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In this article, with the help of Heidegger and Foucault's conceptual tools, as well as the achievements of the cognitive science, we try to examine the transition from paper reading to digital reading. This discussion can be a preparation for a philosophical understanding of the digital age. We show that Heidegger's analysis of the ready-to-hand beings and technology can help to understand the reading behaviors of paper and digital readers. We have also use Heidegger's analytical framework for text ontology. On the other hand, Foucault's reflections on the embodied technologies can explain the power-knowledge relationships that is constructed around the reader. In addition, the cognitive science has provided detailed empirical findings on the effects of reading behaviors on the human cognitive biases and skills by examining the various modes of reading. Embodied and skillful cognitive science, by going beyond the computational understanding of human beings, has noticed the various behaviors and states of the body in reading processes and highlighted their effects. To integrate these areas of research, we try to place the findings of the cognitive science within the framework of Heidegger and Foucault's technological understanding of the modern power-knowledge relationships. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe's AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe's approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption. [ABSTRACT FROM AUTHOR]

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]

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]

This paper proposes epistemic environmentalism as a novel framework for accounting for the contribution of the environment – broadly construed – to epistemic standings and which can be used to improve or protect epistemic environments. The contribution of the environment to epistemic standings is explained through recent developments in epistemology and cognitive science, including embodied cognition, embedded cognition, extended cognition and distributed cognition. The paper examines how these developments support epistemic environmentalism, as well as contributes theoretical resources to make epistemic assessments of dynamic environments. The epistemic environmentalist procedure from the assessment of an individual environment to changes made to that environment based on promoting the attainment of epistemic goods is also discussed. [ABSTRACT FROM AUTHOR]

Since 2000, there has been a movement toward bringing the micro-level variables of ideas and agency back into the disciplines of political science. Under these various ideational shifts, the agent-based ideas at the micro-level are conceptualized as terms of cognition or as cognitive information processes in social or cognitive psychology that are to be used to make inferences of structural or macro-level explanations on changes of policy-making or institutional transformation. This trend emphasizes that analyses of ideational factors should be unpacked as independent variables which can be operationalized and verified to have causation. Therefore, this paper suggests that studies of ideational variables should follow three lines of operationalization: the clarification of levels of analysis, the concretization of causal mechanisms, and the verification of practical inferences synthesizes the practices and the limitations of this trend toward ideas. It argues the the ideational turn facilitates the exploration of the structure-agency dilemma with theoretical models, middle-range causal mechanisms, and practical verifications in political science. However, the valid inferences on various mechanisms at different levels need further investigations and evidence-based verifications. [ABSTRACT FROM AUTHOR]

While research evidence has the potential to improve classroom practice, research–practice gaps continue to persist not least owing to the limited relevance of research findings for practice. A common approach in healthcare to address the research–practice gap is to form research priority setting partnerships (PSPs) in which stakeholders identify questions they would like research to answer. This paper presents the results from such a PSP in education with a focus on cognitive science research, which has received increased attention in the past few years owing to its potential to explain memory and learning processes and inform classroom practice. Over 400 questions from teachers were collected using an online survey. The final 15 research priorities highlight the need for research on a wider range of subjects, settings and phases as well as research designs that take the complexity of classrooms into consideration and aim to answer how different teaching strategies interact with each other as well as student motivation and agency at the micro‐ and macro‐level. The role of teacher expertise vs. fidelity to original research designs should also be investigated further. Overall, this paper highlights the importance of taking teacher voice into account to ensure that new research in the field is both academically rigorous and practically relevant. [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]

