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This study explores the patterns of exchange of research knowledge among Education Research, Cognitive Science, and what we call "Border Fields." We analyze a set of 32,121 articles from 177 selected journals, drawn from five sample years between 1994 and 2014. We profile the references that those articles cite, and the papers that cite them. We characterize connections among the fields in sources indexed by Web of Science (WoS) (e.g., peer-reviewed journal articles and proceedings), and connections in sources that are not (e.g., conference talks, chapters, books, and reports). We note five findings—first, over time the percentage of Education Research papers that extensively cite Cognitive Science has increased, but the reverse is not true. Second, a high percentage of Border Field papers extensively cite and are cited by the other fields. Border Field authors' most cited papers overlap those most cited by Education Research and Cognitive Science. There are fewer commonalities between Educational research and Cognitive Science most cited papers. This is consistent with Border Fields being a bridge between fields. Third, over time the Border Fields have moved closer to Education Research than to Cognitive Science, and their publications increasingly cite, and are cited by, other Border Field publications. Fourth, Education Research is especially strongly represented in the literature published outside those WoS-indexed publications. Fifth, the rough patterns observed among these three fields when using a more restricted dataset drawn from the WoS are similar to those observed with the dataset lying outside the WoS, but Education Research shows a far heavier influence than would be indicated by looking at WoS records alone. [ABSTRACT FROM AUTHOR]

In the paper, I Traffic flow guidance algorithm in intelligent transportation systems considering the effect of non i - I floating vehicle i , Chen et al. ([4]) present the method of estimating non-floating vehicles' driving information according to floating vehicles' information. The authors presented the estimation method, a new traffic flow guidance algorithm, Estimated Weighted Vehicle Density Feedback Strategy (EWVDFS) based on Weighted Vehicle Density Feedback Strategy (WVDFS). In the paper, A I robust lane detection method based on hyperbolic model i , Li et al. ([9]) investigated a robust lane detection method under structured roads. The paper I Road network i - I based region of interest mining and social relationship recommendation i by Tan and Zhang ([17]) proposed road context-based active region extraction algorithm (RAREA) which explores the method to extract the specific regions within the road network. [Extracted from the article]

In a society where computers have become ubiquitous, it is necessary to develop a broader understanding of the nature of computing and programming, not just from a technical viewpoint but also from a historical and philosophical perspective. Computers and computer programs do not exist in a vacuum. Instead, they are a part of a rich socio-technological context that provides ways for understanding computers and reasoning about programs. This includes not only formal logic, mathematics, sciences, and technology but also cognitive sciences and sociology. The focus of this special issue is on questions that arise when we consider computing and programming in a wider context. In particular, the papers in this special issue explore the interplay between computing or programming and mathematics, formal logic, sciences, technology, and society. [ABSTRACT FROM AUTHOR]

Among those living systems, which are cognizers? Among the behaviours of, and causes of behaviour in, living systems, which are cognitive? Such questions sit at the heart of a sophisticated, ongoing debate, of which the recent papers by Corcoran et al. (2020) and Sims and Kiverstein (2021) serve as excellent examples. I argue that despite their virtues, both papers suffer from flawed conceptions of the point of the debate. This leaves their proposals ill-motivated—good answers to the wrong question. Additionally, their proposals are unfit to serve the legitimate roles for characterizations of cognition. [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]

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]

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]

Since the publication of "the case against grammar correction in L2 writing classes" in 1996, written corrective feedback has become a controversial issue in second language writing instruction (Lee, [27]; for a review, study Reinders & Mohebbi, [28]) as far as almost every single article investigating the effect of feedback on L2 writing improvement refers to this paper (citation = 2480). On the one hand, Truscott, in a series of research publications (Truscott, [29], [30], [31], [32], [33], [34], [35], [36]; (Truscott: The efficacy of written corrective feedback: A critique of a meta-analysis, unpublished); Truscott & Hsu, [37]) argued against the perceived and claimed positive effect of written corrective feedback on improving writing accuracy of L2 learners. Therefore, we interviewed Professor John Truscott to have his feedback on feedback research. [Extracted from the article]

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]

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]

