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In 2015, the Open Science Collaboration reported in the journal Science that a disturbingly large proportion of psychological studies cannot be replicated (Open Science Collaboration, 2015). The ensuing 'reproducibility crisis' became a lightning rod for contesting what counts as legitimate research, and for negotiating the relationship between communication infrastructures and research practice. In the psychological and cognitive sciences, the Open Science community has advocated widespread reforms to incentivize transparency, encourage replication, and detect and discourage questionable research practices. The model of 'openness' underlying mainstream Open Science centers on sharing information to increase science's self-correcting capacity. Against the backdrop of broad-scale transformations in Open Science, this case study depicts how scientists read. By examining the activity of a group of researchers 'virtually witnessing' an experiment together, this study reveals reading as a non-trivial process that matters for how research is apprehended and for how science is moved through time and space. The case complicates a disembodied, information-centric 'openness' pursued by mainstream Open Science reforms and advocates integrating situated and embodied resources into methods reforms, beginning with practices of reading. [ABSTRACT FROM AUTHOR]

Background: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. Purpose: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. Methods: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country. Results: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. Conclusions: Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices. [ABSTRACT FROM AUTHOR]

The fractal dimension of cognition refers to the idea that the cognitive processes of the human brain exhibit fractal properties. This means that certain patterns of cognitive activity, such as visual perception, memory, language, or problem-solving, can be described using the mathematical concept of fractal dimension.The idea that cognition is fractal has been proposed by some researchers as a way to understand the complex, self-similar nature of the human brain. However, it's a relatively new idea and is still under investigation, so it's not yet clear to what extent cognitive processes exhibit fractal properties or what implications this might have for our understanding of the brain and clinical practice. Indeed, the mission of the "fractal neuroscience" field is to define the characteristics of fractality in human cognition in order to differently characterize the emergence of brain disorders., (© 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

People's cooperativeness depends on many factors, such as their motives, cognition, experiences, and the situation they are in. To date, it is unclear how these factors interact and shape the decision to cooperate. We present a computational account of cooperation that not only provides insights for the design of effective incentive structures but also redefines neglected social-cognitive characteristics associated with attention-deficit hyperactivity disorder (ADHD). Leveraging game theory, we demonstrate that the source and magnitude of conflict between different motives affected the speed and frequency of cooperation. Integrating eye-tracking to measure motivation-based information processing during decision-making shows that participants' visual fixations on the gains of cooperation rather than its costs and risks predicted their cooperativeness on a trial-by-trial basis. Using Bayesian hierarchical modeling, we find that a situation's prosociality and participants' past experience each bias the decision-making process distinctively. ADHD characteristics explain individual differences in responsiveness across contexts, highlighting the clinical importance of experimentally studying reactivity in social interactions. We demonstrate how the use of eye-tracking and computational modeling can be used to experimentally investigate social-cognitive characteristics in clinical populations. We also discuss possible underlying neural mechanisms to be investigated in future studies., (© 2023. The Psychonomic Society, Inc.)

Voice message in social media environment includes a large number of conversation natural languages, which increases the difficulty of emotion tagging and affective computing. In order to solve the above difficulties, this paper analyzes the cognitive differences between the semantic and acoustic features of voice message from the perspective of cognitive neuroscience, and presents a voice feature extraction method based on EEG (Electroencephalogram) experiments, and gets the representation of 25 acoustic feature parameter vectors. Meanwhile, we proposed an affective computing method based on PAD (Pleasure-Arousal-Dominance) dimension emotional space according to the above parameters. Experiments show that the method can effectively solve the affective computing problem of voice message. Overall, there are two main contributions of this paper. Firstly, it comprehensively analyzes the emotional cognitive feature of voice message in social media environment from the perspectives of cognitive neural mechanism, voice acoustic feature and text semantics. Secondly, the segmented affective computing method for voice message based on acoustic feature parameters and PAD emotional state model is proposed. [ABSTRACT FROM AUTHOR]

This document is an introduction to a special issue of the journal Bilingualism: Language & Cognition that focuses on the clinical aspects of bilingualism research in adults. The issue includes six papers that explore various topics related to language and cognitive function in bilingual adults with clinical issues. The papers cover areas such as speech production in bilingual patients with aphasia or neurodegenerative diseases, the impact of bilingual language experience on neurocognitive outcomes, and the protective role of bilingualism in the aging process. The findings from these papers contribute to our understanding of bilingualism and its effects on language and cognition in clinical populations. [Extracted from the article]

The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the softwarehardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mindbody dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a braininspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction. [ABSTRACT FROM AUTHOR]

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The article discusses the history and development of the journal Social Cognitive & Affective Neuroscience (SCAN). The author, Matthew D. Lieberman, reflects on the early years of the journal and the challenges faced in establishing a dedicated platform for social and affective neuroscience research. Lieberman announces that he will be stepping down as the Editor-in-Chief in January 2025, marking the end of his tenure. He also outlines some upcoming changes for the journal, including the acceptance of non-neuroscience papers and the publication of a "Tools of the Trade" piece to support the growth of social and affective neuroscience in academic institutions. [Extracted from the article]

This paper explores memory from a cognitive neuroscience perspective and examines associated neural mechanisms. It examines the different types of memory: working, declarative, and non-declarative, and the brain regions involved in each type. The paper highlights the role of different brain regions, such as the prefrontal cortex in working memory and the hippocampus in declarative memory. The paper also examines the mechanisms that underlie the formation and consolidation of memory, including the importance of sleep in the consolidation of memory and the role of the hippocampus in linking new memories to existing cognitive schemata. The paper highlights two types of memory consolidation processes: cellular consolidation and system consolidation. Cellular consolidation is the process of stabilizing information by strengthening synaptic connections. System consolidation models suggest that memories are initially stored in the hippocampus and are gradually consolidated into the neocortex over time. The consolidation process involves a hippocampalneocortical binding process incorporating newly acquired information into existing cognitive schemata. The paper highlights the role of the medial temporal lobe and its involvement in autobiographical memory. Further, the paper discusses the relationship between episodic and semantic memory and the role of the hippocampus. Finally, the paper underscores the need for further research into the neurobiological mechanisms underlying non-declarative memory, particularly conditioning. Overall, the paper provides a comprehensive overview from a cognitive neuroscience perspective of the different processes involved in memory consolidation of different types of memory. [ABSTRACT FROM AUTHOR]

