Sun

Abstract Human attention can be guided by semantic information conveyed by individual objects in the environment. Over time, we learn to allocate attention resources towards stimuli that are behaviourally relevant to ongoing action, leading to attention capture by meaningful peripheral stimuli. A common example includes, while driving, stimuli that imply a possibly hazardous scenario (e.g. a pedestrian about to cross the road) warrant attentional prioritization to ensure safe proceedings. In the current study, we report a novel phenomenon in which the guidance of attention is dependent on the stimuli appearing in a behaviourally relevant context. Using a driving simulator, we simulated a real-world driving task representing an overlearned behaviour for licensed drivers. While driving, participants underwent a peripheral cue-target paradigm where a roadside pedestrian avatar (target) appeared following a cylinder cue. Results revealed that, during simulated driving conditions, participants (all with driver’s licenses) showed greater attentional facilitation when pedestrians were oriented towards the road compared to away. This orientation-specific selectivity was not seen if the 3-D context was removed (Experiment 1) or the same visual scene was presented, but participants’ viewpoints remained stationary (Experiment 2), or an inanimate object served as a target during simulated driving (Experiment 3). This context-specific attention modulation likely reflects drivers’ expertise in automatically attending to behaviourally relevant information in a context-dependent manner.

Kara N. Moore, Dara U. Zwemer, James Michael Lampinen et al.

Abstract The pristine conditions hypothesis postulates that highly confident witnesses will be highly accurate, even when witnessing conditions are poor. Recent research has extended this to children and concluded that, on average, child-eyewitnesses who are highly confident are rather accurate (i.e., 85–97%, Winsor et al., Journal of Experimental Psychology. General 150:2387–2407, 2021). However, this has only been tested in good witnessing conditions. Since then, research in adults has shown that, in some poor witnessing conditions, the high confidence-accuracy relationship breaks down. We sought to determine if highly confident child and adult eyewitnesses would be highly accurate even in poor witnessing conditions. We presented 1,055 participants (485 young children, 357 older children, and 213 adults) with a 360-degree live-action mock-crime video in a virtual reality headset. To test whether witnessing conditions impact children’s confidence-accuracy relationship, we manipulated exposure duration (short-6 s, long-34 s) at encoding and the presence of the culprit in the lineup identification task. Surprisingly, memory strength was weak for all age groups under good and poor witnessing conditions. There were so few high confidence identifications in adults that the confidence-accuracy relationship could not be plotted. Importantly, we found that the pristine conditions hypothesis does not hold regardless of the state of the witnessing condition. This research suggests that there are boundary conditions to the pristine conditions hypothesis and that further research is needed to determine the boundary conditions of the pristine conditions hypothesis.

Aïda Elamrani

With the significant progress of artificial intelligence (AI) and consciousness science, artificial consciousness (AC) has recently gained popularity. This work provides a broad overview of the main topics and current trends in AC. The first part traces the history of this interdisciplinary field to establish context and clarify key terminology, including the distinction between Weak and Strong AC. The second part examines major trends in AC implementations, emphasising the synergy between Global Workspace and Attention Schema, as well as the problem of evaluating the internal states of artificial systems. The third part analyses the ethical dimension of AC development, revealing both critical risks and transformative opportunities. The last part offers recommendations to guide AC research responsibly, and outlines the limitations of this study as well as avenues for future research. The main conclusion is that while AC appears both indispensable and inevitable for scientific progress, serious efforts are required to address the far-reaching impact of this innovative research path.

Gnankan Landry Regis N'guessan, Issa Karambal

Research on artificial consciousness lacks the equivalent of the perceptron: a small, trainable module that can be copied, benchmarked, and iteratively improved. We introduce the Reflexive Integrated Information Unit (RIIU), a recurrent cell that augments its hidden state $h$ with two additional vectors: (i) a meta-state $μ$ that records the cell's own causal footprint, and (ii) a broadcast buffer $B$ that exposes that footprint to the rest of the network. A sliding-window covariance and a differentiable Auto-$Φ$ surrogate let each RIIU maximize local information integration online. We prove that RIIUs (1) are end-to-end differentiable, (2) compose additively, and (3) perform $Φ$-monotone plasticity under gradient ascent. In an eight-way Grid-world, a four-layer RIIU agent restores $>90\%$ reward within 13 steps after actuator failure, twice as fast as a parameter-matched GRU, while maintaining a non-zero Auto-$Φ$ signal. By shrinking "consciousness-like" computation down to unit scale, RIIUs turn a philosophical debate into an empirical mathematical problem.

