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This document is a response to a comment made by Robertas Damaševičius regarding an article on info-autopoiesis and artificial general intelligence (AGI). The response addresses specific points raised in the comment and clarifies the author's position. It emphasizes that info-autopoiesis, the self-creation of information, is fundamental only to humans and not achievable by artificial creations. The article argues for the impossibility of achieving AGI using info-autopoiesis and suggests that resources should be directed towards more achievable goals in AI. The response also discusses the relationship between biological aspects of human intelligence and AI research, highlighting the use of neural networks and considering the architecture of AGI. It concludes by stating that info-autopoiesis sets a limit to human knowledge based on fundamental principles and that all human creations are syntactic and incapable of semantic interpretation. The text discusses the concept of info-autopoiesis and its application to AI research. Info-autopoiesis is a framework that aims to explain how living organisms function, and the paper explores its potential implications for AI and AGI development. The author acknowledges the limitations of info-autopoiesis and emphasizes the need for interdisciplinary collaboration and ethical considerations in AI research. The text concludes by suggesting areas for further research and expressing gratitude for the comments and suggestions provided by Robertas Damaševičius. [Extracted from the article]

To what extent does playing a musical instrument contribute to an individual’s construction of knowledge? This paper aims to address this question by examining music performance from an embodied perspective and offering a narrative-style review of the main literature on the topic. Drawing from both older theoretical frameworks on motor learning and more recent theories on sensorimotor coupling and integration, this paper seeks to challenge and juxtapose established ideas with contemporary views inspired by recent work on embodied cognitive science. By doing so we advocate a centripetal approach to music performance, contrasting the prevalent centrifugal perspective: the sounds produced during performance not only originate from bodily action (centrifugal), but also cyclically return to it (centripetal). This perspective suggests that playing music involves a dynamic integration of both external and internal factors, transcending mere output-oriented actions and revealing music performance as a form of knowledge acquisition based on real-time sensorimotor experience. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures. [ABSTRACT FROM AUTHOR]

This study utilizes mathematical formalism and Figure-Ground argument mapping to categorize stative locative events in Vietnamese as surjective or bijective. Bijective events exhibit a one-to-one mapping of Figures and Grounds, while surjectives allow a single Figure to map onto multiple Grounds. Through analysis of Vietnamese sentences, the paper demonstrates how bijective and surjective locatives encode deterministic and ambiguous spatial relations between Figures and Grounds, respectively. This novel distinction aids in enhancing the understanding of locative event typologies and provides an explanatory model for cross-linguistic analysis. The proposed integrated formal framework combines predicate logic, set theory, and lexical semantics to account for complex locative meanings, with significant applications in computational and theoretical linguistics, as well as cognitive science. [ABSTRACT FROM AUTHOR]

Ignorance, or the lack of knowledge, appears to be steadily spreading, despite the increasing availability of information. The notion of informed ignorance herein proposed to describe the widespread position of being exposed to an abundance of information yet lacking relevant knowledge, which is tied to the exponential growth in misinformation driven by technological developments and social media. Linked to many of societies' most looming catastrophes, from political polarization to the climate crisis, practices related to knowledge and information are deemed some of the most imminent and daunting modern threats, evidenced by the latest report of the World Economic Forum, which has named misinformation the most severe short-term global risk. This paper's epistemic perspective links the properties of today's information culture and the ways in which it interacts with individual capacities and limitations in current technological and socio-political contexts. Such a position is analyzed through the lens of epistemic principles as a contemporary epistemic phenotype that emerges from an environment of ill-adapted and excessive information inputs and leads to a distinctive type of social injustice that is primarily epistemic in nature. While equity and accessibility are widely discussed as important contributing factors to epistemic discrepancies, other overlooked but fundamental issues underlying epistemic injustices are considered, such as information manipulation, cognitive limitations, and epistemic degradation. To effectively face this elusive threat, we propose an inclusive viewpoint that harnesses knowledge from cognitive science, science and technology studies, and social epistemology to inform a unifying theory of its main impacts and driving forces. By adjusting a modern epistemic framework to the described phenomena, we intend to contextually outline its trajectory and possible means of containment based on a shared responsibility to maintain ethical epistemic standards. In a time of international unrest and mounting civil acts of violence, it is pertinent to emphasize the ethical principles of knowledge systems and authorities and suggest policy adaptations to maintain a social contract based on the shared values of truth and freedom. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between societal structures shaped by traditions, norms, laws, and customs, and creative expressions in arts and media through the lens of the predictive coding framework in cognitive science. The article proposes that both dimensions of culture can be viewed as adaptations designed to enhance and train the brain's predictive abilities in the social domain. Traditions, norms, laws, and customs foster shared predictions and expectations among individuals, thereby reducing uncertainty in social environments. On the other hand, arts and media expose us to simulated experiences that explore alternative social realities, allowing the predictive machinery of the brain to hone its skills through exposure to a wider array of potentially relevant social circumstances and scenarios. We first review key principles of predictive coding and active inference, and then explore the rationale of cultural traditions and artistic culture in this perspective. Finally, we draw parallels between institutionalized normative habits that stabilize social worlds and creative and imaginative acts that temporarily subvert established conventions to inject variability. [ABSTRACT FROM AUTHOR]