Abstract concepts are relevant to a wide range of disciplines, including cognitive science, linguistics, psychology, cognitive, social, and affective neuroscience, and philosophy. This consensus paper synthesizes the work and views of researchers in the field, discussing current perspectives on theoretical and methodological issues, and recommendations for future research. In this paper, we urge researchers to go beyond the traditional abstract-concrete dichotomy and consider the multiple dimensions that characterize concepts (e.g., sensorimotor experience, social interaction, conceptual metaphor), as well as the mediating influence of linguistic and cultural context on conceptual representations. We also promote the use of interactive methods to investigate both the comprehension and production of abstract concepts, while also focusing on individual differences in conceptual representations. Overall, we argue that abstract concepts should be studied in a more nuanced way that takes into account their complexity and diversity, which should permit us a fuller, more holistic understanding of abstract cognition. [ABSTRACT FROM AUTHOR]

Despite tremendous recent progress, extant artificial intelligence (AI) still falls short of matching human learning in effectiveness and efficiency. One fundamental disparity is that humans possess a wealth of prior knowledge, while AI lacks the essential commonsense knowledge required for learning tasks. Guided by schema theory, we employ the design science research methodology to introduce a novel knowledge-aware learning framework to harness the knowledge-based processes in human learning. Unlike existing pre-trained large language models (LLMs) and knowledge-aware approaches that treat knowledge in considerably different ways from humans, our theoretically grounded framework closely mimics how humans acquire, represent, activate, and utilize knowledge. The extensive evaluations in the context of text analytics tasks demonstrate that our design achieves comparable performance to the state-of-the-art LLMs and enhances model generalizability and learning efficiency. This study takes a step forward by bringing cognitive science into building cognitively plausible AI and human-AI collaboration research. [ABSTRACT FROM AUTHOR]

Cognitive science-informed approaches have gained considerable influence in education in the UK and internationally, but not much is known about how teachers perceive cognitive science-informed strategies or enact them within the contexts of their everyday classrooms. In this paper, we discuss the perceptions and experiences of cognitive science-informed strategies of 13 teachers in England. The paper critically explores how the teachers understood and used cognitive science-informed strategies in their teaching, their views of the benefits and challenges for different subjects and groups of learners, and their reflections on supporting factors and barriers for adopting the strategies in their schools. The teachers' accounts illustrate some of the many complexities of adopting cognitive science-informed approaches in real-life educational settings. Drawing on their narratives, the paper emphasises the importance of acknowledging different contextual dimensions and the dynamic interactions between them to understand when and how teachers enact cognitive science-informed approaches in their classrooms. [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]

Historically, the dispute between empiricists and nativists in philosophy and cognitive science has concerned human and animal minds (Margolis and Laurence in Philos Stud: An Int J Philos Anal Tradit 165(2): 693-718, 2013, Ritchie in Synthese 199(Suppl 1): 159–176, 2021, Colombo in Synthese 195: 4817–4838, 2018). But recent progress has highlighted how empiricist and nativist concerns arise in the construction of artificial systems (Buckner in From deep learning to rational machines: What the history of philosophy can teach us about the future of artificial intelligence. Oxford University Press.). This paper uses nativism and empiricism to address questions about the nature of artificial intelligence and its trajectory. It begins by defining the nativism/empiricism debate in terms of the generality of a system. Nativist systems have initial states with domain-specific features; empiricist systems have initial states with only domain-general features. With the debate framed in this way, it then explores a variety of arguments for nativism and empiricism in AI. These arguments revolve around two different questions which must be distinguished: whether nativism the only possible approach to developing human-level AI (HLAI); and whether nativism is the most practical approach to developing HLAI. On the first question, it argues that nativism is quite clearly not the only possible approach to developing HLAI, as is sometimes suggested. It argues that existing arguments for the necessity of nativism are unconvincing, because they analogize from poverty of the stimulus arguments about humans, while AIs often have access to much more data than humans. Then it argues that the case of evolution gives us a compelling argument against nativism. On the second, practical question, the paper argues that there is a tradeoff between the advantages of encoding innate machinery directly, and the advantages of evolving or learning it. However, as the past decade has shown, empiricism is a much more viable path to greater capability levels, given the 'bitter lesson' (Sutton in Reinforcement Learning: An Introduction. MIT press.) that encoding the 'correct' knowledge in AI systems is perennially outperformed by more empiricist methods that leverage large-scale data and computation. [ABSTRACT FROM AUTHOR]

