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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]

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]

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]

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 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]

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]

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]

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 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]

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]

The demise of the scholastic worldview and the rise of the mechanistic one may give the impression of a parallel demise of the scholastic explanatory framework. In this paper, I argue that this impression is wrong. To this end, I first outline Descartes' representative and particularly sharp mechanistic criticism of the scholastic notion of explanation. Deploying conceptual machinery from contemporary philosophy of science, I then suggest a reconstruction of the scholastic notion that is immune to Descartes' criticism. Based on this reconstruction, I reinterpret the dispute between Descartes and the scholastics as one that concerns the extent of two legitimate conceptions of explanation. Finally, I outline a contemporary dispute within cognitive neuroscience that reflects the Cartesian-scholastic one as thus reinterpreted, thereby showing that aspects of the world may well require a scholastic-like approach for their explanation. The aim of this paper, then, is to shed light on a most important philosophical-cum-scientific historical controversy from a modern perspective, but also to highlight the deep historical roots of a related contemporary dispute. Based on this, the paper also seeks to draw a substantial philosophical conclusion concerning the issue under dispute in both controversies. [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]

The initial version of the article by Clithero, Karmarkar, Nave, and Plassmann (Journal of Consumer Psychology, 2024) was critiqued by open comments from a small group of scholars. Their suggestions encouraged the authors to clarify challenging relationships between brain processes and emotions, beliefs, and actions. The revision expanded fMRI and EEG to include measures of vision, facial expression, breathing, heart rhythms, and blood chemistry. The paper provides multiple avenues of joint work between neurological and psychological scholars. The comments below reflect different reactions to the final article. Wes Hutchinson acknowledges that neuroscience insights complement cognitive measures that generate explicit measures of thought, emotion, or preferences, but he warns that repeated measures over time are problematic for both types of measurement, and the inherent complexity of brain–behavior relationships is often underestimated. With both orientations, understanding the functioning of human behavior is akin to making sense of an orchestra, where the interactive blending of different instruments and musicians reflects a complex activity that generates sounds, emotions, and stories. Both consumer neuroscientists and psychologists need to broaden their paradigmatic approaches with bodily measures and advanced psychological procedures to overcome challenges to joint progress. Martin Reiman asserts that despite difficulties with measures that have different levels of abstraction or velocity, research has provided remarkable associations between brain activity and consumer behavior. Effective studies merging brain and behavior can effectively proceed with studies that differ in two dimensions: first, by altering the number of variables, and second, by shifting whether the scientific paradigm is inductive or deductive. In its simple form, the Excavation path explores brain activity when a person is exposed to specific statements or emotions. In its most challenging form, Integrative Studies generate predictions from theories that test the convergent validity of divergent measures and leverage skills from different researchers. Studies reflecting high levels on one dimension but low levels on the other can also provide fruitful research opportunities. Brian Knutson, like Reimann, counters the idea that consumer psychology has not lived up to its promises. He references studies showing that activity from very specific areas of the brain reliably predicts choices better than explicit ratings or choices. Such research generates deductions from increasingly precise neural maps that enable confirmation of theory. That said, he acknowledges that consumer neuroscience is not able to identify a brain button that would alter choice through manipulated neurostimulation. However, since human brains are similar across people, the depth of neural insights that are consistent across a small sample of 40 respondents may generate greater insights than conventional marketing research with 2000 respondents. The cost of neuroscience will further decrease with gains in reliability, validity, and generalizability, particularly if augmented with bodily measures. He acknowledges that the theoretical side has developed more slowly than applications, particularly applications that are supported by sponsoring organizations more satisfied with local insights than general models. [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]

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]

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]

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 MethodsPhilosophy > Foundations of Cognitive Science [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]

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]