This paper aims to show that properties of cognitive/conceptual representations and formal‐logical structures of linguistic meaning can be inter‐translated, recast, transformed into one another, and so united together, even though cognitive/conceptual representations and formal‐logical structures of linguistic meaning are apparently distinct in ontology and divergent in their form or character. While cognitive/conceptual representations are ultimately rooted in sensory‐motor systems, formal‐logical structures of linguistic meaning are abstractions detached from and independent of the actualized world. This paper sketches out the foundations of how representations of linguistic meaning in terms of cognitive/conceptual structures in Cognitive/Conceptual Semantics can be unified with those in terms of formal‐logical structures in Formal Semantics. This is done by recasting cognitive/conceptual representations in terms of formal‐logical structures of linguistic meaning and re‐encoding formal‐logical structures of linguistic meaning in terms of cognitive/conceptual representations. Then, these two types of semantic representations, thus shown representationally equivalent, will be related to a series of derivations across levels in neuronal networks and dynamics. The general discussion on unifying cognitive/conceptual representations of linguistic meaning with formal‐logical structures is contextualized within the broader context of theorizing in cognitive science. [ABSTRACT FROM AUTHOR]

The victories of the Go-playing artificial intelligence (AI) "AlphaGo" against professional player Lee Sedol in 2016 had a profound impact on public and academic perceptions of AI. This event shocked observers, as the ability of a machine to defeat a world champion human in a highly complex game seemed to indicate that a machine had achieved human-like—or more than human—intelligence. But why was AlphaGo so readily anthropomorphized by academic and non-academic audiences alike? Drawing from existing analyses of reactions to and arguments concerning AlphaGo and AI generally, this paper argues that "generative" cognitive science—a school of thought exemplified by the linguistic work of Noam Chomsky—offers two novel contributions to this subject. First, generativism sheds light on an irrational double standard in the study of the human mind in contrast to the study of non-cognitive systems—"methodological dualism"—which, I argue, has been transferred to evaluations of AlphaGo and other AI. Second, by exposing this irrational double standard in perceptions of AI, I employ generativism's more well-known arguments concerning the nature of human intelligence and its scientific study to the evaluation of AI, exposing deficient interpretations widely used in the case of AlphaGo and AI generally. [ABSTRACT FROM AUTHOR]

Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease. [ABSTRACT FROM AUTHOR]

In his article in Science, Nicolas Gisin claimed that quantum correlations emerge from outside space–time. We explainthat they are due to space-time symmetries. This paper is a critical review of metaphysical conclusions found in many recent articles. It advocates the importance of contextuality, Einstein -causality and global symmetries. Bell tests allow only rejecting probabilistic coupling provided by a local hidden variable model, but they do not justify metaphysical speculations about quantum nonlocality and objects which know about each other's state, even when separated by large distances. The violation of Bell inequalities in physics and in cognitive science can be explained using the notion of Bohr- contextuality. If contextual variables, describing varying experimental contexts, are correctly incorporated into a probabilistic model, then the Bell–CHSH inequalities cannot be proven and nonlocal correlations may be explained in an intuitive way. We also elucidate the meaning of statistical independence assumption incorrectly called free choice, measurement independence or no- conspiracy. Since correlation does not imply causation, the violation of statistical independence should be called contextuality; it does not restrict the experimenter's freedom of choice. Therefore, contrary to what is believed, closing the freedom-of choice loophole does not close the contextuality loophole. [ABSTRACT FROM AUTHOR]

This paper presents a concept for a universal tram driver console that has been developed based on research results regarding the review of tram control panels. These efforts were carried out as part of the project "Innovative training system for tram drivers, based on a full-cab simulator with the application of cognitive science" POIR.01.01.01-00-0135/22, with funding from the Smart Growth Operational Programme. This project involves the development of a tram driver training system based on a full-cabin tram simulator mounted on a motion platform, integrated with eye-tracking technologies and skin conductance response analysis for tram drivers' assessment. The presented research results regarding the development of a universal control panel structure for a tram simulator have led to the creation of a panel based on interchangeable panels. The arrangement of individual switches was determined based on the identification, selection, critical evaluation, and analysis of data from current solutions. [ABSTRACT FROM AUTHOR]