Shirmohammad Tavangari, Sajjad Janfaza, Zahra Shakarami et al.

This paper introduces a biomathematical model designed to describe the internal dynamics of dream formation and spontaneous cognitive processes. The model incorporates neurocognitive factors such as dissatisfaction, acceptance, forgetting, and mental activity, each of which is linked to established neural systems. We formulate a system of differential equations to simulate interactions among these variables and validate the model using simulated neural data. Our results demonstrate biologically plausible cognitive patterns consistent with findings from EEG and fMRI studies, particularly related to the default mode network (DMN), anterior cingulate cortex (ACC), and hippocampal memory mechanisms.

Tadahiro Taniguchi, Yasushi Hirai, Masahiro Suzuki et al.

This paper introduces the System 0/1/2/3 framework as an extension of dual-process theory, employing a quad-process model of cognition. Expanding upon System 1 (fast, intuitive thinking) and System 2 (slow, deliberative thinking), we incorporate System 0, which represents pre-cognitive embodied processes, and System 3, which encompasses collective intelligence and symbol emergence. We contextualize this model within Bergson's philosophy by adopting multi-scale time theory to unify the diverse temporal dynamics of cognition. System 0 emphasizes morphological computation and passive dynamics, illustrating how physical embodiment enables adaptive behavior without explicit neural processing. Systems 1 and 2 are explained from a constructive perspective, incorporating neurodynamical and AI viewpoints. In System 3, we introduce collective predictive coding to explain how societal-level adaptation and symbol emergence operate over extended timescales. This comprehensive framework ranges from rapid embodied reactions to slow-evolving collective intelligence, offering a unified perspective on cognition across multiple timescales, levels of abstraction, and forms of human intelligence. The System 0/1/2/3 model provides a novel theoretical foundation for understanding the interplay between adaptive and cognitive processes, thereby opening new avenues for research in cognitive science, AI, robotics, and collective intelligence.

Guoying Sun, Weiyu Guo, Tong Shao et al.

Brain decoding is a key neuroscience field that reconstructs the visual stimuli from brain activity with fMRI, which helps illuminate how the brain represents the world. fMRI-to-image reconstruction has achieved impressive progress by leveraging diffusion models. However, brain signals infused with prior knowledge and associations exhibit a significant information asymmetry when compared to raw visual features, still posing challenges for decoding fMRI representations under the supervision of images. Consequently, the reconstructed images often lack fine-grained visual fidelity, such as missing attributes and distorted spatial relationships. To tackle this challenge, we propose BrainCognizer, a novel brain decoding model inspired by human visual cognition, which explores multi-level semantics and correlations without fine-tuning of generative models. Specifically, BrainCognizer introduces two modules: the Cognitive Integration Module which incorporates prior human knowledge to extract hierarchical region semantics; and the Cognitive Correlation Module which captures contextual semantic relationships across regions. Incorporating these two modules enhances intra-region semantic consistency and maintains inter-region contextual associations, thereby facilitating fine-grained brain decoding. Moreover, we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual patterns and brain functions. Extensive quantitative and qualitative experiments demonstrate that BrainCognizer outperforms state-of-the-art approaches on multiple evaluation metrics.