The traditional memory-systems view is that explicit (conscious) long-term memory is associated with the hippocampus and implicit (nonconscious) memory is associated with non-hippocampal brain regions. This special issue of Cognitive Neuroscience focuses on whether the hippocampus is associated with implicit memory. An empirical fMRI paper by Miller, Kennard, Gowland, Antoniades, and Rosenthal (this issue) evaluated recognition memory performance of autobiographical amnesia patients with bilateral damage to hippocampal sub-region CA3 and found they had greater than chance recognition memory performance for spatial sequence learning, spatial item learning-same location, and non-spatial item learning, but chance performance for non-spatial sequence learning and spatial item learning-different location. These results are at odds with the view that the hippocampus is generally involved in sequence learning and complex event recognition. A discussion paper by Steinkrauss and Slotnick (this issue) considered whether fMRI studies have provided evidence that the hippocampus is associated with implicit memory. It was argued that all previous studies have been confounded by explicit memory, attentional states, stimuli, or novelty. This discussion paper is followed by commentaries from Hannula (this issue), Henke and Ruch (this issue), Rosenthal (this issue), Spaak (this issue), Thakral et al. (this issue), and Züst (this issue). The articles in this special issue illustrate that the association between the hippocampus and implicit memory is under active investigation and debate. It is hoped that the evidence and discourse in this issue will provide directions for future research. [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 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 introduces a novel controlled adaptive mental causal network model addressing how dreams overnight can influence creativity in waking life. The network model depicts in a causal, dynamic, and generic manner which adaptive mental processes underlie the connection between dreams and creativity and is shown to be validated with the existing cognitive neuroscience literature. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to rehabilitate the notion of role by linking sociological role theory to recent work on motivational, affective, and cognitive neuroscience specifying the internal mechanisms behind motivated action. We argue that there is nothing inherently problematic or retrogressive in the idea of "role," once its link to a purely normative account of motivated action is severed. Instead, by conceptualizing roles as emerging and persisting around structured reward systems, we are able to incorporate contemporary motivational science such that rewards become the proximate causal mechanisms currently missing in role theory. Consequently, a key implication of our argument is that the best way to link role, action, and structures is by reviving the idea of institutions as literal reward systems, which allows us to envision roles as the mechanisms via which the pursuit and delivery of rewards and goal‐objects are routinized. Implications for a motivational theory of roles, rewards, and institutions is discussed. [ABSTRACT FROM AUTHOR]

There are different views in the literature about the number and inter-relationships of cognitive domains (such as memory and executive function) and a lack of understanding of the cognitive processes underlying these domains. In previous publications, we demonstrated a methodology for formulating and testing cognitive constructs for visuo-spatial and verbal recall tasks, particularly for working memory task difficulty where entropy is found to play a major role. In the present paper, we applied those insights to a new set of such memory tasks, namely, backward recalling block tapping and digit sequences. Once again, we saw clear and strong entropy-based construct specification equations (CSEs) for task difficulty. In fact, the entropy contributions in the CSEs for the different tasks were of similar magnitudes (within the measurement uncertainties), which may indicate a shared factor in what is being measured with both forward and backward sequences, as well as visuo-spatial and verbal memory recalling tasks more generally. On the other hand, the analyses of dimensionality and the larger measurement uncertainties in the CSEs for the backward sequences suggest that caution is needed when attempting to unify a single unidimensional construct based on forward and backward sequences with visuo-spatial and verbal memory tasks. [ABSTRACT FROM AUTHOR]

The vigilance decrement or decline in signal detection performance with time on task is one of the most reliable findings in the cognitive neuroscience and psychology literatures. The majority of theories proposed to explain the decrement are limited cognitive or attention resource based theories; the central nervous system is a limited capacity processor. The decrement in performance is then due to resource reallocation (or misallocation), resource depletion or some combination of both mechanisms. The role of resource depletion, in particular, is hotly debated. However, this may be due to a lack of understanding of the renewable nature of the vigilance resources and how this renewal process impacts performance during vigilance tasks. In the present paper, a simple quantitative model of vigilance resource depletion and renewal is described and shown to generate performance data similar to results seen in both humans and spiders. This model clarifies the role resource depletion and resource renewal may play in vigilance in both people and other animals. [ABSTRACT FROM AUTHOR]

This paper describes the development and validation of 3D Affective Virtual environments and Event Library (AVEL) for affect induction in Virtual Reality (VR) settings with an online survey; a cost-effective method for remote stimuli validation which has not been sufficiently explored. Three virtual office-replica environments were designed to induce negative, neutral and positive valence. Each virtual environment also had several affect inducing events/objects. The environments were validated using an online survey containing videos of the virtual environments and pictures of the events/objects. They survey was conducted with 67 participants. Participants were instructed to rate their perceived levels of valence and arousal for each virtual environment (VE), and separately for each event/object. They also rated their perceived levels of presence for each VE, and they were asked how well they remembered the events/objects presented in each VE. Finally, an alexithymia questionnaire was administered at the end of the survey. User ratings were analysed and successfully validated the expected affect and presence levels of each VE and affect ratings for each event/object. Our results demonstrate the effectiveness of the online validation of VE material in affective and cognitive neuroscience and wider research settings as a good scientific practice for future affect induction VR studies. [ABSTRACT FROM AUTHOR]