It is a well-established fact in representationalist cognitive science that concrete concepts influence human perception. In radical, anti-representationalist cognitive science, however, the case is far from clear. One reason for this is that proponents of Radical Enactivism (REC) yet have to clarify whether perceptual activity involving concepts is bound to rely on mental content or if it instantiates basic, contentfree cognition. The purpose of this paper is to show that concept-involving perception instantiates REC-style basic cognition. The paper begins by considering 'cognitive projection' as the term is introduced by Distributed Cognition research. Although being introduced as a means for exploring concept-based perception in non-representationalist terms, I argue that 'cognitive projection' comes with mentalist overtones and unclarities concerning its linguistic basis. With the purpose of overcoming these shortcomings, I unfold a REC-friendly reading of Wittgenstein's writings on aspect perception, arguing that basic concept-involving perception is an instantiation of basic cognition. Concludingly, with the aim of ensuring compatibility, I make a connection between conceptual aspect perception and the REC notion of attentional anchors. The latter has been introduced to explore the role of material artefacts in the context of science, technology, engineering, and mathematics educational design but has, I submit, the potential for being extended to other practical contexts. [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]

Advanced technologies such as drones, intelligent algorithms and androids have grave implications for human existence. With the purpose of exploring their basis for doing so, the paper proposes a framework for investigating the complex relationship between such devices and human practices and language-mediated cognition. Specifically, it centers on the importance of the typically neglected intermediate layer of culture which not only drives both technophobia and philia but also, more fundamentally, connects pre-reflective experience and socio-material practices by placing advanced technologies in the loop. Theoretically, the paper draws on contributions from performativist Science and Technology Studies and Radically Embodied Cognitive Science and pushes new grounds by stressing their compatibility. Yet, it also emphasizes the importance of the enlanguaged side of cognition which is a requirement for human-style socio-material practices and, hence, the emergence of cultures that fetishizes certain technologies. [ABSTRACT FROM AUTHOR]

Enactivism, a development in philosophy of mind and cognitive science, has been influential for several decades now. Yet, at the same time, this special issue intends to bring enactivism closer to applied ethics, that is, several papers in this issue investigate how enactivism can respond to contemporary ethical issues, such as environmental ethics and health care. This conception of cognition as interactive was designed as an alternative to more traditional ideas of cognition that conceived of cognition as transformations (i.e. computations) of representational mental content. They also navigate further into the relations between enactivism and neighboring ethical theories, such as care ethics, phenomenological and hermeneutical ethics, as well as relations with moral psychology and the social sciences. [Extracted from the article]

This essay takes up the framework of Semiotic Cultural Psychology, which in last decade was very productive in analyzing societal phenomena. Digging into complex sensemaking mechanisms, underlying the interaction between individual and his surrounding environment, has consolidated a base of empirical data gathered from different societies. Aiming to better understand the role of culture in this interaction, the current paper proposes to bridge these findings of observable macro-processes with the ones from the Active inference theory and cognitive science. Similarities between the two frameworks, as well as possible benefits of employing them for one goal, are discussed in this paper. [ABSTRACT FROM AUTHOR]

Around 3 million people worldwide have an arm amputation. These people face a lot of trouble in their everyday life whilst performing basic tasks. This paper proposes a novel deep learning-based approach for predicting arm movements using EEG-based signals. We plan to design and develop an active exoskeleton controlled by the same EEG-based signals to rehabilitate the amputees. The architecture design is intended to build an exoskeleton arm with at least 3 degrees of freedom that can perform complex movements and is sophisticated enough to substitute for a real arm. This prosthetic arm will be controlled using electroencephalogram (EEG) signals gathered by different devices/headsets and processed using deep learning models. The results show that our proposed approach gives excellent results. [ABSTRACT FROM AUTHOR]