In this paper we suggest that basic forms of musical entrainment may be considered as intrinsically creative, enabling further creative behaviors which may flourish at different levels and timescales. Rooted in an agent's capacity to form meaningful couplings with their sonic, social, and cultural environment, musical entrainment favors processes of adaptation and exploration, where innovative and functional aspects are cultivated via active, bodily experience. We explore these insights through a theoretical lens that integrates findings from enactive cognitive science and creative cognition research. We center our examination on the realms of groove experience and the communicative and emotional dimensions of music, aiming to present a novel preliminary perspective on musical entrainment, rooted in the fundamental concepts of meaning-making and creativity. To do so, we draw from a suite of approaches that place particular emphasis on the role of situated experience and review a range of recent empirical work on entrainment (in musical and non-musical settings), emphasizing the latter's biological and cognitive foundations. We conclude that musical entrainment may be regarded as a building block for different musical creativities that shape one's musical development, offering a concrete example for how this theory could be empirically tested in the future. [ABSTRACT FROM AUTHOR]

Cognitive communication research with Chinese characteristics is both the theoretical innovation and practical exploration in the interdisciplinary fields of communication and cognitive science. In recent years, under the context of interdisciplinary research, cognitive communication research in China is no longer limited to the discussion of its disciplinary boundary and its legitimacy. According to the former literature, researchers have discussed the three main topics of cognitive communication: the knowledge system of cognitive communication, the framework of cognitive neurocommunication and the applied research of cognitive communication. However, the discipline construction of cognitive communication in China is still in the stage of exploration. Both how to form an independent and original cognitive communication knowledge system and how to put the theoretical vision in the local context and respond to the social problems need to be explained and to be closely integrated with Chinese characteristics in the cognitive communication research. In order to systematically illustrate the Chinese characteristics of cognitive communication research, this paper focuses on four basic questions: what to construct, why to construct, how to construct and what to do. Then, this paper constructs a four-dimensional framework which includes the value system, the dynamic system, the knowledge production system and the application system. This paper (1) discusses the theoretical construction with Chinese characteristics in the value system. (2) integrates the background of disciplinary development and the trend of technological development into the dynamic system of cognitive communication research. (3) focuses on the theoretical innovation based on practice in the knowledge production system. (4) then explores the implement of cognitive communication with Chinese characteristics in overall strategic needs of the country. This study suggests that, under the construction of philosophy and social sciences with Chinese characteristics, cognitive communication with Chinese characteristics needs to ascertain the value orientation, characteristic and research objective. Under the impetus of practice power, discipline development power and tool power, cognitive communication continues to have Chinese characteristics. Cognitive communication with Chinese characteristics needs to be completed in terms of establishing the guiding position of Marxism, integrating multi-theory and solving important problems, which can reflect Chinese characteristics in academic research, theoretical construction and problem-orientation. Finally, in the insight of cognitive pattern, exploration of cognitive mechanism and innovation of cognitive tools, the research result of cognitive communication can be used in practical applications. Compared to previous research, this paper has made extensions in the following aspects: Firstly, this paper has constructed the interactive and complex four-dimensional framework for the study of cognitive communication with Chinese characteristics, which suggests the essential requirements of Chinese characteristics in cognitive communication. At the same time, it explores the approach to the local study of cognitive communication, hoping to establish a subject status in constructing original theories, to keep up with local practice and respond to real problems. Secondly, this paper, closely related to the practice of socialist construction with Chinese characteristics, explores the possible practical aspects of cognitive communication, aiming at exploring and solving real problems. The four-dimensional framework helps to explicit the essence, the development motivation, the implementation approach and the application of the study of cognitive communication with Chinese characteristics, which helps to form the Chinese style of cognitive communication, to expand the Chinese research team of cognitive communication, and to infuse Chinese style into the international academic exchange of cognitive communication, which makes the future cognitive communication research move towards the construction direction of "Chinese characteristics" and finally, the cognitive communication discipline with Chinese characteristics will be constructed. [ABSTRACT FROM AUTHOR]

