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The paper presents an analysis of BSS coloring scheme defined in IEEE 802.11ax standard. The efficiency of dense networks for different scenarios was analyzed and compared. This analysis covers various topologies and work configurations through the use of multiple parameters of the PHY and MAC layers. A positive impact of the coloring mechanism on the QoS was observed. The study also analyzed the impact of the RTS/CTS mechanism on the obtained network performance and adequate prioritization of various traffic classes. It was shown that the proper selection of the coloring mechanism parameters in the IEEE 802.11ax standard has a strong impact on QoS and the performance of dense networks. [ABSTRACT FROM AUTHOR]

The last two decades have produced unprecedented successes in the fields of artificial intelligence and machine learning (ML), due almost entirely to advances in deep neural networks (DNNs). Deep hierarchical memory networks are not a novel concept in cognitive science and can be traced back more than a half century to Simon's early work on discrimination nets for simulating human expertise. The major difference between DNNs and the deep memory nets meant for explaining human cognition is that the latter are symbolic networks meant to model the dynamics of human memory and learning. Cognition‐inspired symbolic deep networks (SDNs) address several known issues with DNNs, including (1) learning efficiency, where a much larger number of training examples are required for DNNs than would be expected for a human; (2) catastrophic interference, where what is learned by a DNN gets unlearned when a new problem is presented; and (3) explainability, where there is no way to explain what is learned by a DNN. This paper explores whether SDNs can achieve similar classification accuracy performance to DNNs across several popular ML datasets and discusses the strengths and weaknesses of each approach. Simulations reveal that (1) SDNs provide similar accuracy to DNNs in most cases, (2) SDNs are far more efficient than DNNs, (3) SDNs are as robust as DNNs to irrelevant/noisy attributes in the data, and (4) SDNs are far more robust to catastrophic interference than DNNs. We conclude that SDNs offer a promising path toward human‐level accuracy and efficiency in category learning. More generally, ML frameworks could stand to benefit from cognitively inspired approaches, borrowing more features and functionality from models meant to simulate and explain human learning. Deep Neural Networks (DNNs) are popular for classifying large noisy analogue data. However, DNNs suffer from several known issues, including explainability, efficiency, catastrophic interference, and a need for high‐end computational resources. Our simulations reveal that psychologically‐inspired symbolic deep networks (SDNs) achieve similar accuracy and robustness to noise as DNNs on common ML problem sets, while addressing these issues. [ABSTRACT FROM AUTHOR]

In the past two decades, human categorisation research has achieved significant progress via the rigorous and systematic study of concepts in terms of category structures and their families. The importance of these structure families stems from evidence suggesting that learning and categorisation performance are not only limited by low- and high-level generalisation mechanisms but by the inherent nature of the environmental and mental stimuli entertained by observers during the concept learning process. In this paper, we propose a new direction for concept learning and categorisation research based on several dual paradigmatic tensions that hinge on the inherent nature of the components of stimuli, limitations of the innate abilities of the observer to process such components, and the relationship between the two. The tensions range from the various possible properties and constraints of the dimensions underlying categories of object stimuli to various notions of supervised learning capable of significantly altering concept learnability. The substantial extant literature on concept learning research indicates that rigorous empirical investigations targeting these tensions are either non-existent or, at best, severely lacking despite their ecological significance. We shall argue that future theory building about concept learning should attempt to resolve these tensions and that without the proper empirical and theoretical focus on them, concept learning research will fail to achieve its ultimate goals anytime soon. [ABSTRACT FROM AUTHOR]

This paper is extracted from the research "Relationship between executive functions and social cognition with job performance in the Corporación Universitaria Minuto de Dios, seccional Bello" carried out in 2019. Social cognition as a fundamental part of higher cognitive processes and Person - Environment interaction is presented as a leading actor at work and in its manifestations, that is, the level of work performance is closely linked to social interaction skills. The article works with a population made up of university professors from the city of Medellín (Colombia) whose performance is variable but within the criteria accepted by that educational institution and who, in addition, do not present emotional alterations or mental illnesses categorized in the DSM -V. A series of tests are applied to this population that are detailed throughout the writing and a direct relationship between social cognition and performance is evidenced. This investigative work promises a great contribution to the improvement, not only of the performance of the teachers, but also of the quality of life of them and their families and the positioning and continuous improvement of the IES (Institutions of Higher Education). [ABSTRACT FROM AUTHOR]

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

This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial intelligence and are of growing importance in cognitive science and neuroscience. They have had many successes but also have several limitations and there is limited understanding of their inner workings. At present Deep Nets perform very well on specific visual tasks with benchmark datasets but they are much less general purpose, flexible, and adaptive than the human visual system. We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. We argue that this combinatorial explosion takes us into a regime where "big data is not enough" and where we need to rethink our methods for benchmarking performance and evaluating vision algorithms. We stress that, as vision algorithms are increasingly used in real world applications, that performance evaluation is not merely an academic exercise but has important consequences in the real world. It is impractical to review the entire Deep Net literature so we restrict ourselves to a limited range of topics and references which are intended as entry points into the literature. The views expressed in this paper are our own and do not necessarily represent those of anybody else in the computer vision community. [ABSTRACT FROM AUTHOR]