The goal of this paper is to examine how the development of attention networks has left many important issues unsolved and to propose possible directions for solving them by combining human and animal studies. The paper starts with evidence from citation mapping that indicates attention has played a central role in integrating cognitive and neural studies into Cognitive Neuroscience. The integration of the fields depends in part upon similarities and differences in performance over a wide variety of animals. In the case of exogenous orienting of attention primates, rodents and humans are quite similar, but this is not so with executive control. In humans, attention networks continue to develop at different rates during infancy and childhood and into adulthood. From age four on, the Attention Network Test (ANT) allows measurement of individual differences in the alerting, orienting and executive networks. Overt and covert orienting do overlap in their anatomy, but there is evidence of some degree of functional independence at the cellular level. The attention networks frequently work together with sensory, memory and other networks. Integration of animal and human studies may be advanced by examining common genes involved in individual attention networks or their integration with other brain networks. Attention networks involve widely scattered computation nodes in different brain areas, both cortical and subcortical. Future studies need to attend to the white matter that connects them and the direction of information flow during task performance. [ABSTRACT FROM AUTHOR]

Revealing the rules of traditional Chinese medicine (TCM) syndrome differentiation thinking is an important part of researching the original thinking in TCM, which involves brain cognition and neural activity principles. However, traditional research on syndrome differentiation lacks exploration of the cognitive mechanisms of TCM practitioners during syndrome differentiation. In this context of cognitive science, this paper proposes a new paradigm for studying the cognitive neural mechanisms of TCM syndrome differentiation thinking. Firstly, the paper discusses the cognitive shift in syndrome differentiation research and then points out that working memory may be an important window reflecting TCM syndrome differentiation thinking. Based on this, we propose a working hypothesis for the cognitive neural mechanisms of TCM's yin-yang syndrome differentiation and design relevant cognitive neuroscience experimental protocols to explore how the brain engages in yin-yang syndrome differentiation thinking using techniques such as event-related potential and functional magnetic resonance imaging. The implementation of this research pathway can clarify the intrinsic cognitive neural mechanisms of syndrome differentiation thinking, provide evaluation indicators for TCM syndrome differentiation thinking from cognitive neuroscience, and provide cognitive principles to support the cultivation of syndrome differentiation ability of TCM talents. [ABSTRACT FROM AUTHOR]

This paper describes the SocialVidStim-a database of video stimuli available to the scientific community depicting positive and negative social evaluative and neutral statements. The SocialVidStim comprises 53 diverse individuals reflecting the demographic makeup of the USA, ranging from 9 to 41 years old, saying 20-60 positive and 20-60 negative social evaluative statements (e.g. 'You are a very trustworthy/annoying person'), and 20-60 neutral statements (e.g. 'The sky is blue'), totaling 5793 videos post-production. The SocialVidStim are designed for use in behavioral and functional magetic resonance imaging paradigms, across developmental stages, in diverse populations. This study describes stimuli development and reports initial validity and reliability data on a subset videos (N = 1890) depicting individuals aged 18-41 years. Raters perceive videos as expected: positive videos elicit positively valenced ratings, negative videos elicit negatively valenced ratings and neutral videos are rated as neutral. Test-retest reliability data demonstrate intraclass correlations in the good-to-excellent range for negative and positive videos and the moderate range for neutral videos. We also report small effects on valence and arousal that should be considered during stimuli selection, including match between rater and actor sex and actor believability. The SocialVidStim is a resource for researchers and we offer suggestions for using the SocialVidStim in future research., (© The Author(s) 2024. Published by Oxford University Press.)

The primate brain and artificial intelligence (AI) can both be conceptualized as information processing systems, each with its own distinct biological and computational architectures. While there are parallels between them, their respective structural and functional connections show significant differences. In this paper, we examine the central role of feedback processing in both the primate brain and AI systems, which has been shown to be crucial in shaping neural processing. By reviewing the key features of feedback processes in the primate brain, which allows the brain to incorporate prior knowledge, contextual information, and task-demands into early-stage processing, we highlight the divergence in goals and functions between biological and AI systems. Understanding these differences is crucial for elucidating the cognitive capabilities of the primate brain and for addressing computational challenges in AI. In advocating "Cognition-Inspired-Computation", we suggest that integrating insights from feedback processing in the primate brain into AI research will offer potentially significant improvements for the advancement of AI systems. [ABSTRACT FROM AUTHOR]

In recent decades, the debate on consciousness has been conditioned by the idea of bottom-up emergence, which has influenced scientific research and raised a few obstacles to any attempt to bridge the explanatory gap. The analysis and explanation of vision conducted according to the accredited methodologies of scientific research in terms of physical stimuli, objectivity, methods, and explanation has encountered the resistance of subjective experience. Moreover, original Gestalt research into vision has generally been merged with cognitive neuroscience. Experimental phenomenology, building on the legacy of Gestalt psychology, has obtained new results in the fields of amodal contours and color stratifications, light perception, figurality, space, so-called perceptual illusions, and subjective space and time. Notwithstanding the outcomes and the impulse given to neuroscientific analyses, the research carried out around these phenomena has never directly confronted the issue of what it means to be conscious or, in other words, the nature of consciousness as self-referentiality. Research has tended to focus on the percept. Therefore, explaining the non-detachability of parts in subjective experience risks becoming a sort of impossible achievement, similar to that of Baron Munchausen, who succeeds in escaping unharmed from this quicksand by pulling himself out by his hair. This paper addresses how to analyze seeing as an undivided whole by discussing several basic dimensions of phenomenal consciousness on an experimental basis and suggesting an alternative way of escaping this quicksand. This mind-set reversal also sheds light on the organization and dependence relationships between phenomenology, psychophysics, and neuroscience. [ABSTRACT FROM AUTHOR]

