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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]

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This paper explores the potential of neuroscience for improving educational practice by describing the perspective of educational psychology as a linking science; providing historical context showing educational psychology's 100-year search for an educationally relevant neuroscience; offering a conceptual framework for the connections among neuroscience, cognitive science, educational psychology, and educational practice; and laying out a research agenda for the emerging field of educational neuroscience.

The paper deals with the aspects of professional training of specialists in computational linguistics by the example of the University of Stuttgart. First of all, we have attempted to define the essence of the terms "applied linguistics" and "computational linguistics" based on the views of Ukrainian and foreign scholars. We have found out that there is an obvious inconsistency in using these terms as Ukrainian scholars view it as a science related to practical application of linguistic achievements with the use of the latest technologies, whereas abroad they believe that computational linguistics is aimed at developing strategies for researches on natural languages. However, applied linguistics is related to language teaching methodology. We have concluded that German scholars view computational linguistics as a cognitive science that attempts to most successfully apply the achievements of linguistics in practice. Based on the view, German universities develop curricula that encompass theoretical and practical aspects of computational linguistics to prepare modern specialists able to comprehend the complexity of the field and be willing to adapt to challenges of a globalized world. Consequently, we have described the peculiarities of the programmes of a Bachelor of Science in Machine Language Programming and a Master of Science in Computational Linguistics. We have defined that duration of the programmes is traditional--three and two years respectively. Their structure comprises obligatory and elective courses, including Computer Science, Mathematics and Linguistics. In addition, future specialists may develop key professional competences attending seminars and colloquiums, participating in research projects, etc. It has been indicated that the list of electives involves those subjects aimed at enhancing future specialists' skills in linguistics as well as computer science, yet they are entitled to suggest what they are interested in even if such courses are not included. We have also suggested that rather perspective are further studies on the essence of the terms "applied linguistics" and "computational linguistics" based on the research findings of scholars all over the world.

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]

This study uses several bibliometric indices to explore the temporal course of publication trends regarding the bilingual advantage in executive control over a ten-year window. These indices include the number of published papers, numbers of citations, and the journal impact factor. According to the information available in their abstracts, studies were classified into one of four categories: supporting, ambiguous towards, not mentioning, or challenging the bilingual advantage. Results show that the number of papers challenging the bilingual advantage increased notably in 2014 and 2015. Both the average impact factor and the accumulated citations as of June 2016 were equivalent between categories. However, of the studies published in 2014, those that challenge the bilingual advantage accumulated more citations in June 2016 than those supporting it. Our findings offer evidence-based bibliometric information about the current state of the literature and suggest a change in publication trends regarding the literature on the bilingual advantage. [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]

Artificial neural network models are able to achieve great performance at numerous computationally challenging tasks like face recognition. It is of significant importance to explore the difference between neural network models and human brains in terms of computational mechanism. This issue has become an experimental focus for some researchers in recent studies, and it is believed that using human behavior to understand neural network models can address this issue. This paper compares the neural network model performance with human performance on a classic yet important task: judging the ethnicity of a given face. This study uses Caucasian and East Asian faces to train 4 neural networks including AlexNet, VGG11, VGG13, and VGG16. Then, the ethnicity judgments of the neural networks are compared with human data using classical psychophysical methods by fitting psychometric curves. The results suggest that VGG11, followed by VGG16, shows a similar response pattern as humans, while simpler AlexNet and more complex VGG13 do not resemble human performance. Thus, this paper explores a new paradigm to compare neural networks and human brains. • Neural networks are able to provide cognitive approaches from face perception. • Human perception is used as a benchmark to evaluate the performance of neural networks. • Neural networks utilizing eye regions are confirmed to be better to perceive faces. • Attentional region analysis would unveil the processing details of a neural network. [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]

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]

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]

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]

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]

As such, the workshop has always been located in Italy, with the workshop Steering Committee constantly committed to involve every major Italian research group working on agents and multi-agent systems. The first Workshop "From Objects to Agents" (WOA) was held in Parma in May 2000. The topics discussed in the papers covered some of the most debated subjects in the research on agents and multi-agent systems, and were not limited to the theme of the workshop, which was suggested in the call for papers as I multi-agent systems in the machine learning era i . [Extracted from the article]

The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by factors like sentence length and lexical frequency. In this paper, we present the results of a set of large-scale experiments using crowd-sourced acceptability judgments that demonstrate gradience to be a pervasive feature in acceptability judgments. We then show how one can predict acceptability judgments on the basis of probability by augmenting probabilistic language models with an acceptability measure. This is a function that normalizes probability values to eliminate the confounding factors of length and lexical frequency. We describe a sequence of modeling experiments with unsupervised language models drawn from state-of-the-art machine learning methods in natural language processing. Several of these models achieve very encouraging levels of accuracy in the acceptability prediction task, as measured by the correlation between the acceptability measure scores and mean human acceptability values. We consider the relevance of these results to the debate on the nature of grammatical competence, and we argue that they support the view that linguistic knowledge can be intrinsically probabilistic.