The success of current deep saliency models heavily depends on large amounts of annotated human fixation data to fit the highly non-linear mapping between the stimuli and visual saliency. Such fully supervised data-driven approaches are annotation-intensive and often fail to consider the underlying mechanisms of visual attention. In contrast, in this paper, we introduce a model based on various cognitive theories of visual saliency, which learns visual attention patterns in a weakly supervised manner. Our approach incorporates insights from cognitive science as differentiable submodules, resulting in a unified, end-to-end trainable framework. Specifically, our model encapsulates the following important components motivated from biological vision. (a) As scene semantics are closely related to visually attentive regions, our model encodes discriminative spatial information for scene understanding through spatial visual semantics embedding. (b) To model the objectness factors in visual attention deployment, we incorporate object-level semantics embedding and object relation information. (c) Considering the “winner-take-all” mechanism in visual stimuli processing, we model the competition mechanism among objects with softmax based neural attention. (d) Lastly, a conditional center prior is learned to mimic the spatial distribution bias of visual attention. Furthermore, we propose novel loss functions to utilize supervision cues from image-level semantics, saliency prior knowledge, and self-information compression. Experiments show that our method achieves promising results, and even outperforms many of its fully supervised counterparts. Overall, our weakly supervised saliency method makes an essential step towards reducing the annotation budget of current approaches, as well as providing a more comprehensive understanding of the visual attention mechanism. Our code is available at: https://github.com/ashleylqx/WeakFixation.git. [ABSTRACT FROM AUTHOR]

There are few academics today who actively argue against demonic realism. Much of this is perhaps due to the fact that there are comparably few defenders of such. This has created a vacuum for critics to comfortably object to the existence of demons without sophistication (for it is only in the professional exchange of ideas do bad arguments get weeded out and good arguments gain vitality). Add to this the common perception of demonology as an anti-intellectual superstition and we end up with a threshold for the success of anti-realist arguments to be set quite low. In this paper, I shall survey three of the most familiar objections to demonic realism to arise out of this skeptical intellectual environment: First, and most ambitiously, there is the impossibility of justified belief objection that proffers that belief in demons cannot even in principle be justified no matter how much (scientific) evidence there is. Alternative explanations are always to be preferred. Second, there is the demon-of-the-gaps objection (or category of objections) which insists that demonic realism is hastily posited as a pre-scientific explanation for physical, medical, and psychological mysteries. Third, there is what I call the ethical argument from scapegoating that questions the existence of demons on grounds that, if they in fact exist, such a fact would preclude moral responsibility and the possibility of retributive justice since we could never know if a bad actor was himself morally culpable for his own evils or if he was under the coercive influence of demonic agents. I argue that, despite their rhetorical appeal and kinship with the anti-supernatural sentiments of many academics today, these three arguments are not successful, for these are either based on egregious philosophical assumptions or assumptions about demonology few if any adopt. [ABSTRACT FROM AUTHOR]

Cognitive science is unusual in that cognitive scientists have dramatic disagreements about the extension of their object of study, cognition. This paper defends a novel analysis of the scientific concept of cognition: that cognition is the sensitive management of an agent's behavior. This analysis is "modular," so that its extension varies depending on how one interprets certain of its constituent terms. I argue that these variations correspond to extant disagreements between cognitive scientists. This correspondence is evidence that the proposed analysis models the contemporary understanding of cognition among scientists, without artificially resolving questions that are currently considered open. [ABSTRACT FROM AUTHOR]

This paper re-engages with the science versus religion debate in the light of the last three decades of scholarly progress in the study of religion coming from the cognitive and evolutionary sciences of religion. I begin by asking (1) 'if it is indeed possible to account for the origins of religious belief, its cultural transmission and evolution, and its maintenance— without any appeal to the real existence of any supernatural deity—then are we in a position to say we have explained it all away?' If we are, how does this affect central themes in the philosophy of religion about God's existence? In (2) I provide an outline of the cognitive and evolutionary approach to explaining religion. In section (3) I discuss abductive reasoning (3.1), problems with the accusation of having committed the genetic fallacy when answering (1) in favour of positive atheism (3.2), the need for first-order justifications of religious belief (3.3), and in (3.4) a peculiar divine attribute (i.e. omnipotent potential in a causal chain of events) that appears to immunise religious belief in a particular kind of supernatural deity from having been explained away. [ABSTRACT FROM AUTHOR]

Speculative literature is an excellent vehicle for questioning the seemingly natural; faith and the shape of reality are two frequently reflected themes in such works. In The Divine Cities trilogy by Robert Jackson Bennett, the relationships among deities, known as the Divinities; people; and reality are complex and thought provoking. The fictional world hints significantly at our reality when inverting the assumingly top-down relationship between the divine and the mundane. Based on findings from the cognitive sciences and working with the concept of intersubjectivity and interaction theory, this paper explores the enormous role of intersubjective minds in constructing reality in both the fictional and the real world. [ABSTRACT FROM AUTHOR]