This paper analyzes the skilled performance of rock climbing through the framework of Embodied and Enacted Cognitive Science. It introduces a notion of enactive planning that is part of one mindful activity of ongoing responsiveness to the affordances of the wall. The paper takes two distinct planning activities involved in rock climbing—route-reading and visualizing—and clarifies them through the enactivist and ecological concepts of nested affordances, prospecting, recalibrating, marking, and corporeal imaginings, as well as Rylean concept of heeding. The paper shows that an enactive approach to planning can make sense of both the planning done in preparation of the climb, and re-planning done during the mindful performance, without invoking additional cognitive architectures. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a topological method for constructing discretizations (tessellations) of topological conceptual spaces. The method works for a class of topological spaces that the Russian mathematician Pavel Alexandroff defined more than 80 years ago. The aim of this paper is to show that Alexandroff spaces, as they are called today, have many interesting properties that can be used to explicate and clarify a variety of problems in philosophy, cognitive science, and related disciplines. For instance, recently, Ian Rumfitt used a special type of Alexandroff spaces to elucidate the logic of vague concepts in a new way. Moreover, Rumfitt's class of Alexandroff spaces can be shown to provide a natural topological semantics for Susanne Bobzien's "logic of clearness". Mainly due to the work of Peter Gärdenfors and his collaborators, conceptual spaces have become an increasingly popular tool of dealing with a variety of problems in the fields of cognitive psychology, artificial intelligence, linguistics and philosophy. For Gärdenfors's conceptual spaces, geometrically defined discretizations (so-called Voronoi tessellations) play an essential role. These tessellations can be shown to be extensionally equivalent to topological tessellations that can be constructed for Alexandroff spaces in general. Thereby, Rumfitt's and Gärdenfors's constructions turn out to be special cases of an approach that works for a more general class of spaces, namely, for weakly scattered Alexandroff spaces. The main aim of this paper is to show that this class of spaces provides a convenient framework for conceptual spaces as used in epistemology and related disciplines in general. Weakly scattered Alexandroff spaces are useful for elucidating problems related to the logic of vague concepts, in particular they offer a solution of the Sorites paradox (Rumfitt). Further, they provide a semantics for the logic of clearness (Bobzien) that overcomes certain problems of the concept of higher-order vagueness. Moreover, these spaces help find a natural place for classical syllogistics in the framework of conceptual spaces. The specialization order of Alexandroff spaces can be used to refine the all-or-nothing distinction between prototypical and non-prototypical stimuli in favor of a gradual distinction between more or less prototypical elements of conceptual spaces. The greater conceptual flexibility of the topological approach helps avoid some inherent inadequacies of the geometrical approach, for instance, the so-called "thickness problem". Finally, it is shown that the Alexandroff spaces offer an appropriate framework to deal with digital conceptual spaces that are gaining more and more importance in the areas of artificial intelligence, computer science and related disciplines. [ABSTRACT FROM AUTHOR]

Despite the large interest in the human ability to perceive space present in neuroscience, cognitive science and psychology, as well as philosophy of mind, the issues regarding egocentric space representation received relatively less attention. In this paper I take up a unique phenomenon related to this faculty: the "spatial purport" of perceptual experiences. The notion was proposed by Rick Grush to describe the subjective, qualitative aspects of egocentric representations of spatial properties and relations. Although Grush offered an explanation of the mechanism giving rise to appearance of spatial purport, his model had considerable shortcomings. In the paper I thoroughly analyze both the notion of spatial purport and Grush's explanation of the mechanism at its core in order to develop his theory using the insights provided by the predictive processing theory of mind, and more particularly by the active inference framework. The extended account I offer, named Predictive and Hierarchical Skill Theory, explains phenomena that escaped Grush's model and furthers the research on egocentric space representation from the perspective of both neuroscience and philosophy of mind. [ABSTRACT FROM AUTHOR]

The progress of EEG-based emotion recognition has received widespread attention from the fields of human-machine interaction and cognitive science. However, recognizing emotions with limited labelled data is still challenging. To address this issue, this paper proposes a self-supervised group meiosis contrastive learning (SGMC) framework for EEG-based emotion recognition. First, to reduce the dependence of emotion labels, SGMC introduces a contrastive learning task according to the alignment of video clips based on the similar EEG response across subjects. Moreover, the model adopts a group projector to extract group-level representations from the group samples to further decrease the subject difference and random effects in EEG signals. Finally, a novel genetics-inspired data augmentation method, named meiosis is developed, which takes advantage of the alignment of video clips among a group of EEG samples to generate augmented groups by pairing, cross exchanging, and separating. The experiments show that SGMC exhibits competitive performance on the publicly available DEAP and SEED datasets. It is worth of noting that the SGMC shows a high ability to recognize emotion even when using limited labelled data. Moreover, the results of feature visualization suggest that the model might have learned video-level emotion-related feature representations to improve emotion recognition. The hyper-parametric analysis further shows the effect of the group size during emotion recognition. Finally, the comparisons of both the symmetric function and the ablation models and the analysis of computational efficiency are carried out to examine the rationality of the SGMC architecture. The code is provided publicly online. [ABSTRACT FROM AUTHOR]

Following Marr's famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. [ABSTRACT FROM AUTHOR]

Is action-guiding vision cognitively penetrable? More specifically, is the visual processing that guides our goal-directed actions sensitive to semantic information from cognitive states? This paper critically examines a recent family of arguments whose aim is to challenge a widespread and influential view in philosophy and cognitive science: the view that action-guiding vision is cognitively impenetrable. I argue, in response, that while there may very well be top–down causal influences on action-guiding vision, they should not be taken to be an instance of cognitive penetration. Assuming otherwise is to assign a computational role to the influencing states that they cannot perform. Although questions about cognitive penetrability are ultimately empirical, the issues addressed in this paper are largely philosophical. The discussion here highlights an important set of considerations that help better understand the relations between cognition, vision, and action. [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]

