Abstract People often continue to rely on information even after it has been retracted–a phenomenon known as the continued-influence effect (CIE) of misinformation. This study investigated real-time indicators of misinformation susceptibility by recording the eye movements of 74 participants as they read pairs of newspaper-style articles containing critical information about the cause of an event that was either retracted or not. A post-reading questionnaire assessed memory for the passages and inferential judgements related to the retracted information. The roles of individual differences in language proficiency and working memory on the CIE were also tested. Questionnaire data replicated prior findings that repetition of the original information improved recall memory for the event. Eye-tracking data revealed that retractions were associated with increased processing effort during encoding of corrective information and reduced re-reading of non-causal details. Reminders of the original misinformation were linked to faster overall reading speeds. Higher reading proficiency predicted greater reductions in misinformation susceptibility, and both reading proficiency and verbal working memory capacity facilitated real-time processing of causal information. Finally, longer reading times and slower reading speeds were tentatively associated with reduced misinformation susceptibility but only when retractions were presented without explicit reminders. Together, these findings suggest that misinformation susceptibility reflects both individual differences in cognitive abilities and the effectiveness of reminder-based corrections.
This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. First, by constructing a network of environmental factor matrix to stimulate the learning process of the robot intelligent model, the model parameters must be subjected to coarse & fine tuning to optimize the loss function for minimizing the loss score, meanwhile robot intelligent model can fuse all previously known concepts together to represent things never experienced before, which need robot intelligent model can be generalized extensively. Secondly, in order to progressively develop a robot intelligent model with primary consciousness, every robot must be subjected to at least 1~3 years of special school for training anthropomorphic behaviour patterns to understand and process complex environmental information and make rational decisions. This work explores and delivers the potential application of deep learning-based quasi-consciousness training in the field of robot intelligent model.
Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind spot for understanding distributed cognition. This paper introduces "Cognitive Infrastructure Studies" (CIS) as a new interdisciplinary domain to reconceptualize AI as "cognitive infrastructures": foundational, often invisible systems conditioning what is knowable and actionable in digital societies. These semantic infrastructures transport meaning, operate through anticipatory personalization, and exhibit adaptive invisibility, making their influence difficult to detect. Critically, they automate "relevance judgment," shifting the "locus of epistemic agency" to non-human systems. Through narrative scenarios spanning individual (cognitive dependency), collective (democratic deliberation), and societal (governance) scales, we describe how cognitive infrastructures reshape human cognition, public reasoning, and social epistemologies. CIS aims to address how AI preprocessing reshapes distributed cognition across individual, collective, and cultural scales, requiring unprecedented integration of diverse disciplinary methods. The framework also addresses critical gaps across disciplines: cognitive science lacks population-scale preprocessing analysis capabilities, digital sociology cannot access individual cognitive mechanisms, and computational approaches miss cultural transmission dynamics. To achieve this goal CIS also provides methodological innovations for studying invisible algorithmic influence: "infrastructure breakdown methodologies", experimental approaches that reveal cognitive dependencies by systematically withdrawing AI preprocessing after periods of habituation.
Recent large scale behavioral studies suggest early emergence of gender differences in mathematical performance within months of school entry. However, these findings lack direct neural evidence and are constrained by cultural contexts. We conducted functional magnetic resonance imaging (fMRI) during mathematical tasks in Japanese participants (N = 156), employing an advanced wavelet time frequency analysis to examine dynamic brain processes rather than static activation patterns. Wavelet decomposition across four frequency bands (0.01-0.25 Hz) revealed that neural processing mechanisms underlying mathematical cognition are fundamentally similar between genders. Time frequency analysis demonstrated 89.1% similarity in dynamic activation patterns (p = 0.734, d = 0.05), with identical temporal sequences and frequency profiles during mathematical processing. Individual variation in neural dynamics exceeded group differences by 3.2:1 (p $<$ 0.001). Machine learning classifiers achieved only 53.8% accuracy in distinguishing gender based neural patterns essentially at chance level even when analyzing sophisticated temporal spectral features. Cross frequency coupling analysis revealed similar network coordination patterns between genders, indicating shared fundamental cognitive architecture. These findings provide robust process level neural evidence that gender similarities dominate mathematical cognition, particularly in early developmental stages, challenging recent claims of inherent differences and demonstrating that dynamic brain analysis reveals neural mechanisms that static behavioral assessments cannot access.