Recent advances in the field of cognitive sciences have provided evidence to classify activities with different cognitive engagement levels. High cognitive load and sustained attention may predispose an individual to psychological and physical stress and may result in a lack of performance. Cognitive load monitoring of professionals in high precision fields has gathered significant attention in the current research scenario. The proposed work in continuity has tried to demonstrate the use of two non‐invasive physiological sensing modalities viz. galvanic skin response (GSR) and photoplethysmography (PPG) to elucidate effectiveness in picking up differences of two induced cognitive state categories based on machine learning and statistical methods. This paper demonstrates how GSR and PPG signals data can be effectively utilized to examine the shift in cognitive load. The present work proposes the use of general linear chirplet transform (GLCT) to evaluate the time‐frequency characteristic of GSR and PPG signals and utilize the statistical features in classification. Random Forest, Decision Tree, and k‐Nearest Neighbours demonstrated an accuracy of 92.13%, 88.0%, and 86.13% respectively on a dataset of 20 subjects against an optimized feature set, thus demonstrating the effectiveness of the proposed methodology for differentiating pre‐defined categories of cognitive load. The study shows the potential of GSR and PPG signals attributes and time‐frequency representation using GLCT to monitor cognitive load in real‐life conditions. The proposed work also indicates the possibility to extend the same to attention‐demanding fields such as aviation, medicine, and manufacturing for effective remediation of fatigue periods with increased cognitive demand, which, if sustained, may lead to cognitive decline in the long run. [ABSTRACT FROM AUTHOR]

In the work setting of deep learning, most of the neural networks employed for visual object detection in recent years are based on bounding box regression. The performance of active detectors through multi-step decision-making is limited by the rough model design. However, from the perspective of cognitive science, the recognition in the human visual system is a decision process from coarse to fine. Based on the theory of "see the forest first, then the trees", this paper proposes a dynamic coarse-to-fine gaze strategy for active object detection, named AHDet, which takes the key points as the realization carrier of the coarse-to-fine concept. The detection process is divided into two steps, AIM and HIT. In the step of AIM, the positioning and prior bounding boxes for objects are given by detecting the center points, referring to the first glance. In the step of HIT, bounding boxes are dynamically adjusted to obtain compact bounding boxes with the help of the corner points, referring to the careful observation. With the design of the two-step coarse-to-fine gaze process, AHDet outperforms traditional approaches. A series of experiments performed on MS-COCO and PASCAL VOC dataset demonstrate the advantages of AHDet. [ABSTRACT FROM AUTHOR]

This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems (KBIS) design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and concept learning, with their theoretical and experimental rigor, can best characterize the problem-solving capabilities of humans and can best overcome five key limitations currently exhibited by knowledge-based intelligent systems. We begin with a brief survey of the relevant work related to KBIS design and then discuss these five shortcomings with new suggestions for how to integrate results from cognitive science to resolve each of them. Our ultimate goal is to increase awareness and direct attention to areas of theoretical and experimental cognitive research that are fundamentally relevant to the goals underlying KBISes. [ABSTRACT FROM AUTHOR]

Hypothesis testing is a central statistical method in psychology and the cognitive sciences. However, the problems of null hypothesis significance testing (NHST) and p values have been debated widely, but few attractive alternatives exist. This article introduces the fbst R package, which implements the Full Bayesian Significance Test (FBST) to test a sharp null hypothesis against its alternative via the e value. The statistical theory of the FBST has been introduced more than two decades ago and since then the FBST has shown to be a Bayesian alternative to NHST and p values with both theoretical and practical highly appealing properties. The algorithm provided in the fbst package is applicable to any Bayesian model as long as the posterior distribution can be obtained at least numerically. The core function of the package provides the Bayesian evidence against the null hypothesis, the e value. Additionally, p values based on asymptotic arguments can be computed and rich visualizations for communication and interpretation of the results can be produced. Three examples of frequently used statistical procedures in the cognitive sciences are given in this paper, which demonstrate how to apply the FBST in practice using the fbst package. Based on the success of the FBST in statistical science, the fbst package should be of interest to a broad range of researchers and hopefully will encourage researchers to consider the FBST as a possible alternative when conducting hypothesis tests of a sharp null hypothesis. [ABSTRACT FROM AUTHOR]

The critical question for cognitive scientists is what does cognitive science do, if anything, for people? Cognitive science is primarily concerned with human cognition but has fallen short in continuously and critically assessing the who in human cognition. This complacency in a world where white supremacist and patriarchal structures leave cognitive science in the unfortunate position of potentially supporting those structures. We take it that many cognitive scientists operate on the assumption that the study of human cognition is both interesting and important. We want to invoke that importance to note that cognitive scientists must continue to work to show how the field is useful to all of humanity and reflects a humanity that is not white by default. We wonder how much the field has done, and can do, to show that it is useful not only in the sense that we might make connections with researchers in other fields, win grants and write papers, even of the highest quality, but useful in some material way to the billions of non‐cognitive scientists across the globe. [ABSTRACT FROM AUTHOR]