Although the role played by finger use in children's numerical development has been widely investigated, their benefit in arithmetical contexts is still debated today. This scoping review aimed to systematically identify and summarize all studies that have investigated the relation between fingers and arithmetic skills in children. An extensive search on Ovid PsycINFO and Ovid Eric was performed. The reference lists of included articles were also searched for relevant articles. Two reviewers engaged in study selection and data extraction independently, based on the eligibility criteria. Discrepancies were resolved through discussion. Of the 4707 identified studies, 68 met the inclusion criteria and 7 additional papers were added from the reference lists of included studies. A total of 75 studies were included in this review. They came from two main research areas and were conducted with different aims and methods. Studies published in the mathematical education field (n = 29) aimed to determine what finger strategies are used during development and how they support computation skills. Studies published in cognitive psychology and neuroscience (n = 45) specified the cognitive processes and neurobiological mechanisms underlying the fingers/arithmetic relation. Only one study combined issues raised in both research areas. More studies are needed to determine which finger strategy is the most effective, how finger sensorimotor skills mediate the finger strategies/arithmetic relation, and how they should be integrated into educational practice. [ABSTRACT FROM AUTHOR]

Cognitive effort is thought to be familiar in everyday life, ubiquitous across multiple variations of task and circumstance, and integral to cost/benefit computations that are themselves central to the proper functioning of cognitive control. In particular, cognitive effort is thought to be closely related to the assessment of cognitive control's costs. I argue here that the construct of cognitive effort, as it is deployed in cognitive psychology and neuroscience, is problematically unclear. The result is that talk of cognitive effort may paper over significant disagreement regarding the nature of cognitive effort, and its key functions for cognitive control. I highlight key points of disagreement, and several open questions regarding what causes cognitive effort, what cognitive effort represents, cognitive effort's relationship to action, and cognitive effort's relationship to consciousness. I also suggest that pluralism about cognitive effort—that cognitive effort may manifest as a range of intentional or non‐intentional actions the function of which is to promote greater success at paradigmatic cognitive control tasks—may be a fruitful and irenic way to conceive of cognitive effort. Finally, I suggest that recent trends in work on cognitive control suggests that we might fruitfully conceive of cognitive effort as one key node in a complex network of mental value, and that studying this complex network may illuminate the nature of cognitive control, and the role of consciousness in cognitive control's proper functioning. This article is categorized under:Philosophy > ConsciousnessPhilosophy > Psychological CapacitiesNeuroscience > Cognition [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]

Research on concepts has focused on categorization. Categorization starts with a stimulus. Equally important are episodes that start with a thought. We engage in thinking to draw out new consequences from stored information, or to work out how to act. Each of the concepts out of which thought is constructed provides access to a large body of stored information. Access is not always just a matter of retrieving a stored belief (semantic memory). Often it depends on running a simulation. Simulation allows conceptual thought to draw on information in special-purpose systems, information stored in special-purpose computational dispositions and special-purpose representational structures. While the utility of simulation, prospection or imagination is widely appreciated, the role of concepts in the process is not well understood. This paper turns to cognitive and computational neuroscience for a model of how simulations enable thinkers to reach novel conclusions. Carried over to conceptual thought, the model suggests that concepts are 'plug & play' devices. The distinctive power of thought-driven simulation derives from the ability of concepts to plug into two kinds of structure at once: the combinatorial structure of a thought at one end and special-purpose structural representations at the other. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'. [ABSTRACT FROM AUTHOR]

New technologies for monitoring grip forces during hand and finger movements in non-standard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlie perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip-force data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain-inspired neural network model that uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time and reliably captures the differences between novice and expert performance in terms of grip-force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip-force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery). [ABSTRACT FROM AUTHOR]