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In this paper, we present an agent-based model for studying the impact of ‘myside bias’ on the argumentative dynamics in scientific communities. Recent insights in cognitive science suggest that scientific reasoning is influenced by ‘myside bias’. This bias manifests as a tendency to prioritize the search and generation of arguments that support one’s views rather than arguments that undermine them. Additionally, individuals tend to apply more critical scrutiny to opposing stances than to their own. Although myside bias may pull individual scientists away from the truth, its effects on communities of reasoners remain unclear. The aim of our model is two-fold: first, to study the argumentative dynamics generated by myside bias, and second, to explore which mechanisms may act as a mitigating factor against its pernicious effects. Our results indicate that biased communities are epistemically less successful than non-biased ones, and that they also tend to be less polarized than non-biased ones. Moreover, we find that two socio-epistemic mechanisms help communities to mitigate the effect of the bias: the presence of a common filter on weak arguments, which can be interpreted as shared beliefs, and an equal distribution of agents for each alternative at the start of the scientific debate. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) images are an important data source in microwave vision research; however, computer vision cannot interpret these images effectively based on optical perceptual principles. Therefore, microwave vision, which draws inspiration from human visual perception principles and combines computer vision techniques with electromagnetic physical principles, has become an important research direction in microwave remote sensing. Exploring the cognitive basis for microwave vision is crucial for improving the theoretical system of microwave vision. Therefore, as a preliminary attempt to enhance the theoretical understanding of microwave vision, this paper examines the effectiveness of optical perceptual principles for microwave vision. As a classical visual theory, Gestalt perceptual principles are commonly used for describing the perceptual principles of the human visual system for the external optical world and are a cognitive theoretical foundation of computer vision. In this context, this paper uses SAR images as the research object, focuses on the design process of cognitive psychology experiments, and preliminarily studies the effectiveness of Gestalt perceptual principles for SAR images, including the principles of perceptual grouping and perceptual invariance, exploring the cognitive basis of microwave vision. The experimental results indicate that the Gestalt perceptual principles cannot be directly applied to the algorithm design for SAR images, and the knowledge concepts and visual principles derived from the optical world using the human visual system do not perform well in SAR images. In the future, it will be necessary to summarize the corresponding visual cognitive principles based on the characteristics of microwave images, such as SAR images.

This article explores the potential use of dual-process theories of thought as architectures for developing neuro-symbolic AI models. It discusses the limitations of large language models (LLMs) in handling structured tasks that rely on symbolic reasoning and proposes integrating LLMs with symbolic systems to address these limitations. The article explores different models for the interaction between rapid, associative thinking (System 1) and deliberate, rule-based thinking (System 2), and suggests that these architectures could be useful in developing and testing symbolic/subsymbolic systems integration in AI. It emphasizes the need for further exploration of the interaction between the two systems in neuro-symbolic architectures, while also prioritizing minimizing errors and achieving optimal performance. The document contains a list of references and citations from various sources related to bias in artificial intelligence and decision-making processes, providing additional sources for further research. [Extracted from the article]

A humanoid robot is a particular form of embodied agent. The form that an agent takes has a major impact on how that agent interacts with its environment and how it develops an understanding of that environment through its interactions. In this paper, we explore the importance of humanoid embodiment and we argue that humanoids occupy a special niche in the spectrum of robot forms. In doing so, we highlight the implications for the way a humanoid robot can interact with its environment, including humans, for the manner in which humans interact with humanoid robots, and for a humanoid robot's capacity to develop cognitive abilities. We also consider the degree to which humanoid robots should approximate humans, addressing robot morphology, appearance, and movement. We emphasize the dual role of humanoid robots as engineering artifacts that can provide services for humans, and as platforms for scientific enquiry into the nature of human cognition. We conclude by highlighting some key research challenges for the discipline of humanoid robotics. [ABSTRACT FROM AUTHOR]

Combating misinformation is one of the urgent social crises. Much research has shown that disinformation can lead to social panic and adversely affect society. It is crucial to promptly detect and counteract misinformation to reduce its adverse effects. Although progress in text-based fact verification has been made, the community needs further exploration into the user-oriented results. To address this gap, we integrate theories from linguistics, journalism, psychology, and cognitive science. We propose a disinformation detection algorithm based on multidimensional content analysis. This algorithm combines human factors and user perception in text interactive media to establish six dimensions for comprehensive content analysis. We have proposed a quantitative calculation method corresponding to six dimensions to detect misinformation. The average accuracy of the proposed model test on four datasets is 95.28%. The results show that this algorithm can effectively analyze from a multidisciplinary theoretical perspective and effectively identify misinformation in Chinese and English. [ABSTRACT FROM AUTHOR]