Cognitive niche construction is the process whereby organisms create and maintain cause–effect models of their niche as guides for fitness influencing behavior. Extended mind theory claims that cognitive processes extend beyond the brain to include predictable states of the world. Active inference and predictive processing in cognitive science assume that organisms embody predictive (i.e., generative) models of the world optimized by standard cognitive functions (e.g., perception, action, learning). This paper presents an active inference formulation that views cognitive niche construction as a cognitive function aimed at optimizing organisms' generative models. We call that process of optimization extended active inference. [ABSTRACT FROM AUTHOR]

Holbraad and Pedersen have revisited the ontological turn, suggesting that it is strictly concerned with methodology only. Holbraad goes even further, accepting an aesthetic criterion for ethnography only. This is a sign of theoretical decline. In my paper, I claim that ontologists' tendency to overestimate the significance of ethnographic experience causes theoretical confusion. I claim that neo-pragmatic analysis can eliminate this confusion. I also argue that there is only one remaining issue from the ontological turn that is not entirely lost. A careful evaluation of all folk categories, with all its possible consequences, can boost the robustness of all competitive theories, Cognitive Evolutionary Science included. [ABSTRACT FROM AUTHOR]

The paper examines relationships between stormwater control measure (SCM) priorities and environmental value orientations among stormwater managers in Cleveland, Ohio and Denver, Colorado, metro regions with contrasting environmental conditions and policy contexts. While studies show that governance explains differences in broad SCM priorities, less is known about what motivates individual "street level bureaucrats" who influence decisions at the project level. Drawing from cognitive social science perspectives, this study surveyed stormwater professionals (n = 185) about primary and co‐benefit SCM priorities and environmental value orientation. Results revealed different primary SCM priorities by region: Cleveland and Denver respondents prioritized quantity and quality goals, respectively, reflecting regional context. Co‐benefit priorities correlated to two environmental value orientation clusters — "Traditional Technocrats" with relatively anthropocentric orientations and "Champions" with relatively ecocentric orientations — who were equally abundant in both regions. Findings suggest that environmental value orientation influences co‐benefit priorities, which may have implications for project level articulation of policy. Research Impact Statement: Pro‐environment stormwater managers prioritize environmental co‐benefits in projects more than traditional technocrats, with implications for the use of stormwater policy for greening urban land. [ABSTRACT FROM AUTHOR]

Much controversy has arisen in recent years about the relation between legal philosophy and cognitive science, with some authors announcing a revolution brought about in law by the advances in the scientific study of cognition. At the same time, pessimistic declarations were made, and the significance of cognitive science for law and legal philosophy was denied. In this paper I argue that representatives of legal philosophy are now facing a "tension problem" which comes as a consequence of the following beliefs: that legal philosophy should be naturalized; that contemporary cognitive science is not a source of knowledge proper, that is, a source of naturalization; and that contemporary cognitive science is the best available source of knowledge about cognitive mechanisms. To illustrate the significance of the problem, a case study is presented that concerns research devoted to abstract concepts and its significance for the issues analyzed by legal philosophers, such as legal concepts and legal cognition. More general remarks about the manner of naturalizing jurisprudence are also presented. [ABSTRACT FROM AUTHOR]

Deity yoga is a practice found in Tibetan Buddhism involving visualizations that have the normative goal of "becoming one" with a supernatural being. During the practice, practitioners report experiencing that their own body transforms into the body of the deity. This paper offers a potential cognitive explanation of how such an experience is possible. Applying findings from cognitive science on the phenomenon of illusory ownership, we argue that the practice of deity yoga has the necessary means to cause an experience analogous to the famous "rubber hand illusion" in which one misattributes their ownership to a fake hand. In this paper, we 1) introduce deity yoga practice and its key aspects; 2) discuss illusory ownership and its explanation embedded in a predictive processing framework; 3) argue that visualization in deity yoga may induce the experience of illusory ownership; and 4) conclude with a short discussion of the hypothesis' limitations and of ways to test our hypothesis. Overall, the paper suggests how the practice of visualization in deity yoga may lead to an experience of a transfer of identity onto an imagined supernatural agent. [ABSTRACT FROM AUTHOR]