The received view of implicit bias holds that it is associative and unreflective. Recently, the received view has been challenged. Some argue that implicit bias is not predicated on "any" associative process, but it is unreflective. These arguments rely, in part, on debiasing experiments. They proceed as follows. If implicit bias is associative and unreflective, then certain experimental manipulations cannot change implicitly biased behavior. However, these manipulations can change such behavior. So, implicit bias is not associative and unreflective. This paper finds philosophical and empirical problems with that argument. When the problems are solved, the conclusion is not quite right: implicit bias is not necessarily unreflective, but it seems to be associative. Further, the paper shows that even if legitimate non-associative interventions on implicit bias exist, then both the received view and its recent contender would be false. In their stead would be interactionism or minimalism about implicit bias. [ABSTRACT FROM AUTHOR]

Several contemporary philosophers have been articulating tenets in pragmatism (broadly construed) to motivate this view as an alternative philosophical foundation for a comprehensive understanding of cognition, opposed to a far-reaching representationalist tradition. After criticizing standard representational accounts of numerical cognition for their lack of explanatory power, he argues that a non-representational approach that is inspired by radical enactivism offers the best hope for developing a genuine naturalistic explanatory account for these cognitive capacities. This long-established representationalist tradition in philosophy of mind and cognitive science defends that cognition is fundamentally content-involving. [Extracted from the article]

Over the past few decades, cognitive science has identified several forms of reasoning that make essential use of conceptual knowledge. Despite significant theoretical and empirical progress, there is still no unified framework for understanding how concepts are used in reasoning. This paper argues that the theory of conceptual spaces is capable of filling this gap. Our strategy is to demonstrate how various inference mechanisms which clearly rely on conceptual information—including similarity, typicality, and diagnosticity-based reasoning—can be modeled using principles derived from conceptual spaces. Our first topic analyzes the role of expectations in inductive reasoning and their relation to the structure of our concepts. We examine the relationship between using generic expressions in natural language and common-sense reasoning as a second topic. We propose that the strength of a generic can be described by distances between properties and prototypes in conceptual spaces. Our third topic is category-based induction. We demonstrate that the theory of conceptual spaces can serve as a comprehensive model for this type of reasoning. The final topic is analogy. We review some proposals in this area, present a taxonomy of analogical relations, and show how to model them in terms of distances in conceptual spaces. We also briefly discuss the implications of the model for reasoning with concepts in artificial systems. [ABSTRACT FROM AUTHOR]

This special issue is dedicated to reassessing the relationship between phenomenology and explanation. The editors' introduction serves to provide a brief historical analysis of the sources and the reasons for thinking that phenomenology neither is nor ought to be explanatory before moving on to challenge this commonplace assumption by reference to Husserl, and by pointing out that there are various developments within the field of explanation that merit a re-examination of this topic. The introduction highlights the importance of explanation as a topic vis a vis the naturalization of phenomenology and provides an overview of the contributions to the special issue. [ABSTRACT FROM AUTHOR]

This paper intends to further the understanding of the formal properties of (higher-order) vagueness by connecting theories of (higher-order) vagueness with more recent work in topology. First, we provide a "translation" of Bobzien's account of columnar higher-order vagueness into the logic of topological spaces. Since columnar vagueness is an essential ingredient of her solution to the Sorites paradox, a central problem of any theory of vagueness comes into contact with the modern mathematical theory of topology. Second, Rumfitt's recent topological reconstruction of Sainsbury's theory of prototypically defined concepts is shown to lead to the same class of spaces that characterize Bobzien's account of columnar vagueness, namely, weakly scattered spaces. Rumfitt calls these spaces polar spaces. They turn out to be closely related to Gärdenfors' conceptual spaces, which have come to play an ever more important role in cognitive science and related disciplines. Finally, Williamson's "logic of clarity" is explicated in terms of a generalized topology ("locology") that can be considered an alternative to standard topology. Arguably, locology has some conceptual advantages over topology with respect to the conceptualization of a boundary and a borderline. Moreover, in Williamson's logic of clarity, vague concepts with respect to a notion of a locologically inspired notion of a "slim boundary" are (stably) columnar. Thus, Williamson's logic of clarity also exhibits a certain affinity for columnar vagueness. In sum, a topological perspective is useful for a conceptual elucidation and unification of central aspects of a variety of contemporary accounts of vagueness. [ABSTRACT FROM AUTHOR]