To introduce our special issue How Minds Work: The Collective in the Individual, we propose "radical CI," a form of collective intelligence, as a new paradigm for cognitive science. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. To explain cognitive performance, it appeals to the distribution of cognitive labor on the assumption that the human project runs on countless interactions between locally acting individuals with specialized skills that each retain a small part of the relevant information. Some of the papers in the special issue appeal to radical CI to account for a variety of cognitive phenomena including memory performance, metacognition, belief updating, reasoning, and problem‐solving. Other papers focus on the cultural and institutional practices that make radical CI possible. The notion that cognition is collective has a long pedigree but is enjoying considerable attention in the cognitive sciences as multiple fields of study deliver evidence for the central role of community and culture in cognition. To introduce the special issue of topiCS surveying this groundswell, we propose the concept of "radical collective intelligence," the view that some of the key representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. This concept clarifies how the volume's contributions either rethink long‐studied cognitive processes (memory, metacognition, reasoning) or contemplate how radical CI can arise. [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]

Caregivers, i.e., parents, grandparents, and anyone caring for children, make up the majority of users in many urban centers around the world, including in China. Yet urban design and planning rarely consider specific needs of caregivers. This can result in a negative perception among people that cities are unfriendly places to raise children. Such negative perception is among the major contributors to the aging population problem, as parents and parents-to-be feel reluctant to give birth and raise more than one or even any children. Using an interdisciplinary approach, i.e., cognitive science, psychology, behavioral economics, and urban studies, this paper examines a new city model of "parent-friendly city," where urban design serves the needs of caregivers, effectively creating a perception that cities are friendly places for raising children. First, we identify that caregivers' needs are characterized by two major factors: (1) provision: parents want to provide for their children, such as providing places for play and learning, and (2) restoration: parents want to restore things that they lack due to childcare responsibilities, such as lack of rest and interaction with other adults. Second, we offer cognitive-based design principles that can work powerfully and effectively to fill both needs. Third, using real case studies from cities in China and around the world, we illustrate how these design principles work, even reversing caregivers' perception from negative to positive. Ultimately, thoughtful urban design can support parents in raising children, making cities into partners in parenthood. [ABSTRACT FROM AUTHOR]

Dennett (Synthese,53(2), 159–180, 1982, 1991, Journal of Consciousness Studies,10(9–10), 19–30, 2003, Phenomenology and the Cognitive Sciences,6, 247–270, 2007) proposes and defends a method called heterophenomenology. Heterophenomenology is a method to study consciousness from a third-person objective point of view as opposed to a first-person subjective point of view or (auto)-phenomenology. The method of heterophenomenology serves a necessary role in Dennett's schema of bridging the gap between the manifest and the scientific image of the world. In this paper, I attempt to present a limited critique of the method of heterophenomenology. The objection raised in this paper is limited to one of the steps involved in the method, i.e., the interpretation of the heterophenomenological text as analogous to novelist fiction. I attempt to show that the assumptions made by Dennett about the interpretation of fiction are contradictory in nature and therefore the same cannot be applied to the interpretation of a heterophenomenological text. The assumptions fail in justifying the interpretation of fiction itself and hence exporting them by analogy to interpret a heterophenomenological text is a mistake. [ABSTRACT FROM AUTHOR]