Sebahat Gok, Robert L. Goldstone

Abstract Interactive computer simulations are commonly used as pedagogical tools to support students’ statistical reasoning. This paper examines whether and how these simulations enable their intended effects. We begin by contrasting two theoretical frameworks—dual processes and grounded cognition—in the context of people’s conceptions about statistical sampling, setting the stage for the potential benefits of simulations in learning such conceptions. Then, we continue with reviewing the educational literature on statistical sampling simulations. Our review tentatively suggests benefits of the simulations for building statistical habits of mind. However, challenges seem to persist when more specific concepts and skills are investigated. With and without simulations, students have difficulty forming an aggregate view of data, interpreting sampling distributions, showing a process-based understanding of the law of large numbers, making statistical inferences, and context-independent reasoning. We propose that grounded cognition offers a framework for understanding these findings, highlighting the bidirectional relationship between perception and conception, perceptual design features, and guided perceptual routines for supporting students’ meaning making from simulations. Finally, we propose testable instructional strategies for using simulations in statistics education.

Gordon Dodwell, Rebecca Nako, Martin Eimer

Representations of task-relevant object attributes (attentional templates) control the adaptive selectivity of visual processing. Previous studies have demonstrated that templates involved in the guidance of attention during visual search are activated in a preparatory fashion prior to the arrival of visual search displays. The current study investigated whether such proactive mechanisms are also triggered in non-search tasks, where attentional templates do not mediate the guidance of attention towards targets amongst distractors but are still necessary for subsequent target recognition processes. Participants either searched for colour-defined targets among multiple distractors or performed two other non-search tasks where imperative stimuli appeared without competing distractors (a colour-based Go/NoGo task, and a shape discrimination task where target colour was constant and could thus be ignored). Preparatory activation of colour-selective templates was tracked by measuring N2pc components (markers of attention allocation) to task-irrelevant colour singleton probes flashed every 200 ms during the interval between target displays. As expected, N2pcs were triggered by target-coloured probes in the search task, indicating that a corresponding guidance template was triggered proactively. Critically, clear probe N2pcs were also observed in the Go/NoGo task, and even in the shape discrimination task in an attenuated fashion. These findings demonstrate that the preparatory activation of feature-selective attentional task settings is not uniquely associated with the guidance of visual search but is also present in other types of visual selection tasks where guidance is not required.

Nuria Carriedo, Odir A. Rodríguez-Villagra, Odir A. Rodríguez-Villagra et al.

IntroductionSocial adaptation is a multifaceted process that encompasses cognitive, social, and affective factors. Previous research often focused on isolated variables, overlooking their interactions, especially in challenging environments. Our study addresses this by investigating how cognitive (working memory, verbal intelligence, self-regulation), social (affective empathy, family networks, loneliness), and psychological (locus of control, self-esteem, perceived stress) factors interact to influence social adaptation.MethodsWe analyzed data from 254 adults (55% female) aged 18 to 46 in economically vulnerable households in Santiago, Chile. We used Latent profile analysis (LPA) and machine learning to uncover distinct patters of socioadaptive features and identify the most discriminating features.ResultsLPA showed two distinct psychosocial adaptation profiles: one characterized by effective psychosocial adaptation and another by poor psychosocial adaptation. The adaptive profile featured individuals with strong emotional, cognitive, and behavioral self-regulation, an internal locus of control, high self-esteem, lower stress levels, reduced affective empathy, robust family support, and decreased loneliness. Conversely, the poorly adapted profile exhibited the opposite traits. Machine learning pinpointed six key differentiating factors in various adaptation pathways within the same vulnerable context: high self-esteem, cognitive and behavioral self-regulation, low stress levels, higher education, and increased social support.DiscussionThis research carries significant policy implications, highlighting the need to reinforce protective factors and psychological resources, such as self-esteem, self-regulation, and education, to foster effective adaptation in adversity. Additionally, we identified critical risk factors impacting social adaptation in vulnerable populations, advancing our understanding of this intricate phenomenon.