In this paper, we address the question of how an agent can guide its behavior with respect to aspects of the sociomaterial environment that are not sensorily present. A simple example is how an animal can relate to a food source while only sensing a pheromone, or how an agent can relate to beer, while only the refrigerator is directly sensorily present. Certain cases in which something is absent have been characterized by others as requiring 'higher' cognition. An example of this is how during the design process architects can let themselves be guided by the future behavior of visitors to an exhibit they are planning. The main question is what the sociomaterial environment and the skilled agent are like, such that they can relate to each other in these ways. We argue that this requires an account of the regularities in the environment. Introducing the notion of general ecological information, we will give an account of these regularities in terms of constraints, information and the form of life or ecological niche. In the first part of the paper, we will introduce the skilled intentionality framework as conceptualizing a special case of an animal's informational coupling with the environment namely skilled action. We will show how skilled agents can pick up on the regularities in the environment and let their behavior be guided by the practices in the form of life. This conceptual framework is important for radical embodied and enactive cognitive science, because it allows these increasingly influential paradigms to extend their reach to forms of 'higher' cognition such as long-term planning and imagination. [ABSTRACT FROM AUTHOR]

The aim of the paper is to discuss some possible connections between philosophical proposals about the social organisation of science and developments towards a greater democratisation of science policy. I suggest that there are important similarities between one approach to objectivity in philosophy of science-Helen Longino's account of objectivity as freedom from individual biases achieved through interaction of a variety of perspectives-and some ideas about the epistemic benefits of wider representation of various groups' perspectives in science policy, as analysed by Mark Brown. Given these similarities, I suggest that they allow one to approach developments in science policy as if one of their aims were epistemic improvement that can be recommended on the basis of the philosophical account; analyses of political developments inspired by these ideas about the benefits of inclusive dialogue can then be used for understanding the possibility to implement a philosophical proposal for improving the objectivity of science in practice. Outlining this suggestion, I also discuss the possibility of important differences between the developments in the two spheres and show how the concern about the possible divergence of politically motivated and epistemically motivated changes may be mitigated. In order to substantiate further the suggestion I make, I discuss one example of a development where politically motivated and epistemically motivated changes converge in practice-the development of professional ethics in American archaeology as analysed by Alison Wylie. I suggest that analysing such specific developments and getting involved with them can be one of the tasks for philosophy of science. In the concluding part of the paper I discuss how this approach to philosophy of science is related to a number of arguments about a more politically relevant philosophy of science. [ABSTRACT FROM AUTHOR]

Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc analytic techniques from Explainable AI can be used to refine target phenomena in medical science, to identify starting points for future investigations of (potentially) causal relationships, and to generate possible explanations of target phenomena in cognitive science. In this way, this paper describes how Explainable AI—over and above machine learning itself—contributes to the efficiency and scope of data-driven scientific research. [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]

For most, an improvement in memory would always be desirable, whether from the point of view of an aging individual with declining memory, or from the perspective of someone seeking to memorize large amounts of information in the shortest period of time. One way for people to improve upon their memory performance is by using the Method of Loci (MoL), a famously complex, ancient memorization technique for non-spatial information recall. With the use of virtual reality technology, this technique can finally be easily taught to individuals for use in their daily lives. In this paper, we present an exploration into this avenue of using MoL in virtual reality and report on the design and evaluation of our new virtual memory palace that aims to prove the feasibility of improving upon designs from other studies to optimize memory recall performance. An experiment was conducted to evaluate our VR MoL environment. The results from week 1 on the pre-test (M = 62.55, SD = 24.01) and post-test (M = 82.91, SD = 15.99) memory task showed an increase in the number of words remembered was statistically significant, t(20) = -2.34, p = 0.014 where participants were able to remember approximately 20.4% more non-spatial information, when compared to traditional memorization techniques. After a second use, participants improved, remembering 22.2% more non-spatial information on the pre-test (M = 63.44, SD = 26.64) and post-test (M = 85.67, SD = 16.10) memory task, indicating that the increase in number of words remembered was statistically significant, t(16) = -2.142, p = 0.024. The results suggest that the virtual memory palace experience could be optimized to help participants learn the MoL technique with very little training time and potentially produce significant improvements in recall performance as a result. [ABSTRACT FROM AUTHOR]

The study of the contrast between fundamental aspects of spatial and temporal awareness offers a good opportunity to bring to light the relation between philosophical and scientific theories of consciousness. In this paper we critically examine important work by Rick Grush on spatial and temporal experience, and we show that while there is a valid claim for the relevant neuroscientific model to be one that supports Gareth Evans's stance on "behavioral space", there is not at present any scientific model that offers comparable support for philosophical theories of temporal experience, despite some claims by Grush that might suggest the contrary. Moreover, we argue that careful attention to the spatial case allows us to locate the point at which even relatively successful cases of neuroscientific modeling and explanation are left wanting when their aim is to show that phenomenal features of experience are a function of the representational structures produced by our neural information-processing machinery: an aim widely shared by the predominant programmatic stance in current neuroscience research. [ABSTRACT FROM AUTHOR]