Autism is a neurodevelopmental condition, behaviourally identified, which is generally characterised by social communication differences, and restrictive and repetitive patterns of behaviour and interests. It has long been claimed that it is more common in males. This observed preponderance of males in autistic populations has served as a focussing framework in all spheres of autism-related issues, from recognition and diagnosis through to theoretical models and research agendas. One related issue is the near total absence of females in key research areas. For example, this paper reports a review of over 120 brain-imaging studies of social brain processes in autism that reveals that nearly 70% only included male participants or minimal numbers (just one or two) of females. Authors of such studies very rarely report that their cohorts are virtually female-free and discuss their findings as though applicable to all autistic individuals. The absence of females can be linked to exclusionary consequences of autism diagnostic procedures, which have mainly been developed on male-only cohorts. There is clear evidence that disproportionately large numbers of females do not meet diagnostic criteria and are then excluded from ongoing autism research. Another issue is a long-standing assumption that the female autism phenotype is broadly equivalent to that of the male autism phenotype. Thus, models derived from male-based studies could be applicable to females. However, it is now emerging that certain patterns of social behaviour may be very different in females. This includes a specific type of social behaviour called camouflaging or masking, linked to attempts to disguise autistic characteristics. With respect to research in the field of sex/gender cognitive neuroscience, there is emerging evidence of female differences in patterns of connectivity and/or activation in the social brain that are at odds with those reported in previous, male-only studies. Decades of research have excluded or overlooked females on the autistic spectrum, resulting in the construction of inaccurate and misleading cognitive neuroscience models, and missed opportunities to explore the brain bases of this highly complex condition. A note of warning needs to be sounded about inferences drawn from past research, but if future research addresses this problem of male bias, then a deeper understanding of autism as a whole, as well as in previously overlooked females, will start to emerge. Plain Language Summary: Autism is a neurodevelopmental condition, behaviourally identified, which is generally characterised by social communication differences, and restrictive and repetitive patterns of behaviour and interests. It has long been claimed that it is more common in males, with oft-quoted ratios of 4M: 1F. This has been reflected in the development of diagnostic criteria for autism and, consequently, of measures of eligibility for autism research programmes, with females being (as is now emerging) disproportionately excluded. As outlined in this review, this issue has been particularly problematic in brain-based studies of autism. Many studies have only tested male autistic participants, or minimal numbers of autistic females. By default, sex differences were not examined. But the impression given by such research reports has commonly been that the findings would be applicable to all autistic individuals. Recent psychological and clinical research has shown that there are a significant number of autistic females who have been missed by traditional diagnostic practices. Their inclusion has increased their eligibility for autism research studies. With respect to brain research, it has become possible to devise studies with matched numbers of autistic females and males, and to replicate studies that have previously only tested males. Newly emerging findings from such studies are demonstrating that the 'robust' autism-related differences previously observed in autistic male-only cohorts do not fully generalise to autistic females. It will be necessary to exercise caution in drawing inferences from previous male-biased studies of the autistic brain. However, the identification and inclusion of previously excluded female autistic participants hopefully offers more accurate insights into this highly complex and heterogeneous condition. Highlights: Several decades of neuroimaging research into autism has been based almost entirely on males; even big data sets show strong evidence of male bias. There is clear evidence that autistic females are being excluded from the research process by failures in diagnostic practices that have been developed on male-biased cohorts. Models of autism which inform research protocols are based on male autism phenotypes; it appears to have been assumed that the substantially fewer females that are diagnosed will present with equivalent, if milder, patterns of autistic differences. Newly emerging findings from social cognitive neuroscience research studies investigating sex/gender differences in autistic behaviour and associated biological correlates are demonstrating that the 'robust' autism-related differences previously observed in autistic male-only cohorts do not fully generalise to autistic females. Autism research programmes should prioritise the exploration of sex/gender effects. [ABSTRACT FROM AUTHOR]

Despite being a central topic, current theories of motivation in sociology remain underdeveloped. This paper supplements existing sociological theories on motivation with insights from affective and cognitive neuroscience to address this issue. The resulting sociological affective model of motivation treats affect as an independent force that sometimes coordinates with cognition while taking charge at other times. Drawing on recent work in the neuroscience of motivation and reward, the paper shows how two affective mechanisms—wanting and liking—can shed light on various behavioral outcomes of interest while allowing for sharper theorizing of key distinctions that need clarification in the literature. By examining the distinctive contribution of each process, the paper reveals a proactive, desire‐driven agent often overlooked by prevailing sociological models emphasizing a reactive actor responding creatively and deliberately when internal meanings and external cues are incongruent. The paper concludes by outlining the broader implications of this synthetic affective model of motivation for sociology. [ABSTRACT FROM AUTHOR]

There are many times when visuals and text will often disrupt each other in a single space and, as a result, make both more difficult to understand. Many methods exist to counteract this, such as placing text and visuals in specific locations in the given space. We have utilized these certain "combinations" to a great extent. Still, when there are special cases where these methods are less effective, finding an optimal combination as a replacement is more difficult. To see other patterns and techniques to assist in locating salient elements, we can use game theory and graph theory, as well as a small amount of cognitive neuroscience, to determine what combinations allow words and visuals to complement each other rather than compete. The research done in this paper is exemplified by bento grids, a recent design trend popularized by Apple. [ABSTRACT FROM AUTHOR]