Wen Jiao

The COVID-19 pandemic, propelled by the highly transmissible Omicron variant, had a global impact and significantly affected Shanghai, a major city in China. This study investigates how food media exposure influenced unhealthy eating habits among older adults during the COVID-19 lockdown in Shanghai, focusing on the roles of negative emotions, food literacy, health consciousness, and eating self-efficacy. The random sample comprised 400 individuals aged ≥50 years who lived in Shanghai from March to June 2022. A path and correlation analysis was performed. The exposure of older adults to food media resulted in the acceleration of unhealthy eating. The relationship was significantly exacerbated by food literacy and negative emotions. In contrast, eating self-efficacy and health consciousness effectively countered the media. The enhanced pathway from food-related media influence to eating habits through negative emotions or self-efficacy towards health awareness and food literacy showed significant effects. The findings provide insights for future research and public health strategies. Importantly, this study has practical significance for media professionals, public health decision-makers, and healthy food businesses regarding how to enhance older adults’ cognition to respond to unhealthy eating crises.

Cammi K. Borden, Cammi K. Borden, Daniel G. McHail et al.

IntroductionReduced environmental oxygen levels at high altitudes can result in hypoxic hypoxia, which remains a primary threat in tactical aviation. Hypoxia broadly impairs cognition and can degrade a pilot's ability to safely operate the aircraft. Current hypoxia countermeasures include aircraft life support systems that deliver supplemental oxygen and using controlled hypoxia exposures to train aviators to recognize symptoms. To maximize the effectiveness of these countermeasures, it is critical to understand how hypoxia impacts performance and associated neurocognitive outcomes. We previously showed that a neural marker that indexes sensory processing integrity is sensitive to hypoxia impairment.MethodsHere, we extend this line of research closer to the training environment by using hypoxia simulation equipment currently standard in aviation survival training. In a single-blind, repeated-measures, counterbalanced design, we exposed 34 healthy participants to either normoxic air (ground level) or normobaric hypoxia (altitude equivalent gradually increasing from 10 to 25k') for 20 min after a 10 min baseline at ground level. During the exposure, participants completed a cognitive assessment battery while passively elicited neural responses to auditory tones were recorded using electroencephalography (EEG). Participants reported their hypoxia symptoms throughout and upon completion of their exposures.ResultsWe found that the hypoxia exposure rapidly elicited the predicted physiological responses in peripheral oxygen saturation (decrease) and heart rate (increase) within 2–3 minutes of exposure onset. On average, participants reported hypoxia symptoms in a delayed manner, ~8 min following the exposure onset. Performance on the cognitive tasks was relatively unaffected by hypoxia for basic tasks including Stroop, fine motor tracking, color vision and arithmetic, but was significantly degraded by hypoxia for more advanced tasks that combined a visual search component with Stroop and a working memory task. EEG activity associated with pre-attentive auditory processing was impaired on average shortly after the first symptom report, ~10 min from exposure start.DiscussionTogether, these results move hypoxia research closer to conditions encountered in aviation survival training and support the use of training devices for future hypoxia research.

Elżbieta Tabakowska

Works written in the cognitivist vein have been clearly inspired by and connected with Gestalt psychology – a fact recognized by scholars who describe the beginnings and subsequent development of cognitive theories of language. However, the discoverers of hidden aspects of the history of Cognitive Linguistics hardly ever put on their lists of forerunners the name of Roman Ingarden. And yet many of fundamental principles that underlie cognitivist theories of language and grammar can be found in Ingarden’s writings, notably in his The Cognition of the Literary Work of Art, first published in 1937 – exactly half a century before the year 1987, the annus mirabilis of Cognitive Linguistics, when its founding fathers published their groundbreaking monographs. Ingarden wrote about “literature”, while Langacker and his followers focus upon “non-literature”, i.e. text and discourse as elements of everyday communication. But both the (narrower) aesthetic concepts of Ingarden and the wider (linguistic) notions of Langacker, Lakoff or Talmy are based upon the fundamental opposition between the objectivist and the subjectivist approach. Most striking is the convergence of their view upon the shape of language as it occurs in verbal expression, inevitably connected with consciousness and mental activity of the producer: a cognizant subject of perception, conceptualization and expression. Deeper knowledge of Ingarden’s phenomenological thought might enrich cognitivist reflection on language and by taking account of phenomenological aspects of language use promote the search for markers of “everyday literariness”.