This paper proposes a novel theoretical model to explain how the human mind and artificial intelligence can approach real-time awareness by reducing perceptual delays. By investigating cosmic signal delay, neurological reaction times, and the ancient cognitive state of stillness, we explore how one may shift from reactive perception to a conscious interface with the near future. This paper introduces both a physical and cognitive model for perceiving the present not as a linear timestamp, but as an interference zone where early-arriving cosmic signals and reactive human delays intersect. We propose experimental approaches to test these ideas using human neural observation and neuro-receptive extensions. Finally, we propose a mathematical framework to guide the evolution of AI systems toward temporally efficient, ethically sound, and internally conscious decision-making processes
Katharina Wendebourg, Birgit Öttl, Detmar Meurers
et al.
Designing effective language learning settings requires an understanding of the processes taking place in language learning and the way they interact. One important issue concerns the interaction between meaning and grammar. A number of studies have shown a beneficial effect of semantics in grammar learning. What is unclear, however, is how far this effect may be influenced by the presentation formats of the semantic content. In two experiments, participants performed rule search tasks on Latin sentences. In Experiment 1, we presented semantic information in the form of naturalistic photographs, whereas in Experiment 2, the semantic information was implemented by quasi-translations. The control groups did not receive any semantic information. Learning performance was assessed by a grammaticality-judgment task combined with a source-attributions task. In both experiments, participants in the with-semantics group outperformed the respective control groups. Yet, only in Experiment 1, participants report having more explicit than implicit knowledge. We argue that semantic information boosts the acquisition of grammatical structures regardless of the presentation format. Furthermore, we suggest that, consistent with multimedia learning theories, the pictorial presentation format of Experiment 1 helped to use working memory capacity efficiently, which may have led to the generation of more explicit knowledge.
Memory for episodic associations declines with ageing due to decreased recollection abilities. Unitization—the encoding of multiple items as one integrated entity—has been shown to support familiarity-based retrieval that is independent of recollection and is relatively preserved in healthy ageing. Accordingly, unitization has been proposed as a promising strategy to attenuate age-related associative deficits, but evidence regarding its utility was lacking. The current study aimed to establish unitization as a viable mnemonic strategy. First, to ensure that unitization can attenuate the age-related associative deficit for initially unrelated materials, top-down unitization was used. Namely, participants were given an initially unrelated word pair in the context of either a definition which allows the words to be encoded as a unitized compound or a sentence in which the words are encoded as separate entities. Second, to ensure that unitization can be used as a self-initiated strategy, participants also completed the task by generating their own binding information (definitions/sentences). As expected, a unitization effect had emerged, such that associative memory was enhanced following definition encoding. However, this effect only occurred when binding information was provided. Additionally, a general memory advantage for the self-generation condition had emerged, but this was (generally) similar across unitization conditions and age groups. Taken together, the results show that unitization can be used as a mnemonic strategy under certain conditions, and highlight additional steps that should be taken before it can be effectively used beyond lab settings.
Abstract How do witnesses make identification decisions when viewing a lineup? Understanding the witness decision-making process is essential for researchers to develop methods that can reduce mistaken identifications and improve lineup practices. Yet, the inclusion of fillers has posed a pivotal challenge to this task because the traditional signal detection theory is only applicable to binary decisions and cannot easily incorporate lineup fillers. This paper proposes a multi-item signal detection theory (mSDT) model to help understand the witness decision-making process. The mSDT model clarifies the importance of considering the joint distributions of suspect and filler signals. The model also visualizes the joint distributions in a multivariate decision space, which allows for the incorporation of all eyewitness responses, including suspect identifications, filler identifications, and rejections. The paper begins with a set of simple assumptions to develop the mSDT model and then explores alternative assumptions that can potentially accommodate more sophisticated considerations. The paper further discusses the implications of the mSDT model. With a mathematical modeling and visualization approach, the mSDT model provides a novel theoretical framework for understanding eyewitness identification decisions and addressing debates around eyewitness SDT and ROC applications.