The experimental method has promoted the popularity of neuroscientific research on the human mind. In this interdisciplinary enterprise, the experimental method, with its roots in natural science and experimental psychology, is often uncritically accepted as the royal road to investigate the human mind not only by neuroscientists, but by many philosophers as well, especially those inclined to some form of naturalism. It is rarely disputed that experiments reveal actual states of nature (here: of mind and/or brain). Experimental results are used to picture the human person or subject as an illusionary construct resulting from neuronal interactions. The present paper sketches some of the limitations of neuroscientific experiments in order to demonstrate that cognitive neuroscience is far from relying on firm methodological grounds. Numerous issues still have to be solved, some of which date back to the early days of modern science. At least, to make experiments work, many theoretical presuppositions have to be accepted and decisions of relevance have to be made in the scientific process. This implies that all scientific endeavor is constituted by persons making free decisions for good reasons, despite all reductionist claims to the contrary. The fact that we as scientists have to distinguish relevant from irrelevant aspects of experimental procedures is also crucial for dealing with the current replicability crisis in the life sciences including neuroscience. [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 Special Feature explores the various purposes served by sleep, describing current attempts to understand how the many functions of sleep are instantiated in neural circuits and cognitive structures. Our feature reflects current experts' opinions about, and insights into, the dynamic processes of sleep. In the last few decades, technological advances have supported the updated view that sleep plays an active role in both cognition and health. However, these roles are far from understood. This collection of articles evaluates the dynamic nature of sleep, how it evolves across the lifespan, becomes a competitive arena for memory systems through the influence of the autonomic system, supports the consolidation and integration of new memories, and how lucid dreams might originate. This set of papers highlights new approaches and insights that will lay the groundwork to eventually understand the full range of functions supported by sleep. [ABSTRACT FROM AUTHOR]

This paper delves into the concept of archetypes, universal patterns of behavior and cognition, and proposes a novel tripartite model distinguishing between structural, regulatory, and representational archetypes. Drawing on insights from code biology, neuroscience, genetics, and epigenetics, the model provides a nuanced framework for understanding archetypes and their role in shaping cognition and behavior. The paper also explores the interplay between these elements to express representational archetypes. Furthermore, it addresses the informational capacity of the genome and its influence on post-natal development and the psyche. The paper concludes by discussing the future trajectory of psychology, emphasizing the need for an integrative approach that combines our understanding of social constructs with insights into our inherent organizational propensities or archetypes. This exploration holds the potential to advance our understanding of the human condition. [ABSTRACT FROM AUTHOR]

Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that the central tendency effect is stronger in social than in non-social task settings. The particular nature of such behavioral changes induced by social interaction, and their underlying cognitive processes in the human brain are, however, still not well understood. In this paper, we address this question by training an artificial neural network inspired by the predictive coding theory on the above behavioral data set. Using this computational model, we investigate whether the change in behavior that was caused by the situational context in the human experiment could be explained by continuous modifications of a parameter expressing how strongly sensory and prior information affect perception. We demonstrate that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals, indicating that social and non-social task settings might in fact exist on a continuum. At the same time, an analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions. Our results emphasize the importance of computational replications of behavioral data for generating hypotheses on the underlying cognitive mechanisms of shared perception and may provide inspiration for follow-up studies in the field of neuroscience. [ABSTRACT FROM AUTHOR]

Cross–writing system research in psychology and cognitive neuroscience has yielded important findings regarding how a writing system's structure can influence the cognitive challenges of learning to read and the neural underpinnings of literacy. The current paper reviews these differences and extends the findings to demonstrate diversity in how skilled reading is accomplished within a single writing system, English. We argue that broad clusters of behavioral and neural patterns found across writing systems can also be found within subpopulations who display atypical routes to skilled English reading, subpopulations including Chinese–English bilinguals, deaf native signers, compensated readers, and distortion‐sensitive readers. The patterns of interest include a tradeoff between the degree of reliance on phonological and morphological processing for skilled reading, a shift in attentional focus from smaller to larger orthographic units, and enhanced bilaterality of neural processing during word reading. Lastly, we consider how understanding atypical routes to reading may apply to other writing systems. [ABSTRACT FROM AUTHOR]