This paper is concerned with the ℓ 1 , ∞ -norm ball constrained multi-task learning problem, which has received extensive attention in many research areas such as machine learning, cognitive neuroscience, and signal processing. To address the challenges of solving large-scale multi-task Lasso problems, this paper develops an inexact semismooth Newton-based augmented Lagrangian (Ssnal) algorithm. When solving the inner problems in the Ssnal algorithm, the semismooth Newton (Ssn) algorithm with superlinear or even quadratic convergence is applied. Theoretically, this paper presents the global and asymptotically superlinear local convergence of the Ssnal algorithm under standard conditions. Computationally, we derive an efficient procedure to construct the generalized Jacobian of the projector onto ℓ 1 , ∞ -norm ball, which is an important component of the Ssnal algorithm, making the computational cost in the Ssn algorithm very cheap. Comprehensive numerical experiments on the multi-task Lasso problems demonstrate that the Ssnal algorithm is more efficient and robust than several existing state-of-the-art first-order algorithms. [ABSTRACT FROM AUTHOR]

Background: This paper discusses a paradox in mental health. It manifests as a relationship between adverse "bad" effects (suffering, clinical costs, loss of productivity) in individuals and populations and advantageous "good" aspects of mental disorders. These beneficial aspects (scientific, artistic and political accomplishments) emanate at the societal level through the frequently unprecedented creativity of people suffering from mental disorders and their relatives. Such gains can contribute to societal innovation and problem-solving. Especially in times of accelerated social-ecological change, approaches are needed that facilitate best-possible mental health care but also recognize creative ideas conducive to beneficial clinical and social-ecological innovations as soon as possible. Discussion: This paper emphasizes the need to account for creativity as a crucial component in evolving mental health systems and societies. It highlights the need for wide-ranging approaches and discusses how research targeting multiple facets (e.g., brain level, cognitive neuroscience, psychiatry, neurology, socio-cultural, economic and other factors) might further our understanding of the creativity-mental disorder link and its importance for innovating mental health systems and societies. Conclusion: Our discussion clarifies that considerable research will be needed to obtain a better understanding of how creativity associated with mental disorders may help to create more sustainable societies on a fast-changing planet through innovative ideas. Given the current-state-of-the-art of research and healthcare management, our discussion is currently speculative. However, it provides a basis for how pros and cons might be studied in the future through transdisciplinary research and collaborations across sectors of society. [ABSTRACT FROM AUTHOR]

Problem-solving skills are highly valued in modern society and are often touted as core elements of school mission statements, desirable traits for job applicants, and as some of the most complex thinking that the brain is capable of executing. While learning to problem-solve is a goal of education, and many strategies, methodologies, and activities exist to help teachers guide the development of these skills, there are few formal curriculum structures or broader frameworks that guide teachers toward the achievement of this educational objective. Problemsolving skills have been called "higher order cognitive functions" in cognitive neuroscience as they involve multiple complex networks in the brain, rely on constant rehearsal, and often take years to form. Children of all ages employ problem solving, from a newborn seeking out food to children learning in school settings, or adults tackling real-world conflicts. These skills are usually considered the end product of a good education when in fact, in order to be developed they comprise an ongoing process of learning. "Ways of thinking" have been studied by philosophers and neuroscientists alike, to pinpoint cognitive preferences for problem solving approaches that develop from exposure to distinct models, derived from and resulting in certain heuristics used by learners. This new theory paper suggests a novel understanding of the brain's approach to problem solving that structures existing problem-solving frameworks into an organized design. The authors surveyed problem-solving frameworks from business administration, design, engineering, philosophy, psychology, education, neuroscience and other learning sciences to assess their differences and similarities. This review lead to an appreciation that different problem-solving frameworks from different fields respond more or less accurately and efficiently depending on the kinds of problems being tackled, leading to our conclusion that a wider range of frameworks may help individuals approach more varied problems across fields, and that such frameworks can be organized in school curriculum. This paper proposes that explicit instruction of "mental frameworks" may help organize and formalize the instruction of thinking skills that underpin problem-solving--and by extension--that the more such models a person learns, the more tools they will have for future complex problem-solving. To begin, this paper explains the theoretical underpinnings of the mental frameworks concept, then explores some existing mental frameworks which are applicable to all age groups and subject areas. The paper concludes with a list of five limitations to this proposal and pairs them with counter-balancing benefits. [ABSTRACT FROM AUTHOR]

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

Recently, cultural neuroscience has gained attention as a new, important, and interdisciplinary topic in the field of neuroscience. It helps us understand the interaction of cultural and biological factors over the course of life. This study aims to provide a comprehensive overview of the field to readers and potential researchers engaged in cultural neuroscience research. A bibliometric analysis was performed on 113 articles in the field of cultural neuroscience from 2008 to 2021 using data from the core collection of Web of Science. Network visualization software VOSviewer and ITGInsight were used for performance analysis and science mapping. Specifically, the performance analysis included countries, institutions, authors, papers, and journals, while science mapping analyzed the collaboration network, keyword network, bibliographic coupling network, and time series evolution. The results showed that the United States was the most productive country, Northwestern University was the most influential research institution, Chiao Jy was the most influential scholar, and "Social Cognitive and Affective Neuroscience" made the greatest contribution to publishing in the field of cultural neuroscience. Furthermore, collaboration is expected to be the development trend in the future. The key research topics in the field of cultural neuroscience included neuroimaging and psychiatric diseases, theoretical methods, interdisciplinary research, cultural differences (collectivism and individualism), and brain functions. Finally, future research will focus on cultural neuroscience, culture, and self, while adolescence will be the emerging research frontier. [ABSTRACT FROM AUTHOR]