It should seem obvious that any purportedly comprehensive account of human cognition should be able to coherently accommodate itself—qua an instance of human cognition—where that means accommodating not just the specific tenets that distinguish it from competing accounts, but also the fundamental presuppositions that constitute the framework within which it has been developed and argued for. That seemingly obvious requirement of self-accommodation becomes problematic, I argue, when the cognitive scientist is committed, as most contemporary cognitive scientists are, to a broadly naturalist-physicalist perspective, or framework, and at the same time is moved by empirical findings and theoretical considerations to recognize our active and ineliminable contribution, not only to the sense the world makes to us cognitively, but already to the sense it makes to us at the level of (‘pre-objective’) perception. For the sake of clarity of exposition, this paper presses that difficulty of contemporary cognitive science by looking closely at how it manifests itself in Andy Clark’s Surfing Uncertainty (Clark Surfing uncertainty, Oxford University Press, 2016); but the difficulty is principled and general. To avoid it, without denying the active role we play in the constitution of the world as pre-objectively perceived and as cognitively, objectively represented, contemporary cognitive scientists would need, at the very least, to acknowledge that their commitment to the naturalist-physicalist framework may not itself be justified from within that framework. Having taken that step, they might as well take another, and recognize that a truly satisfying understanding of human perception and cognition can only be attained from a perspective that, though fully attentive to empirical findings, transcends the naturalist-physicalist framework and affords a critical examination of it. [ABSTRACT FROM AUTHOR]

Marek Pokropski's "Mechanisms and Consciousness Integrating Phenomenology with Cognitive Science" reopens the question about the possibility of naturalization of phenomenology. The author adopts the position of nonreductive mechanistic integration, which, he claims, has offered new methodological perspectives for studying complex mental phenomena such as consciousness. In this paper, I outline the context of the discussions on the relationship between phenomenology and cognitive sciences, and (§2) discuss the approach proposed by Pokropski, pointing out the main arguments and the most important research achievements. Then I focus on some difficulties of this position. The first of these I associate with a procedure which I call a dismemberment of phenomenology (§3). The second (§4) concerns the concept of constraint, which is key to Pokropski's analyzes, and some consequences of its application and alternative reading. Finally, in §5 I draw attention to two difficulties. The first is whether it is possible to read phenomenology in terms of functional analysis; the second is related to the doubt whether psychological or phenomenological functional explanations can be treated as mechanistic (which is a strong premise of Pokropski's argument). In Conclusion I make an overall assessment of the presented approach. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the advantages of combining the detailed military history of actual battles with contemporary decision-making cognitive theories. Specifically, we analyse two battle decisions made in Israeli-Arab wars, and interpret, through a cognitive lens, some of the decisions that were subsequently deemed "incomprehensible" in the civilian and military literature. This perspective permits analysis of multiple processes – such as risk evaluation, mental completion of unknown information, and estimation of enemy response. Moreover, it predicts that even highly experienced commanders are prone to the effect of cognitive biases, and may therefore make bad decisions with disastrous consequences. [ABSTRACT FROM AUTHOR]

This paper studies a new problem, active learning with partial labels (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process. To address ALPL, we first build an intuitive baseline that can be seamlessly incorporated into existing AL frameworks. Though effective, this baseline is still susceptible to the overfitting, and falls short of the representative partial-label-based samples during the query process. Drawing inspiration from human inference in cognitive science, where accurate inferences can be explicitly derived from counter-examples (CEs), our objective is to leverage this human-like learning pattern to tackle the overfitting while enhancing the process of selecting representative samples in ALPL. Specifically, we construct CEs by reversing the partial labels for each instance, and then we propose a simple but effective WorseNet to directly learn from this complementary pattern. By leveraging the distribution gap between WorseNet and the predictor, this adversarial evaluation manner could enhance both the performance of the predictor itself and the sample selection process, allowing the predictor to capture more accurate patterns in the data. Experimental results on five real-world datasets and four benchmark datasets show that our proposed method achieves comprehensive improvements over ten representative AL frameworks, highlighting the superiority of WorseNet. [ABSTRACT FROM AUTHOR]

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]