This paper examines consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. We propose a formal TCS model, the Conscious Turing Machine (CTM). The CTM is influenced by Alan Turing's simple yet powerful model of computation, the Turing machine (TM), and by the global workspace theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeux, George Mashour, and others. Phenomena generally associated with consciousness, such as blindsight, inattentional blindness, change blindness, dream creation, and free will, are considered. Explanations derived from the model draw confirmation from consistencies at a high level, well above the level of neurons, with the cognitive neuroscience literature. [ABSTRACT FROM AUTHOR]

The role that the second-person perspective can play in cognition has been largely ignored. Pérez and Gomila have recently carefully articulated a novel approach to the understanding of our mind-reading capacity based on second-person interactions, and argued that the second-person perspective is conceptually, phylogenetically and ontogenetically prior to the first- and the third-person perspective. In this paper I review their reasonings, and argue that their second-person interactions depend indeed on the firstperson perspective that conscious experiences offer us. Moreover, I put forward a characterization of such a first-person perspective showing that it should not be understood in terms of privileged access. This in turn mitigates the authors reluctance to consider such a perspective in the first place, while making room for most of their reasons to emphasize the role of face-to-face interactions in understanding cognition. [ABSTRACT FROM AUTHOR]

In Social Cognition and the Second Person in Human Interaction (2021), Routledge, Diana Pe'rez and Antoni Gomila articulate a complex web of conceptual and empirical explorations that altogether make a remarkable case for the primitiveness and distinctiveness of second-person mental attributions. I find their case for the primitiveness of second-person attributions quite convincing and deeply rooted in their philosophical project, whose specific nature I will examine in the first section. The rest of the paper will then focus on their case for the distinctiveness of those mental attributions that are made in second-person interactions. My conclusion will be that Pe'rez and Gomila's various strategies to motivate the distinctiveness of such attributions are ultimately unsatisfactory. [ABSTRACT FROM AUTHOR]

In social and service robotics, complex collaborative tasks are expected to be executed while interacting with humans in a natural and fluent manner. In this scenario, the robotic system is typically provided with structured tasks to be accomplished, but must also continuously adapt to human activities, commands, and interventions. We propose to tackle these issues by exploiting the concept of cognitive control, introduced in cognitive psychology and neuroscience to describe the executive mechanisms needed to support adaptive responses and complex goal‐directed behaviors. Specifically, we rely on a supervisory attentional system to orchestrate the execution of hierarchically organized robotic behaviors. This paradigm seems particularly effective not only for flexible plan execution but also for human–robot interaction, because it directly provides attention mechanisms considered as pivotal for implicit, non‐verbal human–human communication. Following this approach, we are currently developing a robotic cognitive control framework enabling collaborative task execution and incremental task learning. In this paper, we provide a uniform overview of the framework illustrating its main features and discussing the potential of the supervisory attentional system paradigm in different scenarios where humans and robots have to collaborate for learning and executing everyday activities. We provide a uniform overview of a robotic cognitive control framework discussing the potential of the proposed approach in different scenarios where humans and robots collaborate for learning and executing everyday activities. [ABSTRACT FROM AUTHOR]

This commentary reflects on the discussions of Whitall et al.'s paper "Motor Development Research: II. The First Two Decades of the 21st Century Shaping Our Future." Comments focus on (a) the emergence and importance of the Developmental Systems approach to motor development, (b) the perceived ambiguity between Dynamic and Developmental Systems approaches, and (c) a case for the evolution of Developmental Motor Neuroscience from the field of Developmental Cognitive Neuroscience. [ABSTRACT FROM AUTHOR]

This paper analyzes notions and models of optimized cognition emerging at the intersections of psychology, neuroscience, and computing. What I somewhat polemically call the algorithms of mindfulness describes an ideal that determines algorithmic techniques of the self, geared at emotional resilience and creative cognition. A reframing of rest, exemplified in corporate mindfulness programs and the design of experimental artificial neural networks sits at the heart of this process. Mindfulness trainings provide cues as to this reframing, for they detail each in their own way how intermittent periods of rest are to be recruited to augment our cognitive capacities and combat the effects of stress and information overload. They typically rely on and co-opt neuroscience knowledge about what the brains of North Americans and Europeans do when we rest. Current designs for artificial neural networks draw on the same neuroscience research and incorporate coarse principles of cognition in brains to make machine learning systems more resilient and creative. These algorithmic techniques are primarily conceived to prevent psychopathologies where stress is considered the driving force of success. Against this backdrop, I ask how machine learning systems could be employed to unsettle the concept of pathological cognition itself. [ABSTRACT FROM AUTHOR]