Neural degeneration is a hallmark of healthy aging and can be associated with specific cognitive impairments. However, neural degeneration per se is not matched by unremitting declines in cognitive abilities. Instead, middle-aged and older adults typically maintain surprisingly high levels of cognitive functioning, suggesting that the human brain can adapt to structural degeneration by neural compensation. Here, we summarize prevailing theories and recent empirical studies on neural compensation with a focus on often neglected contributing factors, such as lifestyle, metabolism and neural plasticity. We suggest that these factors moderate the relationship between structural integrity and neural compensation, maintaining psychological well-being and behavioral functioning. Finally, we discuss that a breakdown in neural compensation may pose a tipping point that distinguishes the trajectories of healthy vs pathological aging, but conjoint support from psychology and cognitive neuroscience for this alluring view is still scarce. Therefore, future experiments that target the concomitant processes of neural compensation and associated behavior will foster a comprehensive understanding of both healthy and pathological aging., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This editorial titled "Cognitive perception: cognition-dependent perception, perception-dependent cognition" explores the relationship between perception and cognition in the context of cognitive perception (CP). It argues for a unified understanding of perception and cognition to fully comprehend the mind. The editorial presents different perspectives on this topic, including the role of Non-Axiomatic Logic (NAL) in perception and knowledge, the connection between attention mechanisms and perception and cognition, and the significance of causal reasoning in general intelligence. It emphasizes the advantages of a holistic approach and calls for further research in this area. The text also suggests future research topics, such as the compositionality of perceptual representation and the relationship between precepts and concepts. The authors stress the importance of collaboration between psychology, cognitive neuroscience, and artificial intelligence to gain a more comprehensive understanding of the mind. [Extracted from the article]

This is an invited commentary on the position paper of Gruber, Block and Montemayor (2022). First, I oppose the premise in Gruber et al. 's proposal according to which there exists a 'real' and an 'illusory' time. Second, our knowledge about the universe is hypothesized, tested, and verified by the most complex physical systems known to date (brains) hence postulating the coexistence of an 'absolute' and a 'real', or an 'illusory' and a 'nonillusory' time might be unnecessary. Instead, and parsimoniously, (organic or atomic) measuring devices for duration, simultaneity, order, and so forth, have variable precision. The most exquisite clock is atomic, a good enough one for survival on Earth is at the scale of neural networks. A difference between measuring devices and brains is that the latter compute on stored temporal information. Last, I suggest additional gadgets for the Information Gathering and Utilizing Systems (IGUs) proposal. In sum, I oppose some of the theoretical premises while lending support to the idea that representing temporal statistics is a useful heuristics for some aspects of temporal cognition. From the cognitive neuroscience standpoint, IGUs are compatible with information-theoretic approaches, and with current Bayesian and predictive models implemented in neural systems, in which brains code information and compute on symbolic representations. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cognitive neuroscience experiments require accurate and traceable methods of measuring cognitive phenomena, analyzing and processing data, and validating results, including measurement of impact of such phenomena on brain activity and consciousness. EEG measurement is the most widely used tool for evaluation of the experiment's progress. To extract more information from the EEG signal, continuous innovation is necessary to provide a broader range of information. OBJECTIVE: This paper presents a new tool for measuring and mapping cognitive phenomena using time window-based multispectral brain mapping of electroencephalography (EEG) signals. METHODS: The tool was developed using Python programming language and enables users to create brain maps images for six spectra (Delta, Theta, Alpha, Beta, Gamma, and Mu) of EEG signal. The system can accept an arbitrary number of EEG channels with standardized labels based on the 10–20 system, and users can select the channels, frequency bandwidth, type of signal processing, and time window length to perform the mapping. RESULTS: The key advantage of this tool is its ability to perform short-time brain mapping, which allows for the exploration and measurement of cognitive phenomena. The tool's performance was evaluated through testing on real EEG signals, and results demonstrated its effectiveness in accurately mapping cognitive phenomena. CONCLUSION: The developed tool can be used in various applications, including cognitive neuroscience research and clinical studies. Future work involves optimizing the tool's performance and expanding its capabilities. [ABSTRACT FROM AUTHOR]

Starting from the tension and integration between cognitive neuroscience and cognitive psychology, this paper analyzes the waxing and waning of physicalism versus mentalism over the last century and defends an anti-reductionist ontology of the mind. It first argues that "function logically precedes structure" and then refutes two classic arguments about the mind-brain problem. The paper shows that if structure is treated as the logical premise of function, discussions of the mind-brain problem will inevitably lead to dualism or reductionism. Structure cannot explain its own occurrence; instead, if function, which logically precedes structure, is taken as the starting point of the explanation, then the principle of the "ladder of explanation" will be satisfied. A higher-level theory can explain lower-level observed facts, and lower-level observed facts are the cognitive starting point leading to a theory through induction. We conclude that function logically precedes structure; therefore, logically, "the mind shapes the brain." The logical sequence from function to structure represents a reverse strategy for solving the mind-brain problem. [ABSTRACT FROM AUTHOR]

Investigating how animals navigate space and time is key to understanding communication. Small differences in spatial positioning or timing can mean the difference between a message received and a missed connection. However, these spatio-temporal dynamics are often overlooked or are subject to simplifying assumptions in investigations of animal signaling. This special issue addresses this significant knowledge gap by integrating work from researchers with disciplinary backgrounds in neuroscience, cognitive ecology, sensory ecology, computer science, evolutionary biology, animal behavior, and philosophy. This introduction to the special issue outlines the novel questions and approaches that will advance our understanding of spatio-temporal dynamics of animal communication. We highlight papers that consider the evolution of spatio-temporal dynamics of behavior across sensory modalities and social contexts. We summarize contributions that address the neural and physiological mechanisms in senders and receivers that shape communication. We then turn to papers that introduce cutting edge technologies that will revolutionize our ability to track spatio-temporal dynamics of individuals during social encounters. The interdisciplinary collaborations that gave rise to these papers emerged in part from a novel workshop-symposium model, which we briefly summarize for those interested in fostering syntheses across disciplines. [ABSTRACT FROM AUTHOR]

This document is an editorial published in Frontiers in Human Neuroscience titled "Methods and protocols in Brain-Computer Interfaces." The editorial discusses the rapid progress of research on Brain-Computer Interfaces (BCIs) and their applications in various fields. It highlights the challenges that need to be overcome for BCIs to be ready for use in different environments. The editorial also introduces several papers that were selected for their contributions to the discussion on BCIs, including topics such as motion artifact reduction, EEG functional connectivity, hybrid BCI systems, and an open-source research framework for BCIs. The authors express their gratitude to the contributors and reviewers of the research topic. [Extracted from the article]

Causal inference is a key step in many research endeavors in cognitive science and neuroscience, and particularly cognitive neuroscience. Statistical knowledge is sufficient for prediction and diagnosis, but causal knowledge is required for action and intervention. Most statistics courses and textbooks emphasize the difficulty of causal inference, focusing on the maxim that "correlation does not mean causation": there can be multiple causal possibilities, often many of them, consistent with given observed statistics. This paper focuses instead on the conceptual issues and assumptions that confront causal and other kinds of inference, primarily focusing on cognitive neuroscience. We connect inference methods with goals and challenges, and provide concrete guidance about how to select appropriate tools for the scientific task. This article is categorized under: Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science., (© 2023 The Authors. WIREs Cognitive Science published by Wiley Periodicals LLC.)

The construction process is a dynamic one, and the complexity of the working conditions and the high level of uncertainty make the construction industry the third most dangerous industry after mining and agriculture. And since the construction industry is vital to the development of a country, safety during construction is of particular importance. A great deal of research, studies and practices have been conducted to reduce potential risks and improve worker efficiency during the construction process. In recent years, with the rapid development of cognitive neuroscience and the integration of medical technology, various wearable monitoring devices have been widely used in the field of building construction for real-time monitoring of workers' physical and mental conditions. Among them, the application of EEG (electroencephalogram) in the building construction process enables researchers to gain insight into the physical and mental state of construction workers while performing construction tasks. This paper introduces EEG technology and portable EEG monitoring equipment and summarizes its application in monitoring workers' adverse reactions (emotion, fatigue, psychological burden, and vigilance) and construction hazard identification during the process of construction in recent years, which provides future EEG research in the field of building construction and construction site safety management. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) spectroscopy, also known as functional NIR spectroscopy (fNIRS), is a tool for measuring the hemodynamic response of the prefrontal cortex (PFC) by using NIR light, enabling a noninvasive indirect neural activity assessment. The application of fNIRS in emotion recognition or the differential diagnosis of psychiatric disorders of depressive patients, including major depressive disorder, bipolar disorder and schizophrenia, has previously been reported. Although the use of fNIRS has gradually expanded in cognitive neuroscience studies, few researchers have focused on the effects of light exposure in fNIRS studies. In addition, to the best of our knowledge, there are no scoping reviews of fNIRS studies on light exposure. Because light is an important topic in cognitive neuroscience and psychiatry, we evaluated fNIRS studies on light exposure in humans. We reviewed 10 papers in their entirety. Bright light (BR) modulates fear, and the color differences showed no significance in 1 study, whereas other studies delved extensively into the effects of colored light, finding some individual hemodynamic responses. In our study, we highlighted that the effects of natural light have not been studied using fNIRS. Light is becoming a critical topic in cognitive neuroscience and psychiatry, and fNIRS is critical for improving public health and managing psychiatric disorders. [ABSTRACT FROM AUTHOR]

The Gestalt psychologists' theory of insight problem-solving was based on a direct parallelism between perceptual experience and higher-order forms of cognition (e.g., problem-solving). Similarly, albeit not exclusively, to the sudden recognition of bistable figures, these psychologists contended that problem-solving involves a restructuring of one's initial representation of the problem's elements, leading to a sudden leap of understanding phenomenologically indexed by the "Aha!" feeling. Over the last century, different scholars have discussed the validity of the Gestalt psychologists' perspective, foremost using the behavioral measures available at the time. However, in the last two decades, scientists have gained a deeper understanding of insight problem-solving due to the advancements in cognitive neuroscience. This review aims to provide a retrospective reading of Gestalt theory based on the knowledge accrued by adopting novel paradigms of research and investigating their neurophysiological correlates. Among several key points that the Gestalt psychologists underscored, we focus specifically on the role of the visual system in marking a discrete switch of knowledge into awareness, as well as the perceptual experience and holistic standpoints. While the main goal of this paper is to read the previous theory in light of new evidence, we also hope to initiate an academic discussion and encourage further research about the points we raise. [ABSTRACT FROM AUTHOR]

How do affect and cognition interact in managerial decision making? Over the last decades, scholars have investigated how managers make decisions. However, what remains largely unknown is the interplay of affective states and cognition during the decision-making process. We offer a systematization of the contributions produced on the role of affect and cognition in managerial decision making by considering the recent cross-fertilization of management studies with the neuroscience domain. We implement a Systematic Literature Review of 23 selected contributions dealing with the role of affect and cognition in managerial decisions that adopted neuroscience techniques/points of view. Collected papers have been analyzed by considering the so-called reflexive (X-) and reflective (C-) systems in social cognitive neuroscience and the type of decisions investigated in the literature. Results obtained help to support an emerging "unified" mind processing theory for which the two systems of our mind are not in conflict and for which affective states have a driving role toward cognition. A research agenda for future studies is provided to scholars who are interested in advancing the investigation of affect and cognition in managerial decision making, also through neuroscience techniques – with the consideration that these works should be at the service of the behavioral strategy field. [ABSTRACT FROM AUTHOR]

The study of nervous system architecture and its behavior is an interdisciplinary science related to "Neuroscience." It contains each and every detail related to neuroevolution, molecular and cellular biostructure, anatomy, and pharmacology. To understand this complex structure and architecture of nervous system, a fast, reliable, and advanced technology is required. Object recognition (OR) plays a vital role to understand and adapt the complex system in an easy way. Object recognition provides a computational and cognitive platform to link neuroscience with the human-machine interface for proper interaction in the medical field. OR-computational neuroscience deals with the neural pattern through different models, whereas OR-cognitive science helps to understand the behavior mechanism of neural architecture. The goal of this research is to explain how the brain interprets and processes information using electrical and chemical signals. The paper contains OR-based models in the field of neuroscience. It examines neural representations, neuronal type communication, and neural learning in depth. This paper provides an overview of OR-based cognitive computational neuroscience as well as the models that go with it. A thorough examination of the applications is presented, followed by a discussion of potential future paths. [ABSTRACT FROM AUTHOR]

In a discussion paper published in the special issue of Cognitive Neuroscience, Sex Differences in the Brain, we investigated whether certain experimental parameters contributed to findings in functional magnetic resonance imaging studies of sex differences during long-term memory. Experimental parameters included: the number of participants, stimulus type(s), whether or not performance was matched, whether or not sex differences were reported, the type of between-subject statistical test used, and the contrast(s) employed. None of these parameters determined whether or not differences were observed, as all included studies reported sex differences. We also conducted a meta-analysis to determine if there were any brain regions consistently activated to a greater degree in either sex. The meta-analysis identified sex differences (male > female) in the lateral prefrontal cortex, visual processing regions, parahippocampal cortex, and the cerebellum. We received eight commentaries in response to that paper. Commentaries called for an expanded discussion on various topics including the influence of sex hormones, the role of gender (and other social factors), the pros and cons of equating behavioral performance between the sexes, and interpreting group differences in patterns of brain activity. There were some common statistical assumptions discussed in the commentaries regarding the 'file drawer' issue (i.e., the lack of reporting of null results) and effect size. The current paper provides further discussion of the various topics brought up in the commentaries and addresses some statistical misconceptions in the field. Overall, the commentaries echoed a resounding call to include sex as a factor in cognitive neuroscience studies. [ABSTRACT FROM AUTHOR]

In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points. [ABSTRACT FROM AUTHOR]

Civitarese masterfully unravels and explains Husserl's new approach, largely found in unpublished notes and manuscripts, in which he proposed that subjectivity and intersubjectivity originate together in a dialectical, prereflexive, transcendental unity. As Civitarese aptly states, "for Husserl, intersubjectivity is not a concept descriptive of a directly observable reality, but a transcendental concept... and not a suitable term to account for the child observed in the rich capacity for interaction it already possesses at birth" (p. 860). His remarkably clear presentation of Husserl and of his own perspective on intersubjectivity makes a powerful argument about the central issues facing contemporary psychoanalysis: what does intersubjectivity mean in practice and how best to conceive of it? I enjoyed reading Giuseppe Civitarese's eloquent and conceptually rich paper. [Extracted from the article]

Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the ambiguity of legal language and the diversity of value functions. The imprecision and procedural knowledge inherent in law makes judicial issues difficult to be expressed in a computer symbol system. Current semantic processing and machine learning technologies cannot fully capture the complex nature of legal relations, thereby raising doubts about the accuracy of legal predictions and reliability of judicial models. Cognitive computing, designed to emulate human brain functions and aid in enhancing decision-making processes, offers a better understanding of legal data and the processes of legal reasoning. This paper discusses the advancements made in cognitive methods applied to legal concept learning, semantic extraction, judicial data processing, legal reasoning, understanding of judicial bias, and the interpretability of judicial models. The integration of cognitive neuroscience with law has facilitated several constructive attempts, indicating that the evolution of cognitive law could be the next frontier in the intersection of AI and legal practice. [ABSTRACT FROM AUTHOR]

Introduction: This article presents a systematic literature review that follows the PRISMA and PICOS guidelines to analyze current research trends on cognition, integrative complexity (IC) (a cognitive feature focusing on information processing in a person's response rather than its quantity or quality), and decisionmaking from the perspectives of activity theory and neuroscience. Methods: The study examines 31 papers published between 2012 and 2022 and 19 articles specifically related to neuroscience. We performed a content analysis using six categories within activity theory: subjects, objects, rules, community, division of labor, and outcomes. Results: The study investigates the relationship between decision-making outcomes and IC as a cognitive feature in various contexts. Additionally, content analysis on neuroscience and IC revealed significant research gaps, including understanding the nature of IC, challenges related to its measurement, and differentiation from other cognitive features. We also identify opportunities for investigating the brain's activity during decision-making in relation to IC. Discussion:We address the need for a more precise categorization of IC in studies of cognition, IC, and decision-making. We discuss the implications of our analysis for understanding the cognitive nature of IC and the potential of neuroscience methods for studying this attribute. [ABSTRACT FROM AUTHOR]

We examine the challenging "marriage" between computational efficiency and biological plausibility—A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man's land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a crucial tool in cognitive neuroscience, enabling the study of neurophysiological function by measuring the brain's electrical activity. Its applications include perception, learning, memory, language, decision making and neural network mapping. Recently, interest has surged in extending EEG measurements to domestic environments. However, the high costs associated with traditional laboratory EEG systems have hindered accessibility for many individuals and researchers in education, research, and medicine. To tackle this, a mobile-EEG device named "DreamMachine" was developed. A more affordable alternative to both lab-based EEG systems and existing mobile-EEG devices. This system boasts 24 channels, 24-bit resolution, up to 6 h of battery life, portability, and a low price. Our open-source and open-hardware approach empowers cognitive neuroscience, especially in education, learning, and research, opening doors to more accessibility. This paper introduces the DreamMachine's design and compares it with the lab-based EEG system "asalabTM" in an eyes-open and eyes-closed experiment. The Alpha band exhibited higher power in the power spectrum during eyes-closed conditions, whereas the eyes-open condition showed increased power specifically within the Delta frequency range. Our analysis confirms that the DreamMachine accurately records brain activity, meeting the necessary standards when compared to the asalabTM system. [ABSTRACT FROM AUTHOR]

Hong Kong's 'trilingual and biliterate' language policy (TaB, 三語兩文) is almost as old as the special administrative region (SAR) itself. Through free education and language support measures in school, students are expected to be conversant in English and Putonghua in addition to Cantonese, and be able to read and understand written Chinese and English. After being implemented for over two decades, however, there are signs that most students' language standards in Chinese and English fall short of the TaB target, as measured by the public examination results of successive generations of secondary school leavers. Designed with essentially Cantonese-dominant Hongkongers in mind, the TaB policy consists of many measures, with the 'medium of instruction streaming policy' introduced since September 1998 being the most controversial. Driven by the twin principles of monolingual English-medium instruction (EMI) and 'no language mixing allowed', secondary schools are divided into two streams. Stringent requirements must be met before a school could claim to be an EMI school. According to this 'late immersion' model for students aged 11–12 at secondary level, every year about 30 percent of the primary school leavers are allocated to an EMI school. Following Li (Multilingual Hong Kong: languages, literacies and identities. Springer, Cham, 2017), this paper will first discuss why the TaB target is such a tall order for Cantonese-dominant students by reviewing the relevant literature along five inter-related dimensions: contrastive linguistics, psycholinguistics, sociolinguistics, cognitive neuroscience, and instructional strategies and bilingual pedagogies. I will then examine the SAR government's language support measures to assess their effectiveness and explore possible alternatives. The paper will end with a number of recommendations, which together constitute an LPP (language policy and planning) roadmap for improving the chance with which the TaB policy is likely to produce more positive outcomes. (i) To re-prioritize the investment and extent of language support by strengthening the quality of language input at the key stages of learning from age 3–9, which in curriculum terms correspond with K1–P3; (ii) To use Cantonese as the medium of instruction for teaching all subjects except English and Putonghua as separate subjects at preschool (K1–K3, age 3–6); (iii) To explore the possibility of implementing total immersion in Putonghua for three years at lower primary level (P1–P3, age 6–9); (iv) To abandon the 'maximum exposure, no mixing' dogma in secondary education and to encourage basic and action research in bilingual pedagogies and instructional strategies informed by Content-and-language integrated learning (CLIL); (v) To attract academically bright and linguistically gifted students to receive professional training and be prepared and committed for a career in language teaching; and (vi) To encourage civil servants of various government offices, schoolteachers, and university staff to initiate a 'speak English/Putonghua where we can' campaign. For these policy measures to be successfully implemented and bear fruit, apart from careful planning, there ought to be strong leadership from within the government and the education sector, plus mechanisms for coordinating concerted actions on the part of various groups of stakeholders, notably teachers, school principals, educationalists, and experts of language teaching and learning in academia. [ABSTRACT FROM AUTHOR]

This special issue of Cognitive Neuroscience focuses on the roles of the hippocampus during long-term memory. A discussion paper by Tallman, Clark, and Smith (this issue) found that functional connectivity of the hippocampus with the parahippocampal cortex and fusiform gyrus decreased with memory age, providing support for systems consolidation. Commentaries were received by Berdugo-Vega and Gräff (this issue), Feld and Gerchen (this issue), Gellersen and Simons (this issue), Gobbo, Mitchell-Heggs, and Tse (this issue), Gilmore, Audrain, and Martin (this issue), Kirwan (this issue), Manns (this issue), Runyan and Brooks (this issue), Santangelo (this issue), and Yang (this issue). The author response considered the content and context of memorial information along with neuroanatomy and functional specialization and conducted new analyses to clarify their findings. An empirical fMRI paper by Thakral, Yu, and Rugg (this issue) reported that the hippocampus was sensitive to the amount of contextual information retrieved, regardless of remember-know status. Another empirical study by Bjornn, Van, and Kirwan (this issue) found that hippocampal activation changes were correlated with the number of fixations at study for correct but not incorrect mnemonic discrimination judgments. A second discussion paper (Slotnick, this issue) concluded that no fMRI studies have provided evidence that the hippocampus is associated with working memory. Commentaries were received by Courtney (this issue), Kessels and Bergmann (this issue), Peters and Reithler (this issue), Rose and Chao (this issue), Stern and Hasselmo (this issue), and Wood, Clark, and Nee (this issue). The articles in this special issue illustrate that the roles of the hippocampus in long-term memory (and other types of memory) are under active investigation and provide many directions for research in the immediate future. [ABSTRACT FROM AUTHOR]

Understanding the neural correlates of unconscious perception stands as a primary goal of experimental research in cognitive psychology and neuroscience. In this Perspectives paper, we explain why experimental protocols probing qualitative dissociations between perception and action provide valuable insights into conscious and unconscious processing, along with their corresponding neural correlates. We present research that utilizes human eye movements as a sensitive indicator of unconscious visual processing. Given the increasing reliance on oculomotor and pupillary responses in consciousness research, these dissociations also provide a cautionary tale about inferring conscious perception solely based on no-report protocols. [ABSTRACT FROM AUTHOR]

In this work, we obtain weak order-2 conditions of Runge–Kutta method for the optimal control of stochastic differential equations which occurs in many areas of economics and finance and recently in cognitive sciences and neuroscience. We get the order conditions that a stochastic Runge–Kutta technique must meet to have weak order two by comparing the stochastic expansion of the approximation with the associated Taylor scheme. Moreover, we present numerical examples which verify the theoretical results. We conclude our paper by a summary and an outlook to future research and application. [ABSTRACT FROM AUTHOR]

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human‐centered explanations to enhance human−AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems. In this paper, I summarize my research in visual cognition, language processing, and explainable AI to demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems. [ABSTRACT FROM AUTHOR]

It has long been argued that only humans could produce and understand language. But now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the new purchase LMs are providing on the question of how language is implemented in the brain. We discuss why, a priori, LMs might be expected to share similarities with the human language system. We then summarize evidence that LMs represent linguistic information similarly enough to humans to enable relatively accurate brain encoding and decoding during language processing. Finally, we examine which LM properties—their architecture, task performance, or training—are critical for capturing human neural responses to language and review studies using LMs as in silico model organisms for testing hypotheses about language. These ongoing investigations bring us closer to understanding the representations and processes that underlie our ability to comprehend sentences and express thoughts in language. [ABSTRACT FROM AUTHOR]