In this paper, I argue that the fields of artificial intelligence (AI) and education have been deeply intertwined since the early days of AI. Specifically, I show that many of the early pioneers of AI were cognitive scientists who also made pioneering and impactful contributions to the field of education. These researchers saw AI as a tool for thinking about human learning and used their understanding of how people learn to further AI. Furthermore, I trace two distinct approaches to thinking about cognition and learning that pervade the early histories of AI and education. Despite their differences, researchers from both strands were united in their quest to simultaneously understand and improve human and machine cognition. Today, this perspective is neither prevalent in AI nor the learning sciences. I conclude with some thoughts on how the artificial intelligence in education and learning sciences communities might reinvigorate this lost perspective. [ABSTRACT FROM AUTHOR]

How to account for the power that art holds over us? Why do artworks touch us deeply, consoling, transforming or invigorating us in the process? In this paper, we argue that an answer to this question might emerge from a fecund framework in cognitive science known as predictive processing (a.k.a. active inference). We unpack how this approach connects sense-making and aesthetic experiences through the idea of an 'epistemic arc', consisting of three parts (curiosity, epistemic action and aha experiences), which we cast as aspects of active inference. We then show how epistemic arcs are built and sustained by artworks to provide us with those satisfying experiences that we tend to call 'aesthetic'. Next, we defuse two key objections to this approach; namely, that it places undue emphasis on the cognitive component of our aesthetic encounters—at the expense of affective aspects—and on closure and uncertainty minimization (order)—at the expense of openness and lingering uncertainty (change). We show that the approach offers crucial resources to account for the open-ended, free and playful behaviour inherent in aesthetic experiences. The upshot is a promising but deflationary approach, both philosophically informed and psychologically sound, that opens new empirical avenues for understanding our aesthetic encounters. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'. [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]

Children's literature adaptation research is an interface study between children's literature study and adaptation studies. It has the common characteristics of two relatively marginalized research fields, and also shows distinct interdisciplinary attributes due to its interface nature. In the current situation where literary research is constantly seeking innovation and going in-depth, the interdisciplinarity and corresponding interdisciplinary practices of children's literature adaptation research reflect its cutting-edge research directions, while also mapping out some development trends of literary research as a whole. This paper starts from the theoretical discussion of children's literature adaptation studies, reflects on its conjunction points of education studies (with a special focus), narrative studies, cognitive studies, and digital humanities to scrutinize its interdisciplinary theoretical resources, and attempts to explore how the underlying attributes of children's literature adaptation research combine with interdisciplinary theoretical resources to promote its own development. [ABSTRACT FROM AUTHOR]

Tokenization significantly influences language models (LMs)' performance. This paper traces the evolution of tokenizers from word-level to subword-level, analyzing how they balance tokens and types to enhance model adaptability while controlling complexity. Despite subword tokenizers like Byte Pair Encoding (BPE) overcoming many word tokenizer limitations, they encounter difficulties in handling non-Latin languages and depend heavily on extensive training data and computational resources to grasp the nuances of multiword expressions (MWEs). This article argues that tokenizers, more than mere technical tools, should drawing inspiration from the cognitive science about human language processing. This study then introduces the "Principle of Least Effort" from cognitive science, that humans naturally seek to reduce cognitive effort, and discusses the benefits of this principle for tokenizer development. Based on this principle, the paper proposes that the Less-is-Better (LiB) model could be a new approach for LLM tokenizer. The LiB model can autonomously learn an integrated vocabulary consisting of subwords, words, and MWEs, which effectively reduces both the numbers of tokens and types. Comparative evaluations show that the LiB tokenizer outperforms existing word and BPE tokenizers, presenting an innovative method for tokenizer development, and hinting at the possibility of future cognitive science-based tokenizers being more efficient. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University (Social Sciences) is the property of Journal of Xi'an Jiaotong University (Social Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Neophilologica is the property of Acta Neophilologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the digital age, the relationship between human memory and media is intimate and impossible to separate. This understanding serves as the foundation for this study of mediated memory, which includes an investigation into how memory is stored, represented, and communicated via different forms of media. Despite its significance, there has yet to be a comprehensive review leading to a complete understanding of mediated memory research, particularly from the 1950s to the 2010s. This paper aims to address this knowledge gap through an extensive literature review. It examines research spanning multiple decades, focusing on the methods and paradigms used in previous mediated memory studies. The review uncovers a dual-sided representation of memory. Neurological-cognitive science research suggests that memory is represented through technology, particularly in neuroimaging techniques. Conversely, humanities and social science research highlight memory's cultural representation through digital media, such as television, computers, smartphones, the internet, and social media. The review reveals a dichotomy in mediated memory research, with a risk of oversimplification due to the distinct paradigms. However, since the late 2000s, an interdisciplinary approach has gained momentum, leading to a more integrated perspective on mediated memory. This paper offers a comprehensive review of mediated memory studies from the 1950s to the 2010s, providing historical context, key theories, methodologies, and debates. It also identifies three distinct trajectories in these studies and highlights the gaps and issues that exist in the current research. These findings carry profound implications for future mediated memory research, advocating for a comprehensive, interdisciplinary approach to advance our understanding of how memory is shaped and represented in the digital age. [ABSTRACT FROM AUTHOR]