Modelling human function learning has been the subject of intense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper, we focus on the relation between the behaviour of humans searching for the maximum and the probabilistic model used in Bayesian optimization. As surrogate models of the unknown function, both Gaussian processes and random forest have been considered: the Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper, we analyse experimentally how Bayesian optimization compares to humans searching for the maximum of an unknown 2D function. A set of controlled experiments with 60 subjects, using both surrogate models, confirm that Bayesian optimization provides a general model to represent individual patterns of active learning in humans. [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 Cognitive Science of Nationalistic Behavior (CSNB), presented in this paper, integrates the political sciences of nationalities as invented communities with an evolutionary cognitive analysis of social forms as products of the human mind. The framework is modeled after the Cognitive Science of Religion, where decades of cross-disciplinary work has generated standards, predictions, and data about the role of individual cognitive tendencies in shaping societies. We study the nationalistic calendar as a cultural attractor and draw on cue-based behavioral motivation and differential autobiographical memory systems to explain its appeal to the human mind. Calendrical elements are analyzed in the context of essentialist thought patterns and action representation systems. We conclude with the implications of calendrical thinking on the control of elites who aim to forge and reinforce national identities. This paper lays the groundwork for applying a similar approach to the study of other nationalistic elements. [ABSTRACT FROM AUTHOR]

Debate about cognitive science explanations has been formulated in terms of identifying the proper level(s) of explanation. Views range from reductionist, favoring only neuroscience explanations, to mechanist, favoring the integration of multiple levels, to pluralist, favoring the preservation of even the most general, high‐level explanations, such as those provided by embodied or dynamical approaches. In this paper, we challenge this framing. We suggest that these are not different levels of explanation at all but, rather, different styles of explanation that capture different, cross‐cutting patterns in cognitive phenomena. Which pattern is explanatory depends on both the cognitive phenomenon under investigation and the research interests occasioning the explanation. This reframing changes how we should answer the basic questions of which cognitive science approaches explain and how these explanations relate to one another. On this view, we should expect different approaches to offer independent explanations in terms of their different focal patterns and the value of those explanations to partly derive from the broad patterns they feature. Patterns in cognitive phenomena and pluralism of explanatory styles This paper focuses on three general approaches to explanation in cognitive science, namely: reductionist, mechanist and pluralist approaches to explanation. On the basis of various case studies from cognitive psychology, Potochnik and Sanches De Oliveira argue that these three explanatory approaches are best understood as different styles of explanation. Each of these styles would be best suited to explain different, cross‐cutting patterns in cognitive phenomena. [ABSTRACT FROM AUTHOR]

What would it mean to explain the mind in neural terms? Neural accounts of the mind are often sought in a reductionistic spirit in which neural mechanisms explain cognition. Because an individual's thoughts and behaviors are not reproducible without careful control of task, stimulus, and behavioral history, laws of the mind are the currency of psychology. Reduction may thus have to take the form familiar from physics: deriving macroscopic laws from microscopic laws. I argue that the metaphor of reduction from non‐equilibrium physics may be the most appropriate. Macroscopic patterns of neural activity, which cause behavior and thought, are slow dynamical variables that dominate the fast microscopic dynamics of individual neurons and synapses. I outline a theoretical framework in which strongly recurrent neural networks, described by neural dynamics, generate neural representations as attractor states that are embedded in low‐dimensional feature spaces. Instabilities of these states are instrumental in decision‐making and the generation of sequences of mental states that are the basis for higher cognition. Networks of such neural population dynamics form neural cognitive architectures that capture the laws of the mind. The dynamics of neural populations capture the laws of the mind This paper focuses on the level of neural networks. Examining the case of recurrent neural networks, the paper argues that the dynamics of neural populations form a privileged level of explanation in cognitive science. According to Schöner, this level is privileged, because it enables cognitive scientists to discover the laws governing organisms' cognition and behaviour. [ABSTRACT FROM AUTHOR]

Around the world, a growing interest has been seen in learner translator corpora, which are invaluable resources for teaching and research. This paper introduces a new resource to support researchers from different interdisciplinary areas such as computational linguistics, descriptive translation studies, computer-aided translation technology, Arabic machine translation applications, cognitive science, and translation pedagogy. Motivated by the lack of learner translator resources that provide data about learners of translation from and into Arabic, the undergraduate learner translator corpus (ULTC) is an ongoing, error-tagged sentence-aligned parallel corpus of English, Arabic, and French, with Arabic as its main language. The present corpus, consisting of parallel texts of female learners of translation from English or French into Arabic, is the first of its kind in terms of the languages represented, tasks covered, and number of students involved. It is also unique in terms of combining many complementary corpora of cross-lingual data, each of which has its own web-based query interface and corpus analysis tools. This paper describes the ULTC compilation process, preliminary findings, and planned future expansion and research. [ABSTRACT FROM AUTHOR]

In recent years philosophers have used results from cognitive science to formulate epistemologies of arithmetic (e.g. Giaquinto in J Philos 98(1):5–18, 2001). Such epistemologies have, however, been criticised, e.g. by Azzouni (Talking about nothing: numbers, hallucinations and fictions, Oxford University Press, 2010), for interpreting the capacities found by cognitive science in an overly numerical way. I offer an alternative framework for the way these psychological processes can be combined, forming the basis for an epistemology for arithmetic. The resulting framework avoids assigning numerical content to the Approximate Number System and Object Tracking System, two systems that have so far been the basis of epistemologies of arithmetic informed by cognitive science. The resulting account is, however, only a framework for an epistemology: in the final part of the paper I argue that it is compatible with both platonist and nominalist views of numbers by fitting it into an epistemology for ante rem structuralism and one for fictionalism. Unsurprisingly, cognitive science does not settle the debate between these positions in the philosophy of mathematics, but I it can be used to refine existing epistemologies and restrict our focus to the capacities that cognitive science has found to underly our mathematical knowledge. [ABSTRACT FROM AUTHOR]