This paper explores self-tracking as a social practice with significant relationships to human memory. The history of data and memory is fraught with a concern that specifics of qualia are subjugated to datafication. Yet, historical perspectives linking paper diaries and digital tracking show that rich accounts can be preserved in media. Cognisant of both perspectives, this paper argues that rather than delegating reflection to algorithms, users engage critically. Using original research data, this paper demonstrates how users unite the sociotechnical affordances of devices, data visualisations, and personal narratives to communicate memory in mediated forms. In doing so, they bridge semantic and autobiographic memory, combining subjectivity and objectivity. A datafied narration of everyday life emerges, affirming unique and vital stories. Often directed toward future goals, the mnemonic value of self-tracking in the present is overlooked. Yet whether recalling unfortunate accidents, sporting success, work, holidays, or illness experiences, participants use data as a scaffold to build stories and affirm identity. This paper asserts that memory and storytelling is an essential anchor for practices of digital self-tracking.
Communication. Mass media, Consciousness. Cognition
Lorena Caligiuri Lemes, Maria Isabel Triches, Cristiano Carvalho
et al.
Introduction Decreased sleep time is detrimental to physical and mental health, exerting negative impacts on daily functioning and quality of life. Mobile health (mHealth) applications can be useful for improving sleep quality.
Parkinsons disease (PD) diagnosis is challenging due to subtle early clinical signs. F-DOPA PET is commonly used for early PD diagnosis. We explore the potential of machine-learning (ML) based EEG features extracted from single-channel EEG during auditory cognitive assessment as a noninvasive, low-cost support for PD diagnosis. The study included data collected from 32 participants who underwent an F-DOPA PET scan as part of their standard treatment and 20 cognitively healthy controls. Participants performed an auditory cognitive assessment recorded with Neurosteer EEG device. Data processing involved wavelet-packet decomposition and ML. First, a prediction model was developed to predict 1/3 of the undisclosed F-DOPA results. Then, generalized linear mixed models were calculated to distinguish between PD and non-PD subjects on the frequency bands and ML-based EEG features (A0 and L1) previously associated with cognitive functions. The prediction model accurately labeled patients with unrevealed scores as positive F-DOPA. Novel EEG feature A0 and the Delta band showed significant separation between study groups, with healthy controls exhibiting higher activity than PD patients. EEG feature L1 activity was significantly lower in resting state compared to high-cognitive load. This effect was absent in the PD group, suggesting that lower activity in resting state is lacking in PD patients. This study successfully demonstrated the ability to separate patients with positive vs. negative F-DOPA PET results with an easy-to-use single-channel EEG during an auditory cognitive assessment. Future longitudinal studies should further explore the potential utility of this tool for early PD diagnosis and as a potential biomarker in PD.
Lukas Schumacher, Martin Schnuerch, Andreas Voss
et al.
Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.
To make accurate inferences in an interactive setting, an agent must not confuse passive observation of events with having intervened to cause them. The $do$ operator formalises interventions so that we may reason about their effect. Yet there exist pareto optimal mathematical formalisms of general intelligence in an interactive setting which, presupposing no explicit representation of intervention, make maximally accurate inferences. We examine one such formalism. We show that in the absence of a $do$ operator, an intervention can be represented by a variable. We then argue that variables are abstractions, and that need to explicitly represent interventions in advance arises only because we presuppose these sorts of abstractions. The aforementioned formalism avoids this and so, initial conditions permitting, representations of relevant causal interventions will emerge through induction. These emergent abstractions function as representations of one`s self and of any other object, inasmuch as the interventions of those objects impact the satisfaction of goals. We argue that this explains how one might reason about one`s own identity and intent, those of others, of one`s own as perceived by others and so on. In a narrow sense this describes what it is to be aware, and is a mechanistic explanation of aspects of consciousness.