The Free Energy Principle (FEP) and Integrated Information Theory (IIT) are two ambitious theoretical approaches. The first aims to make a formal framework for describing self-organizing and life-like systems in general, and the second attempts a mathematical theory of conscious experience based on the intrinsic properties of a system. They are each concerned with complementary aspects of the properties of systems, one with life and behavior, the other with meaning and experience, so combining them has potential for scientific value. In this paper, we take a first step towards such a synthesis by expanding on the results of an earlier published evolutionary simulation study, which show a relationship between IIT-measures and fitness in differing complexities of tasks. We relate a basic information theoretic measure from the FEP, surprisal, to this result, finding that the surprisal of simulated agents' observations is inversely related to the general increase in fitness and integration over evolutionary time. Moreover, surprisal fluctuates together with IIT-based consciousness measures in within-trial time. This suggests that the consciousness measures used in IIT indirectly depend on the relation between the agent and the external world, and that it should therefore be possible to relate them to the theoretical concepts used in the FEP. Lastly, we suggest a future approach for investigating this relationship empirically. Author summary: Two influential theoretical frameworks in cognitive science, neuroscience and computational biology, are the Free Energy Principle and Integrated Information Theory. The first is a formal approach to self-organization and adaptive behavior ‐ in short, life ‐ based on first principles from statistical physics. The second is an attempt at formally describing the intrinsic experience of a given system, that is, how it feels to be that system. In this way, these two theories provide tools for understanding two complementary aspects of a given organism; namely, how it acts in a goal-directed manner based on statistical beliefs about the world, and how it feels to be that system in that process. In this paper, we provide an initial numerical investigation of the potential relation of these theoretical frameworks. We simulate agents that undergo evolution, and show that as their level of integration (Φ, a measure from Integrated Information Theory) increases, information theoretic surprisal (a quantity used in the Free Energy Principle) decreases. We also see that Φ and surprisal fluctuate together, and that these fluctuations depend on sensory input. Finally we provide considerations for future simulation work, and how to bring these two theoretical frameworks closer together. [ABSTRACT FROM AUTHOR]

In the digital age, the relationship between human memory and media is intimate and impossible to separate. This understanding serves as the foundation for this study of mediated memory, which includes an investigation into how memory is stored, represented, and communicated via different forms of media. Despite its significance, there has yet to be a comprehensive review leading to a complete understanding of mediated memory research, particularly from the 1950s to the 2010s. This paper aims to address this knowledge gap through an extensive literature review. It examines research spanning multiple decades, focusing on the methods and paradigms used in previous mediated memory studies. The review uncovers a dual-sided representation of memory. Neurological-cognitive science research suggests that memory is represented through technology, particularly in neuroimaging techniques. Conversely, humanities and social science research highlight memory's cultural representation through digital media, such as television, computers, smartphones, the internet, and social media. The review reveals a dichotomy in mediated memory research, with a risk of oversimplification due to the distinct paradigms. However, since the late 2000s, an interdisciplinary approach has gained momentum, leading to a more integrated perspective on mediated memory. This paper offers a comprehensive review of mediated memory studies from the 1950s to the 2010s, providing historical context, key theories, methodologies, and debates. It also identifies three distinct trajectories in these studies and highlights the gaps and issues that exist in the current research. These findings carry profound implications for future mediated memory research, advocating for a comprehensive, interdisciplinary approach to advance our understanding of how memory is shaped and represented in the digital age. [ABSTRACT FROM AUTHOR]