The paper concerns an important but underappreciated genre of algorithmic puzzles, explaining what these puzzles are, reviewing milestones in their long history, and giving two different ways to classify them. Also covered are major applications of algorithmic puzzles in cognitive science research, with an emphasis on insight problem solving, and the advantages of algorithmic puzzles over some other classes of problems used in insight research. The author proposes adding algorithmic puzzles as a separate category of insight problems, suggests 12 specific puzzles that could be useful for research in insight problem solving, and outlines several experiments dealing with other cognitive aspects of solving algorithmic puzzles.

Dance Improvisation is an essential skill and tool for dancers. It is grounded in the kinesthetic experience and its constantly changing dynamic qualities through self-movement. It requires a spontaneous kinesthetic response in a spatiotemporal vigorous qualitative dynamic happening of affect and movement; a momentum that allows dancers to perform innovative material and as such supports the creative process. The current study raises questions as to whether dance improvisation can be sufficiently examined by the "Theory of enaction," developed by scientists from late 70s on. This theory is an attempt to explain the brain and bridge the gap between mind and experience. The inquiry in this paper asks on the one hand, to what extent "enaction" as set forth by contemporary neuroscientists and cognitive scientists is sufficient to account for the reality of dance improvisation, and on the other hand, how written descriptions of improvisation in dance education can advance understandings of the relationship of consciousness and movement or mind and body.

Carnap and Popper proposed ways scientists have to work. According to Carnap, they should look for confirmations for hypotheses. In Popper's view, what is important is to try to falsify hypotheses. Cognitive science seems to prove that, in real scientific research, both activities play a role. First, people attempt to confirm hypotheses. Second, they seek examples refuting those hypotheses. This paper is intended to show that the theory of mental models can describe the mental processes involved in both tasks: confirmation and falsification. It addresses the mental possibilities individuals consider in both cases. In addition, the paper reveals that, in accordance with both Carnap's framework and Popper's approach, both mental activities are related to conditional reasoning. [ABSTRACT FROM AUTHOR]

Classical works on writing within cognitive science focus either on the aspects of written text production or the role of writing from a developmental and evolutionary perspective (Donald, 1991; Flower & Hayes, 1981; Goody, 1977; Olson, 1996). Studies concerning the practices of writing in real-world settings are carried out within the framework of situated cognition (Klein & Leacock, 2012; Menary, 2007; O'Hara et al., 2002), but they mainly focus on writer-writer or writer-artefact interactions. This paper provides an ethnographic account of writing that is understood as a practice that is distributed in a dynamically changing sociomaterial system. The cognitive task under study involves making decisions concerning the contents of a formal document as well as turning them into written text, both of which are supposed to take place in an informed and collaborative manner. Task performance depends on establishing a dense network of interactions that involves numerous sources of expert knowledge and people from various professional backgrounds who often represent clashing interests. The focus of the study is 1) identifying the resources employed in order to complete the task, 2) determining what functions they perform, 3) describing the ways that they are coordinated within the distributed cognitive system so that as a whole it creates incentives for some actions while constraining others. The analysis reveals how several resources contribute to realisation of cognitive operations while at the same time producing powerful effects upon the organisation of the cultural practices (Hutchins 2008) within the distributed cognitive system. [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 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]

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]

Purpose: The purpose of this paper is to challenge processes of organizational learning and innovation that are based on making use of, extrapolating, or adapting past experiences and knowledge, because such a strategy turns out to be incapable of dealing with the challenges of today's volatile, uncertain, complex and ambiguous environment. As a possible way out, a conceptual model is proposed that integrates organizational learning and innovation as a future-driven learning process and a future-making practice. Design/methodology/approach: This work is conceptual in nature, drawing on (both theoretical/philosophical and empirical) interdisciplinary concepts and evidence from a variety of fields, including organizational studies, organizational learning, innovation studies, systems theory and systems biology, as well as cognitive science. Findings: The author proposes a paradigm shift in organizational learning: from a future-oriented perspective, organizational learning can be viewed as an innovation process that is based on "learning from the future as it emerges." A conceptual approach is presented that integrates future-oriented innovation and organizational learning as a future-making practice. It is based on learning from future potentials as a source for sustainable innovations. Both epistemological/ontological foundations and organizational implications are discussed. Originality/value: This paper introduces a new perspective on the role of future-oriented innovation in the context of organizational learning. It shows how organizational learning and innovation can be integrated and how shortcomings of absorptive capacity can be overcome by assuming a future-driven perspective. Furthermore, an epistemology of future knowledge/potentials and its applications for organizations is developed. [ABSTRACT FROM AUTHOR]