Due to the rapid growth of textual information on the web, analyzing users' opinions about particular products, events or services is now considered a crucial and challenging task that has changed sentiment analysis from an academic endeavor to an essential analytic tool in cognitive science and natural language understanding. Despite the remarkable success of deep learning models for textual sentiment classification, they are still confronted with some limitations. Convolutional neural network is one of the deep learning models that has been excelled at sentiment classification but tends to need a large amount of training data while it considers that all words in a sentence have equal contribution in the polarity of a sentence and its performance is highly dependent on its accompanying hyper-parameters. To overcome these issues, an Attention-Based Convolutional Neural Network with Transfer Learning (ACNN-TL) is proposed in this paper that not only tries to take advantage of both attention mechanism and transfer learning to boost the performance of sentiment classification but also language models, namely Word2Vec and BERT, are used as its the backbone to better express sentence semantics as word vector. We conducted our experiment on widely-studied sentiment classification datasets and according to the empirical results, not only the proposed ACNN-TL achieved comparable or even better classification results but also employing contextual representation and transfer learning yielded remarkable improvement in the classification accuracy. [ABSTRACT FROM AUTHOR]

The traditional view of hell as eternal conscious torment is challenged by proponents of universalism and conditional immortality. However, they need to explain why the church has been misled in adopting the traditional view. This paper draws from cognitive and evolutionary science of religion to provide an "error theory" of why eternal hell became the dominant view. Early Christianity grew rapidly despite persecution and marginalization. The fear of hell probably helped Christian communities to maintain cooperation by weeding out free riding even in times of crisis. Here the traditional view proved to be more effective than its competitors. [ABSTRACT FROM AUTHOR]

The target paper of Gawronski, Ledgerwood, and Eastwick (this issue) provides a valuable contribution to the literature on implicit bias (IB). Although it is thus understandable that current examples of BIM are relatively poor lab models of IB in the real world, in principle, more useful lab models of IB could be developed in the future. A second potential reason to cherish BIM as instances of IB in the laboratory is that BIM can provide a lab model of IB. Just like different instances of IB outside of the laboratory can differ with regard to the way in which conditions are suboptimal, also different instances of IB in the laboratory (e.g., BIM) can differ with regard to how conditions are suboptimal. [Extracted from the article]

Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system. [ABSTRACT FROM AUTHOR]

This paper discusses the resources for deconstruction offered by cognitive science, drawing inspiration from David Gunkel's work on the topic (Deconstruction, MIT Press 2021). The gesture of deconstruction is seen as having a positive impact on the development of this interdisciplinary field by challenging misleading dichotomies and examining its underlying assumptions, such as the symmetry of integration. [ABSTRACT FROM AUTHOR]

The reality of the aesthetic seems to manifest itself more and more in relational and immersive ways that defy analyses that follow the trail of the modern tradition of philosophy, based on the dual gnoseological relationship between subject and object. Even some areas of the new cognitive sciences seem to converge towards a conception of experience as a complex horizon in which variously related vectors operate. From this point of view, it is worth exploring the notion of "field" as a conceptual tool to describe the aesthetic. In this paper we will consider two possible uses of this notion in reference to the aesthetic: to describe experiential modes (following Arnold Berleant), and to describe social dynamics (following Pierre Bourdieu). Yet, the starting point will be some considerations provided by Peter Abbs. We will thus try to show how the notion of "aesthetic field" can be consonant with scientific settings that advocate models of mind that stress its being extended and situated. A particular test bed will be the psychology of art as a discipline spanning philosophical knowledge and empirical investigation. In this key will also be considered the socalled "experiential revolution" in psychology, which indicates an extra-cognitive horizon variously coinciding with the perspective of an aesthetic research focused on the conception of aisthesis as a system of practices of perception, emotion, and expression. According to this conception, the dynamics within the aesthetic field, such as those related to the nexus between perceptual contents and aesthetic properties, or between emotional content and the practices of sensing could prove to be dynamics of "intervenience," rather than of supervenience. [ABSTRACT FROM AUTHOR]

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. Cognitive Science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, "grounded", perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in Cognitive Science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building "grounded" linguistic intelligence. These components include embodying machines in a perception–action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world. [ABSTRACT FROM AUTHOR]