In the digital age, the relationship between human memory and media is intimate and impossible to separate. This understanding serves as the foundation for this study of mediated memory, which includes an investigation into how memory is stored, represented, and communicated via different forms of media. Despite its significance, there has yet to be a comprehensive review leading to a complete understanding of mediated memory research, particularly from the 1950s to the 2010s. This paper aims to address this knowledge gap through an extensive literature review. It examines research spanning multiple decades, focusing on the methods and paradigms used in previous mediated memory studies. The review uncovers a dual-sided representation of memory. Neurological-cognitive science research suggests that memory is represented through technology, particularly in neuroimaging techniques. Conversely, humanities and social science research highlight memory's cultural representation through digital media, such as television, computers, smartphones, the internet, and social media. The review reveals a dichotomy in mediated memory research, with a risk of oversimplification due to the distinct paradigms. However, since the late 2000s, an interdisciplinary approach has gained momentum, leading to a more integrated perspective on mediated memory. This paper offers a comprehensive review of mediated memory studies from the 1950s to the 2010s, providing historical context, key theories, methodologies, and debates. It also identifies three distinct trajectories in these studies and highlights the gaps and issues that exist in the current research. These findings carry profound implications for future mediated memory research, advocating for a comprehensive, interdisciplinary approach to advance our understanding of how memory is shaped and represented in the digital age. [ABSTRACT FROM AUTHOR]

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges. [ABSTRACT FROM AUTHOR]

This study investigated the knowledge domain of second language acquisition (SLA) research in 2009-2019. It selected about a decade of studies in the SLA knowledge domain and applied an available data analysis tool CiteSpace to show a picture of SLA research. The 11-year period of studies was retrieved from the Web of Science database, totally 4, 660 bibliographic records. By employing the embedded algorithm analyzing literature in CiteSpace, the present study found the factors influencing SLA and statistical models were critical aspects in L2 research, and research topics were conducted in psychological, neuroscience, linguistic, and language teaching frameworks. Additionally, research patterns in the SLA domain have become more diversified, and empirical studies of cognitive science have gradually taken a dominant position in recent years. Spikes of increased numbers of detected papers in the period of 2009-2019 showed research focus and methodology would possibly continue to be interdisciplinary in future research. [ABSTRACT FROM AUTHOR]

Purpose: Organizations face a paradox. Because of the disruptions of COVID-19, learning and development was largely put on hold. However, this disruption also changed the value proposition for employees: they expect learning and development to be prioritized. The purpose of this paper is to resolve this paradox by providing a strategic framework that increases the capacity for workplace learning within the constraints of a hybrid world. Design/methodology/approach: Although the COVID-19 disrupted shifted when and where employees learn, it did not change how learning occurs. Therefore, this paper draws from research on workplace learning, cognitive science and neuroscience to develop a conceptual framework of workplace learning and provide practical guidance on how leaders can support it in a hybrid world. Findings: This paper presents a new framework for workplace learning. First, this paper identifies seven key workplace learning behaviors. This paper addresses why a focus on behavior over outcomes is strategically advantageous for hybrid learning. Second, this paper details the opportunities, resources and leadership behaviors that enable each behavior. Research limitations/implications: This paper provides scholars with a new approach to learning and opens avenues for research on the antecedents of workplace learning behaviors, as well as understanding how the behaviors interact over time. Practical implications: This paper helps executives make strategic decisions about hybrid learning based on the science of learning. This paper also provides key tactics for how to encourage and enable employees to learn in remote or hybrid environments. Originality/value: Although there is an abundance of research on individual, team and organizational learning, there is little guidance on what strategies leaders can use to enable learning in the moment, when it is needed most. This paper reorients learning strategy away from learning outcomes to focus on the behaviors that are required to achieve those outcomes. In doing so, this paper provides a model for learning how to learn in a hybrid world. [ABSTRACT FROM AUTHOR]