The terms "extended cognition" and the "extended mind" identify two strands of philosophical argument that are commonly subsumed under the general heading of active externalism. The present paper describes an integrated approach to understanding extended cognition and the extended mind—one that papers over the differences between these two, ostensibly distinct, forms of cognitive extension. As an added bonus, the paper describes how active externalism might be applied to the realm of non-cognitive phenomena, thereby yielding an expansion in the theoretical and empirical scope of the active externalist enterprise. Both these points of progress stem from what is called the dispositional hypothesis. According to the dispositional hypothesis, extended cognition occurs when the mechanisms responsible for the manifestation of dispositional properties include components that lie beyond the borders of the thing to which the dispositional properties are ascribed. • A clearer explication of active externalist theses is required. • According to the dispositional hypothesis, cognitive extension occurs when the mechanisms responsible for the manifestation of dispositional properties extend beyond the borders of the entity to which dispositional properties are ascribed. • A consideration of dispositional properties provides the basis for a unified approach to understanding extended cognition and the extended mind. • Active externalism can be applied to phenomena that do not fall within the empirical orbit of cognitive science. [ABSTRACT FROM AUTHOR]

This paper examines the interplay between integrative explanatory pluralism and the quest for unified theories. We argue that when grounded in virtues associated with satisfactory explanations, integrative pluralism exhibits an inherent instability stemming from the conflict between the demand for unity and the commitment to preserving a patchwork of disparate partial explanations. A case study in cognitive science illuminates the challenges of maintaining both systematicity and depth in explanations within this framework. While this instability does not render integrative pluralism fundamentally flawed, it stresses the importance of a diachronic analysis of scientific dynamics and norms. The conclusion highlights the continued value of integrative pluralism in interdisciplinary research programs, while emphasizing its role as a temporary rather than permanent approach. [ABSTRACT FROM AUTHOR]

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle. [ABSTRACT FROM AUTHOR]

We offer a framework for the design and use of Ambient Smart Environments (ASEs) for preventive mental health care support. Drawing from Complex Systems Theory (CST) and 'E' Cognitive Science (ECS), we claim that ASEs have the potential to act in a preventive capacity in support of good mental health, i.e. supporting dynamics that avoid so-called "struck states" (which are, according to CST, thought generally to underpin forms of psychopathology). Here, we frame our discussion with what has recently been termed the "mind-technology problem". We define and characterise ASE systems, present some examples, and briefly survey some existing theoretical work. After introducing the essential CST terminology, the paper goes on to apply CST to explain developmental adaptation to continuously changing (smart) environments. Understanding the ASE's navigation in terms of a dynamic geometry between attracting and repelling points (or local minima/local maxima), allows us to develop neurotechnology that can augment clinical interventions by predicting upcoming shifts for good symptomatic outcomes, i.e. when a preventive intervention (i.e. destabilisation) should take place. We further offer clear directions for the development and design of such neurotechnology. [ABSTRACT FROM AUTHOR]

In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems. [ABSTRACT FROM AUTHOR]

Revealing how the mind represents information is a longstanding goal of cognitive science. However, there is currently no framework for reconstructing the broad range of mental representations that humans possess. Here, we ask participants to indicate what they perceive in images made of random visual features in a deep neural network. We then infer associations between the semantic features of their responses and the visual features of the images. This allows us to reconstruct the mental representations of multiple visual concepts, both those supplied by participants and other concepts extrapolated from the same semantic space. We validate these reconstructions in separate participants and further generalize our approach to predict behavior for new stimuli and in a new task. Finally, we reconstruct the mental representations of individual observers and of a neural network. This framework enables a large-scale investigation of conceptual representations. Revealing how the human mind represents information is a longstanding goal of cognitive science. Here, the authors develop a method to reconstruct the mental representations of multiple visual concepts using behavioral judgments. [ABSTRACT FROM AUTHOR]

This work marks a significant advancement in the field of cognitive science and gaming technology. It offers an in-depth analysis of the effects of various video game genres on brainwave patterns and concentration levels in virtual reality (VR) settings. The study is groundbreaking in its approach, employing electroencephalograms (EEGs) to explore the neural correlates of gaming, thus bridging the gap between technology, psychology, and neuroscience. This review enriches the dialogue on the potential of video games as a therapeutic tool in mental health. The study's findings illuminate the capacity of different game genres to elicit varied brainwave responses, paving the way for tailored video game therapies. This review contributes meaningfully to the state of the art by offering empirical insights into the interaction between gaming environments and brain activity, highlighting the potential applications in therapeutic settings, cognitive training, and educational tools. The findings are especially relevant for developing VR gaming content and therapeutic games, enhancing the understanding of cognitive processes, and aiding in mental healthcare strategies. [ABSTRACT FROM AUTHOR]