Structural representations are increasingly popular in philosophy of cognitive science. A key virtue they seemingly boast is that of meeting Ramsey's job description challenge. For this reason, structural representations appear tailored to play a clear representational role within cognitive architectures. Here, however, I claim that structural representations do not meet the job description challenge. This is because even our most demanding account of their functional profile is satisfied by at least some receptors, which paradigmatically fail the job description challenge. Hence, the functional profile typically associated with structural representations does not identify representational posits. After a brief introduction, I present, in the second section of the paper, the job description challenge. I clarify why receptors fail to meet it and highlight why, as a result, they should not be considered representations. In the third section I introduce what I take to be the most demanding account of structural representations at our disposal, namely Gładziejewski's account. Provided the necessary background, I turn from exposition to criticism. In the first half of the fourth section, I equate the functional profile of structural representations and receptors. To do so, I show that some receptors boast, as a matter of fact, all the functional features associated with structural representations. Since receptors function merely as causal mediators, I conclude structural representations are mere causal mediators too. In the second half of the fourth section I make this conclusion intuitive with a toy example. I then conclude the paper, anticipating some objections my argument invites. [ABSTRACT FROM AUTHOR]

Rapid advances in technology during the last few decades have provided a multitude of new options for teaching and learning. Although technology is being widely adopted in education, there is a shortage of research on the effects that this technology might have on student learning, and why those effects occur. We conducted a comprehensive review of the literature on various uses of digital technology in educational settings, and the effects of that technology on students' objective learning outcomes. We interpret these effects within the context of empirical research on effective principles of learning, and the extent to which the affordances of technology permit opportunities for increased engagement with the material, retrieval practice, and spacing. Results revealed that technology is neither beneficial nor harmful for learning when used primarily as a means of presenting information (e.g., information viewed on a computer screen vs. on paper), but can be beneficial when it involves unique affordances that leverage effective learning principles. We discues these findings in light of the ever-increasing availability of technology in education, and the importance of evidence-guided criteria in decisions about adoption and implementation. [ABSTRACT FROM AUTHOR]

Intellectualists suggest that practical knowledge, or 'knowing- how', can be reduced to propositional knowledge, or 'knowing-that'. Anti-intellectualists, on the contrary, suggest, following the original insights by Ryle, that such a reduction is not possible. Rejection of intellectualism can be proposed either by offering purely philosophical analytical arguments, or by recruiting empirical evidence from cognitive science about the nature of the mental representations involved in these two forms of knowledge. In this paper, I couple these two strategies in order to analyze some crucial reasons for which intellectualism seems not to be the best theory we have to correctly understand and describe practical knowledge. In particular, I will start from a specific philosophical account against intellectualism offered by Dickie (Philos Phenomenol Res LXXXV(3):737–745, 2012), and suggest that it can be supported by current experimental results coming from motor neuroscience. The claim of the paper is that there is at least one kind of practical knowledge, which I call motor knowledge, and which is at the basis of the performance of skilled action, which cannot be reduced to propositional knowledge. [ABSTRACT FROM AUTHOR]

How does attention contribute to perceptual experience? Within cognitive science, attention is known to contribute to the organization of sensory features into perceptual objects, or 'object-based organization.' The current paper tackles a different type of organization and thus suggests a different role for attention in conscious perception. Within every perceptual experience we find that more subjectively interesting percepts stand out in the foreground, whereas less subjectively interesting percepts are relegated to the background. The sight of a sycamore often gains the visual foreground for a nature lover, whereas the sound of a violin often gains the auditory foreground for a music lover, but not necessarily vice versa. How does the perceptual system organize early sensory processing according to the subject's interests? The current paper reveals how this subject-based organization is brought about and maintained through top-down attention. In fact, the current paper argues that top-down attention is necessary for conscious perception in so far as it is necessary for bringing about and maintaining the subject-based organization of perceptual experience. [ABSTRACT FROM AUTHOR]

There is now an established literature on the link between later Wittgenstein and enactivist approaches in cognitive science. However, is this link not just a matter for card carrying Wittgensteinians? Can enactivists not manage perfectly well without Wittgenstein? In this paper, I show why some enactivists should care about Wittgenstein. Focusing on the enactivist view, "Sensorimotor Identity". I argue that proponents of this view can use Wittgensteinian considerations to resolve an issue confronting their view and thereby shore up their proposed dissolution of the explanatory gap. Some enactivists thus have in fact much to gain by engaging with Wittgenstein. [ABSTRACT FROM AUTHOR]