The semantic method of extension and intension presented by Rudolf Carnap may have some problems from the current perspective in cognitive science. For example, that method is strongly linked to logic, and it is hard to determine where its state-descriptions come from, how many they are, and what the relation of accessibility between them is. This paper tries to solve difficulties such as those by updating the method resorting to the theses and results of a contemporary reasoning theory: the theory of mental models. Basically, the update consists of replacing the state-descriptions of the original method with the possibilities of the theory of mental models. That allows reformulating several relevant concepts in Carnap's method in order to better adjust them to the real characteristics of human language. [ABSTRACT FROM AUTHOR]

In the last twenty years or so neuroscience and linguistics, language and culture have become increasingly interconnected, as we seek to understand the influence of language on behaviour and the way we think and express ourselves. Neuroscience is the study of how the brain and mind work and cognitive science applies that study to how we use language to express our thoughts and feelings and also how our mental faculties work. One of these faculties is memory. How do we recall and express what has happened in the past and even assuming we can remember it at all? The purpose of this paper is to examine how cognitive science has clarified the relationship between language and culture in particular in the way we remember things and express our recollections, using experiments based on facial recognition. In the process the paper demonstrates how language and culture influence our cultural processes and the importance of cognitive science as a part of language and cultural study. [ABSTRACT FROM AUTHOR]

Perception is a prominent subject matter in all Indian Philosophical Systems (Darśanā) as it is in many contemporary knowledge systems like Cognitive Science and Psychology. Questions such as “What is the nature of perception?”, What are the different types of perception?”, “How does it get produced?” What are the means of perception?” have been the same, even as the answers are different with respect to different disciplines. Since the topic is common to all, there is a possibility to get confused or overlap the concepts. This paper discusses perception in the framework of Advaita Vedānta, and tries to remove the possibility of confusion or overlapping other philosophical concepts from the Advaitic perspective. Advaita Vedānta has set a new dimension with regard to the nature of perception which is uncommon to other Darśanā-s. This system has basically divided all perceptions into two categories - Conventional Perception and Actual Perception whereas other prominent philosophical systems like Nyāya, do not have this distinction. In view of this distinction it becomes very necessary to discuss the properties of both categories to avoid the ambiguity. This paper predominantly discusses the various aspects of visual perception from an advaita perspective. There is an emerging idea in cognitive science about nonveridical nature of visual perception which advaita also claims about all conventional perception. Implications for future theory and research are discussed. [ABSTRACT FROM AUTHOR]

Modelling the structure of cognitive systems is a central goal of the cognitive sciences-a goal that has greatly benefitted from the application of network science approaches. This paper provides an overview of how network science has been applied to the cognitive sciences, with a specific focus on the two research 'spirals' of cognitive sciences related to the representation and processes of the human mind. For each spiral, we first review classic papers in the psychological sciences that have drawn on graphtheoretic ideas or frameworks before the advent of modern network science approaches. We then discuss how current research in these areas has been shaped by modern network science, which provides the mathematical framework and methodological tools for psychologists to (i) represent cognitive network structure and (ii) investigate and model the psychological processes that occur in these cognitive networks. Finally, we briefly comment on the future of, and the challenges facing, cognitive network science. [ABSTRACT FROM AUTHOR]

The papers in this issue have specific and focused targets that are essential for making incremental progress on the very difficult problem of identifying the coevolutionary interactions of cognition and culture. The purpose of this paper is to discern the shape of a few of the large problems that loom over these more narrowly focussed papers, and to explain and assess the ways these papers contribute to their solution. The background problems described are (a) the character of the selective interactions between the evolution of culture and of cognition; (b) the special features of cumulative cultural evolution; and (c) the place of language in an account of cognition–culture coevolution. The paper ends with some reflections on the extraordinarily difficult challenge of testing scenarios in this field. In his profound discussion, Sterelny draws out common themes in the contributions to this topic: selective drivers in the coevolution of cognition and culture, the role of language in it, characteristics of cumulative cultural evolution, and issues of testability. He highlights the growing body of evidence for positive feedback mechanisms in cultural evolution, but also notes that progress is piecemeal, calling for more cross‐border work between cognitive science and research on cultural evolution. [ABSTRACT FROM AUTHOR]

The paper analyses a contemporary Georgian novel - Zura Jishkariani's Chewing Dawns: Sugar-free. The novel belongs to the sub-genre of bio-punk. The aim of the paper is to identify the defamiliarized and ironized socio-cultural processes taking place in the contemporary Georgian society, considering the narratological concept of alternative worlds and the theoretical framework of conceptual metaphor. The outcomes of the research draw the cultural-intellectual orientations of contemporary Georgian society. Based on these two conclusions, the paper aims to find an age-long similarity between the socialpolitical challenges of the 1920s and the contemporary problems of the Georgian society. Research has proved that numerous systems of values have been deconstructed and carnivalized by means of a play with alternative worlds. The development of the world depends on the activation of the human brain capacity, which ensures the cognition of the "higher reality". The literary text under analysis reflects current achievements in cognitive sciences. The mental trips reflect the capacity of the human brain. The text describes the protagonist's aspiration towards manipulating and stimulating the human brain. This is the only way to overcome the banality of life. The manner of narration and the idealization of the aim serve the purpose of describing the revolutionary spirit. [ABSTRACT FROM AUTHOR]

