Hasil untuk "Consciousness. Cognition"

Juan Manuel Toro

Humans develop biases during language learning. For example, we rely more heavily on consonants than on vowels to identify words. Advances on artificial intelligence have allowed the development of proficient large language models that sometimes mimic humans’ language use. They do so by tracking regularities in natural language datasets that are used to train them. Here we test the hypothesis that tracking such regularities is enough for the emergence of responses that resemble the consonant bias. We asked ChatGPT which of two nonsense words (one with a vowel and one with a consonant change) was more similar to a target word. We observed that the model uses more the consonants than the vowels to perform similarity judgments across words in the two languages that we tested (English and Spanish).

Arie T. Greenleaf

Plant cognition has progressed from anecdote to rigor, yet the field still lacks a quantitative test for when distributed plant activity crosses into unified – perhaps conscious – processing. I introduce Pattern–Temporal Synergy (PTS), a substrate-agnostic metric rooted in Dynergeia, a relation-first ontology in which consciousness is reflexive coherence among five universal patterns – self-reference, division-creation, information integration, responsiveness, and flux – phase-locked inside a system’s binding window (τ). Each pattern is operationalized with established signal-processing measures; their median strength is multiplied by their mean synergy and released only if a τ-specific coherence gate is met. Three preregistered hypotheses anchor the study: H1 baseline PTS > 0 in intact plants; H2 4% diethyl-ether collapses PTS below threshold ϕ; H3 PTS rebounds on wash-out. A multispecies protocol – Mimosa pudica, Arabidopsis thaliana, Picea abies – combines 64-channel surface electrodes, glutamate-sensitive Ca2+ imaging and micro-optode O2/heat-flux probes. Sliding 3 ×τ windows with phase-shuffled surrogates yield z-scored PTS trajectories, adjudicated by preregistered effect-size criteria. By turning decades of qualitative insight into falsifiable numbers, PTS offers plant biology a litmus test for conscious-level processing, directly challenges Integrated Information Theory and supplies a road-map for cross-kingdom comparisons – including neuromorphic silicon. Confirmatory results would shift debates on plant sentience from speculation to data; null results would equally refine what consciousness requires.

Francois R. Foerster, Anne Giersch, Paola Agalliu et al.

Abstract Temporal acuity reflects our ability to consciously detect a perceptual change within a short period of time, such as an asynchrony separating two visual events. In this virtual reality study, fifty participants performed a simultaneity judgment task to estimate temporal acuity across the visual field and filled the schizotypal personality questionnaire. Topographic maps were computed to visualize asynchrony discrimination skills across the visual space in two different (natural and artificial) static virtual environments. We investigate visual temporal acuity in periphery, and how estimates of temporal acuity in a psychophysical-like setting translates into a naturalistic-like scenario. First, the temporal acuity of vision decreases as the eccentricity of the targets increases, but it remains constant across meridians. Second, this deterioration of temporal coding in peripheral vision concerns non-medicated individuals self-reporting perceptual and cognitive schizotypal traits. Third, temporal acuity estimated in a traditional psychophysical visual context does not generalize to an ecologically-valid landscape scenery, such that asynchrony discrimination skills are reduced under natural vision conditions. The results suggest that distinct temporal mechanisms drive visual temporal acuity in central and peripheral vision. Furthermore, perceptual and cognitive disturbances in the neurotypical population may be linked to abnormal temporal processing in peripheral vision. Overall, these findings may pave the way toward novel investigations into the variety of time experiences across neurotypical and neurodivergent populations.

Chen Zhao, Rui Xu, Tingting Sun

This study investigated the effects of audience design and goal bias in Chinese speakers’ message generation of source-goal motion events (e.g., A bird flies from the tree to the house), using picture description and memory tasks. The status of the source (e.g., the tree) or the goal (e.g., the house) was manipulated as known or unknown to the confederate addressees. The findings revealed that the participants were more likely to omit the sources when they were mutually known to the addressee than when they were not. However, participants showed similar accuracy in detecting source changes, regardless of whether the sources were known to the addressee. Moreover, they consistently mentioned goals and showed similar accuracy in detecting goal changes, regardless of whether the goals were known or unknown to the addressee. The results suggest that audience design influenced the speakers’ mention of sources, but not their memory of them. It did not affect either the mention or the memory of goals. Goal bias was not consistently observed across the two experiments, both linguistically and in memory. This suggests a fragile goal bias in Chinese. Taken together, these findings demonstrate that audience design and goal bias influence the message generation of motion events in Chinese speakers.