Zeina El-Ali, James R. Hebert, Michael D. Wirth et al.

Objective The association between sleep quality and overall health has been extensively examined. However, few studies have investigated the relationship between sleep and the inflammatory potential of the diet. Thus, the purpose of the present study was to explore the association between the scores on the Energy-Adjusted Dietary Inflammatory Index (E-DII) and sleep quality in Lebanese university students.

Sirui Chen, Shu Yu, Shengjie Zhao et al.

Self-consciousness, the introspection of one's existence and thoughts, represents a high-level cognitive process. As language models advance at an unprecedented pace, a critical question arises: Are these models becoming self-conscious? Drawing upon insights from psychological and neural science, this work presents a practical definition of self-consciousness for language models and refines ten core concepts. Our work pioneers an investigation into self-consciousness in language models by, for the first time, leveraging causal structural games to establish the functional definitions of the ten core concepts. Based on our definitions, we conduct a comprehensive four-stage experiment: quantification (evaluation of ten leading models), representation (visualization of self-consciousness within the models), manipulation (modification of the models' representation), and acquisition (fine-tuning the models on core concepts). Our findings indicate that although models are in the early stages of developing self-consciousness, there is a discernible representation of certain concepts within their internal mechanisms. However, these representations of self-consciousness are hard to manipulate positively at the current stage, yet they can be acquired through targeted fine-tuning. Our datasets and code are at https://github.com/OpenCausaLab/SelfConsciousness.

Alex B. Kiefer, Christopher L. Buckley

Although the latent spaces learned by distinct neural networks are not generally directly comparable, recent work in machine learning has shown that it is possible to use the similarities and differences among latent space vectors to derive "relative representations" with comparable representational power to their "absolute" counterparts, and which are nearly identical across models trained on similar data distributions. Apart from their intrinsic interest in revealing the underlying structure of learned latent spaces, relative representations are useful to compare representations across networks as a generic proxy for convergence, and for zero-shot model stitching. In this work we examine an extension of relative representations to discrete state-space models, using Clone-Structured Cognitive Graphs (CSCGs) for 2D spatial localization and navigation as a test case. Our work shows that the probability vectors computed during message passing can be used to define relative representations on CSCGs, enabling effective communication across agents trained using different random initializations and training sequences, and on only partially similar spaces. We introduce a technique for zero-shot model stitching that can be applied post hoc, without the need for using relative representations during training. This exploratory work is intended as a proof-of-concept for the application of relative representations to the study of cognitive maps in neuroscience and AI.

Raghavendra Pradyumna Pothukuchi, Leon Lufkin, Yu Jun Shen et al.

Research progress in quantum computing has, thus far, focused on a narrow set of application domains. Expanding the suite of quantum application domains is vital for the discovery of new software toolchains and architectural abstractions. In this work, we unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling. Cognitive models are critical to understanding and replicating human intelligence. Our work connects computational cognitive models to quantum computer architectures for the first time. We release QUATRO, a collection of quantum computing applications from cognitive models. The development and execution of QUATRO shed light on gaps in the quantum computing stack that need to be closed to ease programming and drive performance. Among several contributions, we propose and study ideas pertaining to quantum cloud scheduling (using data from gate- and annealing-based quantum computers), parallelization, and more. In the long run, we expect our research to lay the groundwork for more versatile quantum computer systems in the future.

N. Block

Is consciousness based in prefrontal circuits involved in cognitive processes like thought, reasoning, and memory or is it based in sensory areas in the back of the neocortex? The no-report paradigm has been crucial to this debate because it aims to separate the neural basis of the cognitive processes underlying post-perceptual decision and report from the neural basis of conscious perception itself. However, the no-report paradigm is problematic because, even in the absence of report, subjects might engage in post-perceptual cognitive processing. Therefore, to isolate the neural basis of consciousness, a no-cognition paradigm is needed. Here, I describe a no-cognition approach to binocular rivalry and outline how this approach can help to resolve debates about the neural basis of consciousness.