In this paper, a definition outlining the scope and goals of the field of Cognitive Aspects of Virtual Reality (cVR) is provided. Leading up to and alongside the definition, the paper includes a discussion on the background behind cVR – with a special focus on new human-AI capablities driven by cognitive, psychological, social and technological factors. Finally, the paper provides an outline of related research fields that can act as synergies in relation to cVR, while at the same time formulates questions and hypotheses that may drive future research in cVR. [ABSTRACT FROM AUTHOR]

Consciousness, central to our existence, challenges scientific characterization. This paper explores the nexus between consciousness, the mind, and quantum mechanics, aiming to bridge gaps in understanding. Innovative hypotheses suggest highspeed data retrieval and cognition mechanisms. The “Wow!” experience is probed as a cognitive phenomenon providing unique insights. The paper offers a grounded perspective, integrating theories and signaling a promising direction for future research in neuroscience, cognitive sciences, and quantum physics. The nature of consciousness has long puzzled science and philosophy. Despite progress in neuroscience and quantum physics, our understanding is incomplete. This paper delves into the potential interrelationship between mind, consciousness, and quantum phenomena. Consciousness, enigmatic for centuries, eludes comprehensive scientific understanding. This research explores the relationship between mind, consciousness, and quantum mechanics, using insights from neuroscience, cognitive science, physics, and information theory. The Objective Reduction (OR) theory posits a quantum basis for consciousness within neural microtubules, shaping this exploration. The research also delves into mind mechanisms, challenging conventional paradigms by suggesting high-speed information retrieval from an unknown dimension. It introduces “Wow!” moments, examining them empirically and theoretically for insights into consciousness. By merging established theories, innovative hypotheses, and rigorous methodology, this research opens avenues for empirical validation and theoretical advancement, contributing to a nuanced understanding of the quantum realm, the mind, and consciousness. This Introduction sets the stage for a detailed examination of these themes, providing a roadmap for the complex journey ahead. [ABSTRACT FROM AUTHOR]

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words (e.g., "chair" is a member of the furniture category). We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: (a) the acquisition of features that discriminate among categories, and (b) the grouping of concepts into categories based on those features. Our model learns categories "incrementally" using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (An earlier version of this work was published in Frermann and Lapata [Frermann, L., 2014].)

The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called "semantic pointers." Semantic pointers are symbol-like representations that result from the compression and recursive binding of perceptual, lexical, and motor representations, effectively integrating traditional connectionist and symbolic approaches. We present a computational model using semantic pointers that replicates experimental data from categorization studies involving each prior paradigm. We argue that a framework involving semantic pointers can provide a unified account of conceptual phenomena, and we compare our framework to existing alternatives in accounting for the scope, content, recursive combination, and neural implementation of concepts.

We reflect upon a paper we wrote that was published in 1995 (20 years ago). We outline the motivation for the work and situate it in the state of the art at that time. We suggest that a key contribution was to highlight the need to provide support for learners who reason with external representations. The support must be flexible enough to accommodate wide individual differences in representational knowledge. In 1995 we saw AIED systems as potentially having several roles; one key role was to enable the learner to move easily from one external representation (ER) to a different one (switching ERs). Other necessary roles included: to seek to use AI to keep several forms of ER in synchrony as the student works on one of them; to guide the student to one ER form (out of several) most likely to be of use; and to help the student use an ER effectively. We outline where our work seems to have had influence and we review progress in the field since 1995. In our conclusion we identify future research agendas that emerge from recent developments in cognitive science.

The paper proposes a new interdisciplinary approach to helping low attaining learners in basic mathematics. It reports on the research-informed design and user testing of an adaptive digital game based on constructionist tasks with intrinsic feedback. The approach uses findings from the neuroscience of dyscalculia, cognitive science research on conceptual understanding in mathematics, and mathematical education research to inform the detailed pedagogic design. It is interdisciplinary in the sense that it synthesises the results from multiple disciplines in the design principles. It then exploits the new capabilities of digital technologies to develop the design for testing with learners, and capturing appropriate data. The initial pilot has shown that the game supports learners age 5-7 years for independent learning of the kind that low attaining learners will need in order to keep pace with mainstream learners. The experimental work will evaluate this and similar games for learners of all ages who have low numeracy. In general, the approach is to (i) focus on a problem at the intersection of robust evidence in both education and neursocience; and (ii) use this data to design and test a digital intervention that fully exploits the adaptive and interactive features of learning technology.

Constructivism is undoubtedly one of the most influential philosophies in education in the twenty-first century. It is, however, one that is frequently misunderstood, has diverse meanings ascribed to it, and is often used by advocates to caricature other approaches inaccurately. The learning theory of constructivism and pedagogical theory of constructivism do not necessitate one another although educators frequently consider them one and the same. This paper underscores how educators in the twenty-first century are increasingly compelled to accept constructivist pedagogy. It further reflects on a challenges this poses for learning, concerns regarding constructivism as the dominant pedagogical tool, and clarity with respect to the consideration of alternative approaches while offering specific reflections for classroom teachers throughout.