Frank Heile

We propose that consciousness arises from a single control agent, the Modeler-schema. It monitors the brain's Modeler as that system constructs and updates the internal World Model. As part of that monitoring, the Modeler-schema generates experience by applying a qualia-based consistency check to the Modeler's output. The Human Agent comprises three cooperating agents: Modeler, Controller, and Targeter, each paired with an associated regulatory "schema" agent. We also describe fast-Modelers and fast-Controllers; evolutionary shortcuts whose rapid actions will precede awareness. Our core prediction is that the Modeler-schema performs a qualia-based consistency check during saccades and issues a bottom-up target when a discrepancy is found. To test this prediction, we propose a saccadic change-detection experiment that distinguishes Modeler-generated from Modeler-schema-generated targets. Locating qualia in the Modeler-schema ties experience to the regulation and refinement of internal representations, clarifies how awareness arises from model control, and suggests a path toward empirical falsification, thereby offering a concrete, testable proposal toward solving the Hard Problem of consciousness.

Denise Moerel, Tijl Grootswagers

In cognitive neuroscience research, Representational Dissimilarity Matrices (RDMs) are often incomplete because pairwise similarity judgments cannot always be exhaustively collected as the number of pairs rapidly increases with the number of conditions. Existing methods to fill these missing values, such as deep neural network imputation, are powerful but computationally demanding and relatively opaque. We introduce a simple algorithm based on geometric inference that fills missing dissimilarity matrix entries using known distances. We use tests on publicly available empirical cognitive neuroscience datasets, as well as simulations, to demonstrate the method's effectiveness and robustness across varying sparsity and matrix sizes. We have made this geometric reconstruction algorithm, implemented in Python and MATLAB, publicly available. This method provides a fast and accurate solution for completing partial dissimilarity matrices in the cognitive neurosciences.

Neta Batya Maimon, Lior Molcho, Talya Zaimer et al.

Identifying neural markers of stress and cognitive load is key to developing scalable tools for mental state assessment. This study evaluated whether a single-channel high-density EEG (hdrEEG) system could dissociate cognitive and stress-related activity during a brief auditory task-based protocol. Sixty-eight healthy adults completed resting state recordings, cognitively demanding auditory tasks, and exposure to unpredictable literalized startle stimuli. Participants also rated their stress and anxiety using a modified State-Trait Anxiety Inventory (STAI). EEG analysis focused on frequency bands (Theta, Gamma, Delta) and machine-learning-derived features (A0, ST4, VC9, T2). A double dissociation emerged: Theta and VC9 increased under cognitive load but not startle, supporting their sensitivity to executive function. In contrast, Gamma and A0 were elevated by the startle stimulus, consistent with stress reactivity. ST4 tracked cognitive effort and worry, while T2 negatively correlated with self-reported calmness, indicating relevance to emotional regulation. These results demonstrate that a short, uniform assessment using portable EEG can yield multiple reliable biomarkers of cognitive and affective states. The findings have implications for clinical, occupational, and educational settings, and may inform future neurofeedback protocols targeting simultaneous regulation of attention and stress.