One of the current AI issues depicted in popular culture is the fear of conscious super AIs that try to take control over humanity. And as computational power goes upwards and that turns more and more into a reality, understanding artificial brains might be increasingly important to control and drive AI towards the benefit of our societies. This paper proposes a base framework to aid the development of autonomous multipurpose artificial brains. To approach that, we propose to first model the functioning of the human brain by reflecting and taking inspiration from the way the body, the consciousness and the unconsciousness interact. To do that, we tried to model events such as sensing, thinking, dreaming and acting, thoughtfully or unconsciously. We believe valuable insights can already be drawn from the analysis and critique of the presented framework, and that it might be worth implementing it, with or without changes, to create, study, understand and control artificially conscious systems.
The evolution of the human mind through natural selection mandates that our conscious experiences are causally potent in order to leave a tangible impact upon the surrounding physical world. Any attempt to construct a functional theory of the conscious mind within the framework of classical physics, however, inevitably leads to causally impotent conscious experiences in direct contradiction to evolution theory. Here, we derive several rigorous theorems that identify the origin of the latter impasse in the mathematical properties of ordinary differential equations employed in combination with the alleged functional production of the mind by the brain. Then, we demonstrate that a mind--brain theory consistent with causally potent conscious experiences is provided by modern quantum physics, in which the unobservable conscious mind is reductively identified with the quantum state of the brain and the observable brain is constructed by the physical measurement of quantum brain observables. The resulting quantum stochastic dynamics obtained from sequential quantum measurements of the brain is governed by stochastic differential equations, which permit genuine free will exercised through sequential conscious choices of future courses of action. Thus, quantum reductionism provides a solid theoretical foundation for the causal potency of consciousness, free will and cultural transmission.
Paul Stoewer, Achim Schilling, Andreas Maier
et al.
The human brain possesses the extraordinary capability to contextualize the information it receives from our environment. The entorhinal-hippocampal plays a critical role in this function, as it is deeply engaged in memory processing and constructing cognitive maps using place and grid cells. Comprehending and leveraging this ability could significantly augment the field of artificial intelligence. The multi-scale successor representation serves as a good model for the functionality of place and grid cells and has already shown promise in this role. Here, we introduce a model that employs successor representations and neural networks, along with word embedding vectors, to construct a cognitive map of three separate concepts. The network adeptly learns two different scaled maps and situates new information in proximity to related pre-existing representations. The dispersion of information across the cognitive map varies according to its scale - either being heavily concentrated, resulting in the formation of the three concepts, or spread evenly throughout the map. We suggest that our model could potentially improve current AI models by providing multi-modal context information to any input, based on a similarity metric for the input and pre-existing knowledge representations.
Resting state networks comprise several brain regions that exhibit complex patterns of interaction. Switching from eyes closed (EC) to eyes open (EO) during the resting state modifies these patterns of connectivity, but precisely how these change remains unclear. Here we use functional magnetic resonance imaging to scan healthy participants in two resting conditions (viz., EC and EO). Seven resting state networks were chosen for this study: salience network (SN), default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), visual network (VN), motor network (MN) and auditory network (AN). We performed functional connectivity (FC) analysis for each network, comparing the FC maps for both EC and EO. Our results show increased connectivity between most networks during EC relative to EO, thereby suggesting enhanced integration during EC and greater modularity or specialization during EO. Among these networks, SN is distinctive: during the transition from EO to EC it evinces increased connectivity with DMN and decreased connectivity with VN. This change might imply that SN functions in a manner analogous to a circuit switch, modulating resting state relations with DMN and VN, when transitioning between EO and EC.