Collective memory researchers predominantly in the cultural and social sciences have commonly understood the concept of collective memory as a mere metaphor, as something not existing in itself as memory but useful only as a tool for referring to the way groups construct shared representations of their past. Few have however addressed the question of whether it is a metaphor or literal in its own right. This paper looks at the plausibility of the claim that collective memory is a mere metaphor by probing its presuppositions, where the representationalist theory of mind emerges as the ground for such a claim. Then appealing to the externalist model of the mind championed in recent studies of mind in disciplines as varied as philosophy, cognitive science, evolutionary biology, and collective intentionality studies, we try to expose the presuppositions of that claim, opening up possibilities for conceiving collective memory as not merely metaphorical but literal and naturally existing as memory. [ABSTRACT FROM AUTHOR]

Enactivism advances an understanding of cognition rooted in the dynamic interaction between an embodied agent and their environment, whilst new mechanism suggests that cognition is explained by uncovering the organised components underlying cognitive capacities. On the face of it, the mechanistic model's emphasis on localisable and decomposable mechanisms, often neural in nature, runs contrary to the enactivist ethos. Despite appearances, this paper argues that mechanistic explanations of cognition, being neither narrow nor reductive, and compatible with plausible iterations of ideas like emergence and downward causation, are congruent with enactivism. Attention to enactivist ideas, moreover, may serve as a heuristic for mechanistic investigations of cognition. Nevertheless, I show how enactivism and approaches that prioritise mechanistic modelling may diverge in starting assumptions about the nature of cognitive phenomena, such as where the constitutive boundaries of cognition lie. [ABSTRACT FROM AUTHOR]

Predictive processing (PP) and embodied cognition (EC) have emerged as two influential approaches within cognitive science in recent years. Not only have PP and EC been heralded as "revolutions" and "paradigm shifts" but they have motivated a number of new and interesting areas of research. This has prompted some to wonder how compatible the two views might be. This paper looks to weigh in on the issue of PP-EC compatibility. After outlining two recent proposals, I argue that further clarity can be achieved on the issue by considering a model of scientific progress. Specifically, I suggest that Larry Laudan's "problem solving model" can provide important insights into a number of outstanding challenges that face existing accounts of PP-EC compatibility. I conclude by outlining additional implications of the problem solving model for PP and EC more generally. [ABSTRACT FROM AUTHOR]

About three decades ago, AI theory underwent a sharp turn as a consequence of criticism that pointed out the problem of externalism in the cognitivist position. Hubert Dreyfus, undoubtedly the main exponent of this criticism, opened the possibility of a Heideggerian reading using the frame problem to bring to light obscurities that otherwise would have been very difficult to detect. However, the question still remains of whether or not Heidegger's philosophy can serve as the source of a positive contribution to AI. In this paper, we argue that in the small measure in which such a task has been attempted, its orientation has been hampered by the omission of what, for Heidegger, was the central issue to be pondered: the question for being and the ontological difference. To propose a possible direction in which AI can be headed as a consequence of this novel perspective, we undertake a brief and schematic review of two published projects suggesting that they both manage to avoid the frame problem and also bear this Heideggerian outlook. [ABSTRACT FROM AUTHOR]

Progress in psychology and the cognitive sciences is often taken to vindicate physicalism and cast doubt on such extravagant metaphysical theses as dualism and idealism. The goal of this paper is to argue that cognitive science has no such implications—rather, evidence from cognitive science is largely (but not wholly) irrelevant to the mind-body problem. Our argument begins with the observation that data from cognitive science can be modeled by supervenience relations. We then show that supervenience relations are neutral, by showing how they can be coherently interpreted in physicalist, idealist, and dualist terms. We distinguish several types of supervenience relation, and show that each coheres better with some positions on the mind-body problem than the other. Since these variants of supervenience are not empirically equivalent, there is a possibility that data from cognitive science will end up supporting some positions on the mind-body problem more than others. It is in this sense that cognitive science is mostly, but not wholly, neutral. [ABSTRACT FROM AUTHOR]

Contemporary science is a field that is becoming increasingly computational. Today's scientists not only leverage computational tools to conduct their investigations, they often must contribute to the design of the computational tools for their specific research. From a science education perspective, for students to learn authentic science practices, students must learn to use the tools of the trade. This necessity in science education has shaped recent K–12 science standards including the Next Generation Science Standards, which explicitly mention the use of computational tools and simulations. These standards, in particular, have gone further and mandated that computational thinking be taught and leveraged as a practice of science. While computational thinking is not a new term, its inclusion in K–12 science standards has led to confusion about what the term means in the context of science learning and to questions about how to differentiate computational thinking from other commonly taught cognitive skills in science like problem-solving, mathematical reasoning, and critical thinking. In this paper, we propose a definition of computational thinking for science (CT-S) and a framework for its operationalization in K–12 science education. We situate our definition and framework in Activity Theory, from the learning sciences, in order to position computational thinking as an input to and outcome of science learning that is mediated by computational tools. [ABSTRACT FROM AUTHOR]