Ducel Jean-Berluche

This review examines the transformative impact creativity has on mental health. Creative expression has the potential to promote the cognitive, emotional, physical, and social well-being of individuals of all ages. Drawing from various scholarly sources, including empirical studies and theoretical frameworks, this review synthesizes the current knowledge on the relationship between creativity and mental health. The review elucidates how creativity influences emotional regulation, cognitive flexibility, and social connectedness. Through a detailed literature search utilizing databases such as PubMed, PsycINFO, PsychARTICLES, and Google Scholar, research findings from articles across different creative activities, including visual arts, writing, music, and crafts/DIY projects, are discussed in conjunction with reported benefits on mental health and well-being. Furthermore, the review discusses the practical implications of the positive link between creative expression and mental health, emphasizing the relevance of this for therapeutic interventions and community programs. The findings highlight the need for further research to explore the underlying mechanisms, long-term effects, and potential cultural variations of the creativity-mental health relationship. This review provides a comprehensive overview of the positive influences, inviting researchers, practitioners, and policymakers to harness the healing power of creative expression.

Benoit Le Blanc

Thomas R. Zentall, Daniel N. Peng

Abstract Bonobos appear to show little evidence of learning to make one response (R1) to an AB sequence and a different response (R2) to sequences BB, AA, and BA (Lind et al. PLoS ONE 18(9):e0290546, 2023), yet under different conditions, pigeons can learn this (Weisman et al. Exp Psychol Anim Behav Process 6(4):312, 1980). Aspects of the bonobo procedure may have contributed to this failure. Most important, no response was required in the presence of the stimuli to encourage attention to them. Furthermore, learning to make one response to the target sequence and another to the other sequences involves a bias that allows for better than chance responding. With the two-alternative forced-choice procedure used with the bonobos, the R1 response is correct for one sequence, whereas the R2 response is correct for three sequences. To correct for this, there are three times as many AB trials as each of the other sequences. However, this correction allows a bias to develop in which reinforcement often can be obtained by using only the last stimulus seen as the basis of choice (e.g., when the last stimulus is B respond R1 when the last stimulus is A respond R2). This solution yields reinforcement on five out of six, or 83%, of the trials. In the present experiment with pigeons, using this two-alternative forced choice procedure, most subjects tended to base their choice on the last-seen stimulus. This design allowed subjects to use a suboptimal but relatively effective choice strategy.

Hanzhong Zhang, Jibin Yin, Haoyang Wang et al.

Large Language Models (LLMs) still face challenges in tasks requiring understanding implicit instructions and applying common-sense knowledge. In such scenarios, LLMs may require multiple attempts to achieve human-level performance, potentially leading to inaccurate responses or inferences in practical environments, affecting their long-term consistency and behavior. This paper introduces the Internal Time-Consciousness Machine (ITCM), a computational consciousness structure to simulate the process of human consciousness. We further propose the ITCM-based Agent (ITCMA), which supports action generation and reasoning in open-world settings, and can independently complete tasks. ITCMA enhances LLMs' ability to understand implicit instructions and apply common-sense knowledge by considering agents' interaction and reasoning with the environment. Evaluations in the Alfworld environment show that trained ITCMA outperforms the state-of-the-art (SOTA) by 9% on the seen set. Even untrained ITCMA achieves a 96% task completion rate on the seen set, 5% higher than SOTA, indicating its superiority over traditional intelligent agents in utility and generalization. In real-world tasks with quadruped robots, the untrained ITCMA achieves an 85% task completion rate, which is close to its performance in the unseen set, demonstrating its comparable utility and universality in real-world settings.

Saima Rathore, Jatin Chaudhary, Boning Tong et al.

Cerebral microbleeds, markers of brain damage from vascular and amyloid pathologies, are linked to cognitive decline in aging, but their role in Alzheimer's disease (AD) onset and progression remains unclear. This study aimed to explore whether the presence and location of lobar microbleeds are associated with amyloid-$β$ (A$β$)-PET, tau tangle formation (tau-PET), and longitudinal cognitive decline. We analyzed 1,573 ADNI participants with MR imaging data and information on the number and location of microbleeds. Associations between lobar microbleeds and pathology, cerebrospinal fluid (CSF), genetics, and cognition were examined, focusing on regional microbleeds and domain-specific cognitive decline using ordinary least-squares regression while adjusting for covariates. Cognitive decline was assessed with ADAS-Cog11 and its domain-specific sub-scores. Participants underwent neuropsychological testing at least twice, with a minimum two-year interval between assessments. Among the 1,573 participants (692 women, mean age 71.23 years), 373 participants had microbleeds. The presence of microbleeds was linked to cognitive decline, particularly in the semantic, language, and praxis domains for those with temporal lobe microbleeds. Microbleeds in the overall cortex were associated with language decline. Pathologically, temporal lobe microbleeds were associated with increased tau in the overall cortex, while cortical microbleeds were linked to elevated A$β$ in the temporal, parietal, and frontal regions. In this mixed population, microbleeds were connected to longitudinal cognitive decline, especially in semantic and language domains, and were associated with higher baseline A$β$ and tau pathology. These findings suggest that lobar microbleeds should be included in AD diagnostic and prognostic evaluations.