The article explores the potential impact of artificial-intelligence chatbots (AICs) on cognitive health. It discusses the interactive and personalized nature of AICs and how they can lead to a deeper cognitive reliance on the chatbots. The article introduces the concept of "AICs induced cognitive atrophy" (AICICA), which refers to the potential deterioration of cognitive abilities resulting from an overreliance on AICs. It emphasizes the need for further research to assess the prevalence and patterns of AICICA, explore long-term cognitive effects, and develop interventions to mitigate AICICA. The article also acknowledges the positive contributions of AICs as external cognitive tools but highlights the importance of a balanced approach and critical thinking skills to avoid potential negative effects. [Extracted from the article]

In service of the goal of examining how cognitive science can facilitate human–computer interactions in complex systems, we explore how cognitive psychology research might help educators better utilize artificial intelligence and AI supported tools as facilitatory to learning, rather than see these emerging technologies as a threat. We also aim to provide historical perspective, both on how automation and technology has generated unnecessary apprehension over time, and how generative AI technologies such as ChatGPT are a product of the discipline of cognitive science. We introduce a model for how higher education instruction can adapt to the age of AI by fully capitalizing on the role that metacognition knowledge and skills play in determining learning effectiveness. Finally, we urge educators to consider how AI can be seen as a critical collaborator to be utilized in our efforts to educate around the critical workforce skills of effective communication and collaboration. [ABSTRACT FROM AUTHOR]

The question of aesthetics in film-theoretical discourse today is split between, on the one hand, a film-phenomenological or "filmosophical" approach that values the putatively immanent relation between film and the mind and, on the other, the naturalizing epistemology of post-theory, which reduces the question of film aesthetics to one of poetics. What unites these otherwise disparate projects is the consideration of aesthetics divorced from the question of politics; in both cases, the social or political significance of the film–spectator relationship has been summarily purged. In this article, I will offer an alternative account of film aesthetics that draws on Jacques Rancière's theory concerning the mutually determining relationship between aesthetics and politics. In particular, I will consider the relevance of Rancière's thesis concerning what he calls the distribution of sensible to current accounts, as well as taking up his novel consideration of aesthetic distance and the "emancipated" spectator. With respect to film phenomenology, I will examine how its film-theoretical program rests on the flawed concept of a de-politicized spectator enchained by the film image. With respect to post-theory, I will examine how its appropriation from cognitive science of the rational agent model of meaning making inappropriately limits the political potential of film aesthetics. [ABSTRACT FROM AUTHOR]

The relationship between philosophy and science has always been complementary. Today, while science moves increasingly fast and philosophy shows some problems in catching up with it, it is not always possible to ignore such relationships, especially in some disciplines such as philosophy of mind, cognitive science, and neuroscience. However, the methodological procedures used to analyze these data are based on principles and assumptions that require a profound dialogue between philosophy and science. Following these ideas, this work aims to raise the problems that a classical connectionist theory can cause and problematize them in a cognitive framework, considering both philosophy and cognitive sciences but also the disciplines that are near to them, such as AI, computer sciences, and linguistics. For this reason, we embarked on an analysis of both the computational and theoretical problems that connectionism currently has. The second aim of this work is to advocate for collaboration between neuroscience and philosophy of mind because the promotion of deeper multidisciplinarity seems necessary in order to solve connectionism's problems. In fact, we believe that the problems that we detected can be solved by a thorough investigation at both a theoretical and an empirical level, and they do not represent an impasse but rather a starting point from which connectionism should learn and be updated while keeping its original and profoundly convincing core. [ABSTRACT FROM AUTHOR]

The metaphor of being touched by music is widespread and almost universal. The tactile experience, moreover, has received growing interest in recent years. There is, however, a need to go beyond a mere metaphorical use of the term, by positioning the tactile experience within the broader frame of embodied cognition and the experiential turn in cognitive science. This article explores the possible contribution of a science of touch by defining music as a vibrational phenomenon that affects the body and the senses. It takes as a starting point the clinical findings on the psychological and physiological value of tender touch with a special focus on the method of kangaroo mother care, which is a method for holding the baby against the chest of the mother, skin-to-skin. It is seen as one of the most basic affiliative bondings with stimuli that elicit reward. Via an extensive review of the research literature, it is questioned as to what extent this rationale can be translated to the realm of music. There are, in fact, many analogies, but a comprehensive theoretical framework is still lacking. This article aims at providing at least some preparatory groundwork to fuel more theorizing about listening and its relation to the sense of touch. [ABSTRACT FROM AUTHOR]