Creative works are always multifaceted and complex; therefore, I argue that literature specialists need to recognize how creativity develops within the literary domain and what criteria we can set as standards for the appreciation of literary creativity. In this paper, I attempt to provide a cognitive theoretical account of the factors or dynamics that underlie the creative literary production. This study has two major purposes. First, the paper explains specific, basic processes of literary creativity and reveals how they function in the composition and comprehension of creative literary works, demonstrating their applicability to both acclaimed canons and works of contemporary writers. Second, it draws on cognitive theories in order to emphasize the cognitive and developmental benefit of the conscious awareness, practice and application of the cognitive processes underlying literary creativity. The selected creative processes are fundamental to the composition of literary works; therefore, they are the most commonly used and recurrent in acknowledged literary works. They include retrieval of knowledge, conceptual integration, compression, ambiguity, association between categories, effectiveness, and problem solving. The paper also argues that these cognitive processes of literary creativity are interdependent and overlapping as the construction of one process entails the functioning of another. [ABSTRACT FROM AUTHOR]

Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie's definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine's involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning. [ABSTRACT FROM AUTHOR]

Our paper contributes to the rapidly expanding body of experimental research on transformative decision making, and in the process, marks out a novel empirical interpretation for assessments of subjective value in transformative contexts. We start with a discussion of the role of subjective value in transformative decisions, and then critique extant experimental work that explores this role, with special attention to Reuter and Messerli (2018). We argue that current empirical treatments miss a crucial feature of practical deliberation manifesting across a variety of everyday decisions: often, people attach more weight to decision criteria that they can know—a phenomenon known as the “evaluability bias.” In transformative contexts, if people cannot know the subjective value of an option, they are unlikely to attach it much weight. Despite this, people may care very much about such value. We then use this point to develop and present new empirical results that, in addition to supporting our concerns about evaluability bias, support the hypothesis that people care about subjective value. Our work enriches the current philosophical understanding of transformative decisions and helps to frame the emerging experimental paradigm for the empirical dimensions of the debate. [ABSTRACT FROM AUTHOR]

This paper introduces an original concept (projective anthropomorphism) towards exploring a psychological dimension that is irreducible to the forms of anthropomorphism investigated in both cognitive science and social robotics. Projective anthropomorphism is an unconscious bias towards anticipating humanlike characteristics in robots. An overview of the variety of ways in which projection has been conceptualised in psychology and psychoanalysis is provided before discussing implications for theorising projective anthropomorphism. The proposed concept alludes to the projection of existential anxieties and desires onto myths, legends, linguistic tropes, and science-fiction motifs of humanoid automata. Such motifs and their associated narratives populate contemporary popular culture, and feed into social representations of robots. The importance of considering projective anthropomorphism lies in the extent to which its phenomena channel people's expectations and attitudes towards technological artefacts, as well as steering technological possibilities. [ABSTRACT FROM AUTHOR]

Personality is generally expressed as the combination of behavior, emotion, motivation, and thoughts that aim to describe different aspects of human behavior based on a few stable and measurable characteristics. Considering the fact that our personality has a remarkable impact in our daily life, automatic recognition of a person's personality attributes can provide many essential practical applications in various aspects of cognitive science. Although various methods have been recently proposed for the task of personality recognition, most of them have mainly focused on human-designed statistical features and they did not make use of rich semantic information existing in users' generated texts while not only these contents can specify its writer's internal thought and emotion but also can be assumed as the most direct way for people to state their feeling and opinion in an understandable form. In order to make use of contextualized information as well as overcoming the complexity and handcraft feature requirement of previous methods, a deep learning based method for the task of contextualized personality recognition is proposed in this paper. Among various deep neural networks, Convolutional Neural Networks (CNN) have demonstrated profound efficiency in natural language processing and especially personality detection. Owing to the fact that various filter sizes in CNN may influence its performance, we decided to combine CNN with AdaBoost, a classical ensemble algorithm, to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter size using AdaBoost. Our proposed method was validated on the Essay and YouTube datasets by conducting a series of experiments and the empirical results demonstrated the superiority of our proposed method on both datasets compared to both machine learning and deep learning methods for the task of personality recognition. [ABSTRACT FROM AUTHOR]