Knauff and Gazzo Castañeda (2022) object to using the term "new paradigm" to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term "paradigm" may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved. [ABSTRACT FROM AUTHOR]

Copyright of Studies about Language / Kalbu Studijos is the property of Studies about Language / Kalbu Studijos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Background. Utilizing cognitive technology has significantly improved educational outcomes, with a real impact on the effectiveness of the learning process. Cognitive technology allows teachers to present lesson content that is aligned with students' level of understanding and learning styles, resulting in more effective and efficient teaching. The use of cognitive technology not only increases the efficiency of the learning process but also creates a more thoughtful and focused learning environment for students. Purpose. ObjectiveThis research aims to determine the impact of using cognitive technology in increasing the efficiency of the learning process. As well as knowing the effect of using cognitive technology which can increase efficiency in the learning process. So that the desired learning objectives are achieved. Method. This research uses quantitative methods so that it can provide data in the form of accurate numbers. Data was obtained by distributing an online questionnaire via the Google Form application to respondents. The respondents' results will be managed using the SPSS application. After data management, researchers present the data in the form of graphs or diagrams. Results. From the results of this research, it can be seen that cognitive technology has a significant impact in increasing the efficiency of the learning process. By using cognitive technology that can be accessed anytime and anywhere, it can make it easier for students to understand the material. So as to create an efficient learning process. Conclusion. From this research, researchers can conclude that the impact of using cognitive technology can increase efficiency in the learning process. So as to create a safe and comfortable learning atmosphere. And achieving the desired learning goals. [ABSTRACT FROM AUTHOR]

Bounded rationality gets a bad rap in epistemology. It is argued that theories of bounded rationality are overly context‐sensitive; conventionalist; or dependent on ordinary language (Carr, 2022; Pasnau, 2013). In this paper, I have three aims. The first is to set out and motivate an approach to bounded rationality in epistemology inspired by traditional theories of bounded rationality in cognitive science. My second aim is to show how this approach can answer recent challenges raised for theories of bounded rationality. My third aim is to clarify the role of rational ideals in bounded rationality. [ABSTRACT FROM AUTHOR]

Current policy in England, informed by a version of cognitive science, enforces an absolute distinction between experts and novices, or teachers and learners; from this binary, operating in tandem with regimes of performativity instantiated in a curriculum shaped by high-stakes testing, are derived particular forms of pedagogy and particular pedagogic relations. And yet what happens in classrooms is seldom reducible to the prescriptive dictates of policy. Even in tightly constrained contexts, teachers and students act in ways that open up other possibilities, other ways of responding to each other and to the texts that are read together. An account of a single observed lesson, taught by a pre-service teacher, explores some of these tensions and suggests the need for different ways of understanding what is accomplished within English as a school subject. [ABSTRACT FROM AUTHOR]

The role of artificial intelligence (AI) in the education sector has received considerable attention from scholars; however, its potency and application among tourism learners are yet to be discerned. Accordingly, this research note articulates the potential impact and limitations of the AI chatbot, ChatGPT, in tourism education practices. In particular, the paper aims to raise awareness among tourism educators regarding the disruptive potential of ChatGPT and its impact on academic integrity and ethics. This paper contributes to tourism education practices on AI and robotics by critically discussing ChatGPT from a cognitive science perspective and providing recommendations on how to minimise AI plagiarism in tourism education. This research argues for adapting novel pedagogies as technology evolves and raises the need for AI ethical use guidelines in tourism education. [ABSTRACT FROM AUTHOR]