Semantic folding theory (SFT) is an emerging cognitive science theory that aims to explain how the human brain processes and organizes semantic information. The distribution of text into semantic grids is key to SFT. We propose a sentence-level semantic division baseline with 100 grids (SSDB-100), the only dataset we are currently aware of that performs a relevant validation of the sentence-level SFT algorithm, to evaluate the validity of text distribution in semantic grids and divide it using classical division algorithms on SSDB-100. In this article, we describe the construction of SSDB-100. First, a semantic division questionnaire with broad coverage was generated by limiting the uncertainty range of the topics and corpus. Subsequently, through an expert survey, 11 human experts provided feedback. Finally, we analyzed and processed the feedback; the average consistency index for the used feedback was 0.856 after eliminating the invalid feedback. SSDB-100 has 100 semantic grids with clear distinctions between the grids, allowing the dataset to be extended using semantic methods. [ABSTRACT FROM AUTHOR]

As the pace of life becomes faster and faster, people with weaker mental capacity are prone to a range of negative emotions in certain environments, such as mania and anxiety, and these negative emotions can interfere with normal learning and life, and even affect physical and mental health. Therefore, it is more and more important to accurately analyze people's emotional state. In this paper, based on the bimodal signals of EEG and EMG, we combine the broad learning network and D-S evidence theory to do an emotion recognition analysis of bioelectrical signals evoked by visual stimuli. Firstly, a suitable experimental paradigm is designed to acquire EEG signals and EMG signals through emotion evocation, followed by pre-processing, decomposing the signals using a wavelet decomposition algorithm, extracting signal components significantly affected by emotional states, and calculating the corresponding differential entropy as well as power spectrum features; after that, EMG features in time and frequency domains are extracted, and different features are fused and analyzed; BLS is used for classification, and finally, the BLS classification The results are fused with the introduced D-S evidence theory at the decision level. The method can recognize emotions effectively with an accuracy of 85.2% in triple classification scenarios. The proposed emotion recognition model is expected to provide a new technical tool for emotional cognitive science research and emotional brain-computer interface systems. [ABSTRACT FROM AUTHOR]

Proust was undoubtedly a pioneer in exploring cognitive processes engaged in memory. The analysis of the episode of the madeleine, as well as the study of Proust's statements on the goals of his work, clearly reveal the visionary side of this author. Long before several concepts entered into mainstream scientific thought, Proust proposed, among other things, that recall was a reconstruction, that a sensory cue could provoke a memory recall, and that we should distinguish between voluntary and involuntary memory. Through numerous episodes of "involuntary reminiscence" scattered throughout his work, Proust illustrates a particular form of autobiographic memory recall: a recall that does not involve consciousness and whose starting point is an emotion provoked by a specific cue. This recall, which leads, according to Proust, to a more intense revival of the memory than voluntary recall, has only reached prominence in cognitive science more than 80 years later. Additionaly, Proust underlined the determinant role that emotion may have in this particular form of recall. On the other hand, studies on animals have shown that the presentation of a retrieval cue could induce emotional reactions followed by a facilitation of the memory retrieval associated with the cue. The existence of these data, which support Proust's proposals, should encourage the neuroscience community to further explore, in humans and animals, this form of cue elicited emotion that initiated involuntary recall of autobiographical memory. [ABSTRACT FROM AUTHOR]

For semantic segmentation of cardiac magnetic resonance image (MRI) with low recognition and high background noise, a fusion‐attention Swin Transformer is proposed based on cognitive science and deep learning methods. It has a U‐shaped symmetric encoding–decoding structure with an attention‐based skip connection. The encoder realizes self‐attention for deep feature representation and the decoder up‐samples global features to the corresponding input resolution for pixel‐level segmentation. By introducing a skip connection between the encoder and decoder based on fusion attention, the remote interaction of global information is realized, and the attention to local features and specific channels is enhanced. A public ACDC cardiac MRI image dataset is used for experiments. The segmentation of the left ventricle, right ventricle, and myocardial layer is realized. The method performs well on a small sample dataset, for example, the pixel accuracy obtained by the proposed model is 93.68%, the Dice coefficient is 92.28%, and HD coefficient is 11.18. Compared with the state‐of‐the‐art models, the segmentation precision has been significantly improved, especially for the low recognition and heavily occluded targets. [ABSTRACT FROM AUTHOR]