The attentional blink (AB) phenomenon reveals a bottleneck of human information processing: the second of two targets is often missed when they are presented in rapid succession among distractors. In our previous work, we showed that the size of the AB can be changed by applying transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) (London & Slagter, Journal of Cognitive Neuroscience, 33, 756–68, 2021). Although AB size at the group level remained unchanged, the effects of anodal and cathodal tDCS were negatively correlated: if a given individual's AB size decreased from baseline during anodal tDCS, their AB size would increase during cathodal tDCS, and vice versa. Here, we attempted to replicate this finding. We found no group effects of tDCS, as in the original study, but we no longer found a significant negative correlation. We present a series of statistical measures of replication success, all of which confirm that both studies are not in agreement. First, the correlation here is significantly smaller than a conservative estimate of the original correlation. Second, the difference between the correlations is greater than expected due to sampling error, and our data are more consistent with a zero-effect than with the original estimate. Finally, the overall effect when combining both studies is small and not significant. Our findings thus indicate that the effects of lDPLFC-tDCS on the AB are less substantial than observed in our initial study. Although this should be quite a common scenario, null findings can be difficult to interpret and are still under-represented in the brain stimulation and cognitive neuroscience literatures. An important auxiliary goal of this paper is therefore to provide a tutorial for other researchers, to maximize the evidential value from null findings. [ABSTRACT FROM AUTHOR]

Much work has been conducted in the social psychological sciences both modelling and predicting how the storage and retrieval of images and words in the mind operate (e.g. Baddeley 1974, 2000, Damasio 1999, Barsalou 1999). The focus has largely been on the interactions between short-term and long-term regions of memory. Such studies have also on occasion been complemented by behavioural experiments. More recently, a growing body of work has started to emerge from the biological cognitive neurosciences which looks at these same processes with the aid of scanning technologies (e.g. Dehaene 2003, 2009, Ledoux 1998, Eichenbaum 2011). The questions that will be considered in this paper are can these scientific findings be extended to aesthetic objects that are studied in the humanities, and in particular to the style of literary texts, and also can the way literary style figures operate shed light on how the mind and brain might function. [ABSTRACT FROM AUTHOR]

Using an adaptation of the PRISMA checklist, this interdisciplinary systematic literary review synthesizes forty peer-reviewed research papers and neuropsychological theoretical publications about the bilingual cognitive advantage debate. The objectives of this study were to (1) analyze the neurocognitive and methodological discussion behind bilingualism studies, and (2) explore how a better understanding of cultural context and colonialism can help contribute to the bilingual cognitive advantage debate. Research with quantitative and qualitative designs were considered, as long as they studied the bilingual population (regardless of what languages the participants knew or their age). Although most papers were from the field of cognitive neurosciences, articles from different scientific disciplines were considered for analysis. Results showed that there doesn't appear to be a consensus on bilinguals holding a significant cognitive advantage over monolinguals, yet some studies found that bilinguals had higher scores in inhibitory control and cognitive flexibility. While bilinguals show an increase in grey matter and activity in areas of the frontal lobe, the precise mechanisms of control between languages have yet to be identified. However, most of these studies did not have diverse samples and showed methodological discrepancies when measuring bilingualism. Although heritage language of immigrated people is a known factor in this area of study, linguistic cultural sensibilities of colonized lands haven't been properly assessed in the cognitive advantage debate. Based on these findings, a research proposal and scale regarding executive functioning and the perceptions of Puerto Rican bilingualism are in development. [ABSTRACT FROM AUTHOR]

Advances in Mobile Brain/Body Imaging (MoBI) technology allows for real‐time measurements of human brain dynamics during every day, natural, real‐life situations. This special issue Time to Move brings together a collection of experimental papers, targeted reviews and opinion articles that lay out the latest MoBI findings. A wide range of topics across different fields are covered including art, athletics, virtual reality, and mobility. What unites these diverse topics is the common goal to enhance and restore human abilities by reaching a better understanding on how cognition is implemented by the brain‐body relationship. The breadth and novelty of paradigms and findings reported here positions MoBI as a new frontier in the field of human cognitive neuroscience. [ABSTRACT FROM AUTHOR]