The debate about scientific realism is concerned with the relation between our scientific theories and the world. Scientific realists argue that our best theories or components of those theories correspond to the world. Anti-realists deny such a correspondence. Traditionally, this central issue in the philosophy of science has been approached by focusing on the theories themselves (e.g., by looking at theory change or the underlying experimental context). I propose a relatively unexplored way to approach this old debate. In addition to focusing on the theory, we should focus on the theorizer. More precisely, in order to determine on which component of a theory we should hinge a realist commitment, we should analyze the cognitive processes underlying scientific theorizing. In this paper I do just that. Drawing from recent developments in the cognitive sciences and evolutionary epistemology, I formulate some tentative conclusions. The aim of this paper is not so much to defend a particular position in the debate on scientific realism but to showcase the value of taking a cognitive perspective in the debate. [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 this paper, we reexamined the typical analysis methods of a visuomotor sequence learning task, namely the ASRT task (J. H. Howard & Howard, 1997). We pointed out that the current analysis of data could be improved by paying more attention to pre-existing biases (i.e. by eliminating artifacts by using new filters) and by introducing a new data grouping that is more in line with the task’s inherent statistical structure. These suggestions result in more types of learning scores that can be quantified and also in purer measures. Importantly, the filtering method proposed in this paper also results in higher individual variability, possibly indicating that it had been masked previously with the usual methods. The implications of our findings relate to other sequence learning tasks as well, and opens up opportunities to study different types of implicit learning phenomena. [ABSTRACT FROM AUTHOR]

Personal motivation is a key factor in the service of foster care, impending both on the welfare of the child and on the satisfaction of the carer. This paper explores the benefits, challenges and dilemmas involved in the job of professional (i.e. state-supported) foster carer in Romania–a country where the issue of child protection has drawn a great deal of international attention over the last thirty years. The principal hypothesis concerns whether the benefits, challenges and dilemmas identified by foster carers are influenced by the factors that led to their taking up this kind of work. Quantitative research was conducted using a questionnaire as the main tool. The paper takes a descriptive, cross-sectional and multifactorial approach. Sampling was carried out by self-selecting method, and the study involved 51 participants. The research project identified a statistically significant variation in the challenges and dilemmas reported by foster carers. Thus, the results of the study show that the majority of the carers indicate a primarily intrinsic motivation for their work. By way of conclusion, it is argued that social workers, operating in collaboration with multidisciplinary teams, can offer carers support in managing more difficult periods in the child–carer relationship, thus enhancing the sustainability of the foster care service. [ABSTRACT FROM AUTHOR]

The interaction between phenotypic plasticity, e.g. learning, and evolution is an important topic both in Evolutionary Biology and Machine Learning. The evolution of learning is commonly studied in Evolutionary Biology, while the use of an evolutionary process to improve learning is of interest to the field of Machine Learning. This paper takes a different point of view by studying the effect of learning on the evolutionary process, the so-called Baldwin effect. A well-studied result in the literature about the Baldwin effect is that learning affects the speed of convergence of the evolutionary process towards some genetic configuration, which corresponds to the environment-induced plastic response. This paper demonstrates that learning can change the outcome of evolution, i.e., lead to a genetic configuration that does not correspond to the plastic response. Results are obtained both analytically and experimentally by means of an agent-based model of a foraging task, in an environment where the distribution of resources follows seasonal cycles and the foraging success on different resource types is conditioned by trade-offs that can be evolved and learned. This paper attempts to answer a question that has been overlooked: whether learning has an effect on what genotypic traits are evolved, i.e. the selection of a trait that enables a plastic response changes the selection pressure on a different trait, in what could be described as co-evolution between different traits in the same genome. [ABSTRACT FROM AUTHOR]

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

The purpose of this paper is to analyze the theoretical commitments of autopoietic enactivism in relation to Errol E Harris’s dialectical holism in the interest of establishing a common metaphysical ground. This will be undertaken in three stages. First, it is argued that Harris’s reasoning provides a means of developing enactivist ontology beyond discussions limited to cognitive science and into domains of metaphysics that have traditionally been avoided by phenomenologists. Here, I maintain enactivist commitments are consistent with Harris’s reasoning from certain synthetic a priori first principles, to his derivation of a teleological anthropic principle, which asserts the necessity of consciousness within the cosmos. Second, it is proposed that Steven Rosen’s long-standing proposal for a topology of phenomenology may provide a common logical foundation for both Harris and enactivists regarding anthropic reasoning. Third, it is argued that a pragmatic approach to process ontology is the most rigorous way of responding to the realism/anti-realism concerns that inevitably follow. If successful, this work will update Harris’s arguments with contemporary scientific and philosophical terminology and extend enactivism from philosophy of mind, into a general phenomenological ontology. [ABSTRACT FROM AUTHOR]