Philosophy of memory is a hot topic in the cognitive sciences and philosophy of mind. This work examines the dispute between simulations and causal theories of memory by means of an examination of the feeling of familiarity and its relation to memory traces, more specifically how they increase the fluency of reconstruction of past episodes. Understanding the relationship between familiarity and memory traces and, furthermore, the relation between a fully-fledged phenomenology of memory and a sense of subjective certainty of the episode has occurred in the past, lead to a different interpretation of the rivalry between the CTM and the simulationist account. [ABSTRACT FROM AUTHOR]

Despite growing interest in spaced retrieval methods, a research gap has been discovered, with insufficient data to support best practises in year-one science. After analysing five systematic action cycles, four interrelated themes emerged: dual coding, cognitive load, peer communication, and feedback-driven metacognition. While literature emphasises the need to take a child's cognitive load into account, the themes of peer communication and dual coding provoked debate among participants. Despite this conflict, an agreement was reached on how limited reading abilities affect retrieval practise. While this small-scale study cannot draw definitive conclusions, I discovered that by optimising cognitive load, teachers can ensure that a task is desirably difficult using dual coding, discussion, and feedback-driven metacognition, all of which contributed to the success of knowledge acquisition methodologies. According to my research, ensuring a retrieval task is challenging, increases children's development of retention of scientific knowledge. However, minimising literacy constraints is critical to avoid thinking being exhausted by writing or reading, rather than recalling and applying. Consequently, the relevance of the study for education and society is illustrated by the findings, which emphasise the necessity to reduce extraneous load (reading and writing) during retrieval tasks and propose potential solutions to this issue. [ABSTRACT FROM AUTHOR]

Advocates and researchers have made many recommendations for forensic science improvement in the United States. These proposals are often motivated by wrongful convictions related to false or misleading forensic evidence. In many cases, the connection between the proposals and the actual experience of wrongful convictions has not been well defined. Further, recommendations may not have been realizable given the structure of the criminal justice system in the United States and the practical realities of forensic science laboratories. Finally, limited attempts have been made to assess recommendations over time to determine the progress of forensic science improvement and elucidate continuing gaps. Reports from the Department of Justice, the National Academy of Sciences, and the President's Council of Advisors on Science and Technology are assessed to determine the extent to which their recommendations have been implemented, whether the recommendations align with the actual experience of wrongful convictions, and how the American forensic science community has implemented forensic science improvement. The most successful proposals reflect a broad movement toward quality assurance, improved standards, and organizational improvement in the forensic sciences. Less successful proposals are associated with calls for large federal investments, difficulties in community-wide implementation, or uncertain linkage to foundations in science and practice. Significant progress has been made in the standardization of reporting and testimony, assessment of the foundational reliability of the disciplines, and DNA mixture interpretation. Significant gaps remain to improve medicolegal death investigation, governance, and the implementation of standards. Improved allocation and use of resources will be required to meet continuing challenges in capacity building, training, and proficiency testing, although past experience indicates that both federal and non-federal funding will be required to address these issues. Continued improvement is needed to address the issues associated with wrongful convictions, although forensic science leaders have demonstrated the ability to prioritize improvement initiatives. [ABSTRACT FROM AUTHOR]

Cognitive science has demonstrated that multilinguals (including children) show a cognitive advantage over monolinguals. Linguistics has provided evidence that multilinguals (including children) are able to successfully separate their lexicons and grammars and negotiate multilingual environments. Apart from these achievements, linguistics has generally failed to demonstrate a multilingual advantage related to the multilingual's linguistic proficiency. The present article summarizes the current literature, which shows that there are first indications of an acceleration effect in multilingual children. This effect is discernable if the languages radically differ, if the child uses a 'weak' language (often a minority language), if the child acquires more than two languages from birth, and if contact with the language exhibiting the acceleration effect is delayed until kindergarten age. This kind of acceleration effect represents an explanation gap under current theorizing in cognitive science and linguistics, and calls for a new language acquisition theory, a best-of-breed solution for further research in language acquisition. AAiMLL (Acquisition Advantages in MultiLingual Learners) combines cognitive and linguistic aspects with a threshold theory. It is claimed here that the multilingual child learns from two cognitive acquisition strategies. One is fed by grammatical features, and requires the child to (re-)use already acquired knowledge by generalizing to new domains. The other strategy enables the child to consider rejected alternatives of earlier decisions in one language, for use in the other language. An acceleration effect related to the multilingual's linguistic proficiency is indicative of the success of both strategies. The success of the strategies is argued to be related to a threshold of language usage from a quantitative or a qualitative perspective. [ABSTRACT FROM AUTHOR]

Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease.