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Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience., (© 2023. Crown.)

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]

In this paper, I analyse how the emerging scientific framework of radical embodied neuroscience is different from contemporary mainstream cognitive neuroscience. To do so, I propose the notion of motif to enrich the philosophical toolkit of cognitive neuroscience. This notion can be used to characterize the guiding ideas of any given scientific framework in psychology and neuroscience. Motifs are highly unconstrained, open-ended concepts that support equally open-ended families of explanations. Different scientific frameworks-e.g., psychophysics or cognitive neuroscience-provide these motifs to answer the overarching themes of these disciplines, such as the relationship between stimuli and sensations or the proper methods of the sciences of the mind. Some motifs of mainstream cognitive neuroscience are the motif of encoding, the motif of input-output systems, and the motif of algorithms. The two first ones answer the question about the relationship between stimuli, sensations and experience (e.g., stimuli are input and are encoded by brain structures). The latter one answers the question regarding the mechanism of cognition and experience. The three of them are equally unconstrained and open-ended, and they serve as an umbrella for different kinds of explanation-i.e., different positions regarding what counts as a code or as an input. Along with the articulation of the notion of motif, the main aim of this article is to present three motifs for radical embodied neuroscience: the motif of complex stimulation, the motif of organic behaviour and the motif of resonance., (© 2024 The Author(s). European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

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]

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 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]

Cognitive neuroscience seeks to explain mind, brain, and behavior. But how do we generate explanations? In this integrative theoretical paper, we review the commitments of the 'New Mechanist' movement within the philosophy of science, focusing specifically on the role of mechanistic models in scientific explanation. We highlight how this approach differs from other explanatory approaches within the field, showing its unique contributions to the efforts of scientific explanation. We then argue that the commitments of the Embodied Cognition framework converge with the commitments of the New Mechanist movement in a way that provides a necessary explanatory strategy available to cognitive neuroscience. We then discuss a number of consequences of this convergence, including issues related to the inadequacy of statistical prediction, neuroscientific reduction, the autonomy of psychology from neuroscience, and psychological and neuroscientific ontology. We hope that our integrative thesis provides researchers with a theoretical strategy for an embodied cognitive neuroscience.

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.)

Objective: This article provides the test–retest reliability and Reliable Change Indices (RCIs) of the Philips IntelliSpace Cognition (ISC) platform, which contains digitized versions of well-established neuropsychological tests. Method: 147 participants (ages 19 to 88) completed a digital cognitive test battery on the ISC platform or paper-pencil versions of the same test battery during two separate visits. Intraclass correlation coefficients (ICC) were calculated separately for the ISC and analog test versions to compare reliabilities between administration modalities. RCIs were calculated for the digital tests using the practice-adjusted RCI and standardized regression-based (SRB) method. Results: Test–retest reliabilities for the ISC tests ranged from moderate to excellent and were comparable to the test-retest reliabilities for the paper-pencil tests. Baseline test performance, retest interval, age, and education predicted test performance at visit 2 with baseline test performance being the strongest predictor for all outcome measures. For most outcome measures, both methods for the calculation of RCIs show agreement on whether or not a reliable change was observed. Conclusions: RCIs for the digital tests enable clinicians to determine whether a measured change between assessments is due to real improvement or decline. Together, this contributes to the growing evidence for the clinical utility of the ISC platform. [ABSTRACT FROM AUTHOR]

This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students' performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes. [ABSTRACT FROM AUTHOR]

Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding. [ABSTRACT FROM AUTHOR]

Objective: Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving. Background: Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception. Methods: Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports. Results: Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario. Conclusion: This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario. Application: Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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.)

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]

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]

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 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]