Bryce Allen Bagley, Claudia K Petritsch

The rapid advancement in neurotechnology in recent years has created an emerging critical intersection between neurotechnology and security. Implantable devices, non-invasive monitoring, and non-invasive therapies all carry with them the prospect of violating the privacy and autonomy of individuals' cognition. A growing number of scientists and physicians have made calls to address this issue, but applied efforts have been relatively limited. A major barrier hampering scientific and engineering efforts to address these security issues is the lack of a clear means of describing and analyzing relevant problems. In this paper we develop Cognitive Neurosecurity, a mathematical framework which enables such description and analysis by drawing on methods and results from multiple fields. We demonstrate certain statistical properties which have significant implications for Cognitive Neurosecurity, and then present descriptions of the algorithmic problems faced by attackers attempting to violate privacy and autonomy, and defenders attempting to obstruct such attempts.

Robert L. West, Spencer Eckler, Brendan Conway-Smith et al.

This article presents a theoretical framework for adapting the Common Model of Cognition to large generative network models within the field of artificial intelligence. This can be accomplished by restructuring modules within the Common Model into shadow production systems that are peripheral to a central production system, which handles higher-level reasoning based on the shadow productions' output. Implementing this novel structure within the Common Model allows for a seamless connection between cognitive architectures and generative neural networks.

G. Mashour, D. Pal, E. Brown

The role of the prefrontal cortex (PFC) in the mechanism of consciousness is a matter of active debate. Most theoretical and empirical investigations have focused on whether the PFC is critical for the content of consciousness (i.e., the qualitative aspects of conscious experience). However, there is emerging evidence that, in addition to its well-established roles in cognition, the PFC is a key regulator of the level of consciousness (i.e., the global state of arousal). In this opinion article we review recent data supporting the hypothesis that the medial PFC is a critical node in arousal-promoting networks.

Jarrod Gott, Sina Stücker, Philipp Kanske et al.

Acetylcholine is a neurotransmitter and neuromodulator involved in a variety of cognitive functions. Additionally, acetylcholine is involved in the regulation of REM sleep: cholinergic neurons in the brainstem and basal forebrain project to and innervate wide areas of the cerebral cortex, and reciprocally interact with other neuromodulatory systems, to produce the sleep-wake cycle and different sleep stages. Consciousness and cognition vary considerably across and within sleep stages, with metacognitive capacity being strikingly reduced even during aesthetically and emotionally rich dream experiences. A notable exception is the phenomenon of lucid dreaming-a rare state whereby waking levels of metacognitive awareness are restored during sleep-resulting in individuals becoming aware of the fact that they are dreaming. The role of neurotransmitters in these fluctuations of consciousness and cognition during sleep is still poorly understood. While recent studies using acetylcholinesterase inhibitors suggest a potential role of acetylcholine in the occurrence of lucid dreaming, the underlying mechanisms by which this effect is produced remains un-modelled and unknown; with the causal link between cholinergic mechanisms and upstream psychological states being complex and elusive. Several theories and approaches targeting the association between acetylcholine and metacognition during wakefulness and sleep are highlighted in this review, moving through microscopic, mesoscopic and macroscopic levels of analysis to detail this phenomenon at several organisational scales. Several exploratory hypotheses will be developed to guide future research towards fully articulating how metacognition is affected by activity at the acetylcholine receptor.

Loukia Tzavella, Christopher D. Chambers

The overvaluation of reward-associated stimuli such as energy-dense foods can drive compulsive eating behaviours, including overeating. Previous research has shown that training individuals to inhibit their responses towards appetitive stimuli can lead to their devaluation, providing a potential avenue for behaviour change. Over two preregistered experiments, we investigated whether training participants to inhibit their responses to specific foods would be effective in reducing their evaluations when these were assessed using both explicit and implicit measures. Participants completed an online session of go/no-go training with energy-dense foods that were consistently associated with either responding (go) or inhibiting a response (no-go). An ‘explicit’ devaluation effect was expected as a reduction in self-reported liking from pre-to post-training for no-go items compared to both go items and foods that were not presented during training (untrained items). An ‘implicit’ devaluation effect was then measured using the affective priming paradigm, by comparing differences in reaction times for congruent and incongruent trials (i.e., priming effects) between food primes. Experiment 1 revealed conclusive evidence for small-to-medium devaluation effects both in terms of explicit ratings and priming effects. We also observed that the priming effect for no-go items was close to zero. Experiment 2 successfully replicated most of the preregistered and exploratory outcomes from Experiment 1 except for the priming effect for untrained items. Potential explanations for this discrepancy are discussed but overall, these findings provide further support for a devaluation effect of response inhibition training. To our knowledge, our study provides the first evidence that training-induced devaluation can potentially be captured by affective priming measures, but more research is needed to further assess their sensitivity before they can be used to elucidate the mechanisms of action underlying devaluation effects.

Cedegao E. Zhang, Katherine M. Collins, Adrian Weller et al.

Mathematics is one of the most powerful conceptual systems developed and used by the human species. Dreams of automated mathematicians have a storied history in artificial intelligence (AI). Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems. In this work, we reflect on these goals from a \textit{cognitive science} perspective. We call attention to several classical and ongoing research directions from cognitive science, which we believe are valuable for AI practitioners to consider when seeking to build truly human (or superhuman)-level mathematical systems. We close with open discussions and questions that we believe necessitate a multi-disciplinary perspective -- cognitive scientists working in tandem with AI researchers and mathematicians -- as we move toward better mathematical AI systems which not only help us push the frontier of the mathematics, but also offer glimpses into how we as humans are even capable of such great cognitive feats.

Ivan S. Maksymov, Ganna Pogrebna

This paper introduces a quantum-mechanical model that bridges the realms of cognition and quantum mechanics, offering a novel perspective on decision-making under risk and perceptual reversals. By integrating quantum theories addressing decision-theoretic anomalies with examples from immersive video games like "Deal or No Deal", we seek to elucidate complex human cognitive behaviours. Study 1 showcases the proposed quantum model's superiority over traditional decision-making approaches using the "Deal or No Deal" video game experiment. In Study 2, we apply our model to bistable perceptions, taking the Necker cube from the Necker game as a primary example. While previous works have hinted at connections between quantum mechanics and cognition, Study 3 provides a more tangible link, likening the physics that underpins quantum tunnelling to an eye blink's role in perceptual reversals. Conclusively, our model displays a promising ability to interpret diverse optical illusions and psychological phenomena, marking a significant stride in understanding human decision making.

John Rushby

A singular attribute of humankind is our ability to undertake novel, cooperative behavior, or teamwork. This requires that we can communicate goals, plans, and ideas between the brains of individuals to create shared intentionality. Using the information processing model of David Marr, I derive necessary characteristics of basic mechanisms to enable shared intentionality between prelinguistic computational agents and indicate how these could be implemented in present-day AI-based robots. More speculatively, I suggest the mechanisms derived by this thought experiment apply to humans and extend to provide explanations for human rationality and aspects of intentional and phenomenal consciousness that accord with observation. This yields what I call the Shared Intentionality First Theory (SIFT) for rationality and consciousness. The significance of shared intentionality has been recognized and advocated previously, but typically from a sociological or behavioral point of view. SIFT complements prior work by applying a computer science perspective to the underlying mechanisms.