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Formal linguistic competence (getting the form of language right) and functional linguistic competence (using language to accomplish goals in the world) are distinct cognitive skills. The human brain contains a network of areas that selectively support language processing (formal linguistic competence), but not other domains like logical or social reasoning (functional linguistic competence). In the late 2010s, large language models trained on word prediction tasks began achieving unprecedented success in formal linguistic competence, showing impressive performance on linguistic tasks that likely require hierarchy and abstraction. Consistent performance on tasks requiring functional linguistic competence is harder to achieve for large language models and often involves augmentations beyond next word prediction. Evidence from cognitive science and neuroscience can illuminate the capabilities and limitations of large language models and pave the way toward better, human-like models of both language and thought. Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of separate mechanisms specialized for formal versus functional linguistic competence. [ABSTRACT FROM AUTHOR]

This article is a commentary on a previous study that proposed that spatial cell types in the brain could be the neural ingredients for the Language of Thought (LoT). The LoT is a theory about how the mind works, suggesting that thoughts are composed of primitive representations stored in a repertoire. The author of this commentary argues against this idea, presenting four main arguments. First, spatial cells show remapping behavior that is inconsistent with the stable repertoire required by the LoT. Second, the linking hypothesis does not address how specific spatial locations, or fillers, are represented in the brain. Third, the compositionality of the LoT is not accounted for in the proposed model. Finally, the author suggests that spatial cell populations do not offer a sufficient system for the LoT and that further mechanisms are necessary. Overall, the author concludes that while spatial cells may play a role in the implementation of the LoT, they are not the neural ingredients themselves. [Extracted from the article]

Remembering objects and their associated location (object–location memory; OLM), is a fundamental cognitive function, mediated by cortical and subcortical brain regions. Previously, the combination of OLM training and transcranial direct current stimulation (tDCS) suggested beneficial effects, but the evidence remains heterogeneous. Here, we applied focal tDCS over the right temporoparietal cortex in 52 participants during a two-day OLM training, with anodal tDCS (2 mA, 20 min) or sham (40 s) on the first day. The focal stimulation did not enhance OLM performance on either training day (stimulation effect: −0.09, 95%CI: [−0.19; 0.02], p = 0.08). Higher electric field magnitudes in the target region were not associated with individual performance benefits. Participants with content-related learning strategies showed slightly superior performance compared to participants with position-related strategies. Additionally, training gains were associated with individual verbal learning skills. Consequently, the lack of behavioral benefits through focal tDCS might be due to the involvement of different cognitive processes and brain regions, reflected by participant's learning strategies. Future studies should evaluate whether other brain regions or memory-relevant networks may be involved in the modulation of object–location associations, investigating other target regions, and further exploring individualized stimulation parameters. [ABSTRACT FROM AUTHOR]

Oscillations serve a critical role in organizing biological systems. In the brain, oscillatory coupling is a fundamental mechanism of communication. The possibility that neural oscillations interact directly with slower physiological rhythms (e.g., heart rate, respiration) is largely unexplored and may have important implications for psychological functioning. Oscillations in heart rate, an aspect of heart rate variability (HRV), show remarkably robust associations with psychological health. Mather and Thayer proposed coupling between high-frequency HRV (HF-HRV) and neural oscillations as a mechanism that partially accounts for such relationships. We tested this hypothesis by measuring phase-amplitude coupling between HF-HRV and neural oscillations in 37 healthy adults at rest. Robust coupling was detected in all frequency bands. Granger causality analyses indicated stronger heart-to-brain than brain-to-heart effects in all frequency bands except gamma. These findings suggest that cardiac rhythms play a causal role in modulating neural oscillations, which may have important implications for mental health. [ABSTRACT FROM AUTHOR]

Atypical orienting of visuospatial attention in autistic individuals or individuals with a high level of autistic-like traits (ALTs) has been well documented and viewed as a core feature underlying the development of autism. However, there has been limited testing of three alternative theoretical positions advanced to explain atypical orienting – difficulty in disengagement, cue indifference, and delay in orienting. Moreover, research commonly has not separated facilitation (reaction time difference between neutral and valid cues) and cost effects (reaction time difference between invalid and neutral cues) in orienting tasks. We addressed these limitations in two experiments that compared groups selected for Low- and High-ALT levels on exogenous and endogenous versions of the Posner cueing paradigm. Experiment 1 showed that High-ALT participants exhibited a significantly reduced cost effect compared to Low-ALT participants in the endogenous cueing task, although the overall orienting effect remained small. In Experiment 2, we increased task difficulty of the endogenous task to augment cueing effects. Results were comparable to Experiment 1 regarding the finding of a reduced cost effect for High-ALT participants on the endogenous cueing task and additionally demonstrated a reduced facilitation effect in High-ALT participants on the same task. No ALT group differences were observed on an exogenous cueing task included in Experiment 2. These findings suggest atypical orienting in High-ALT individuals may be attributable to general cue indifference, which implicates differences in top-down attentional processes between Low- and High-ALT individuals. We discuss how indifference to endogenous cues may contribute to social cognitive differences in autism. [ABSTRACT FROM AUTHOR]

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance–covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4‐way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance–covariance matrices. Voxel‐wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance–covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance–covariance structure using LME modeling. [ABSTRACT FROM AUTHOR]

The first 2 years of life is a period of rapid learning and growth when humans acquire notable motor, perceptual, linguistic, and social capabilities. Because of challenges in measuring brain activity in human infants, many of the neural mechanisms underlying these capabilities have not been identified. Advances in neuroimaging technologies, task designs, and data analyses from studies of older children and adults are starting to be translated into studies of infants. These new methods include frequency tagging designs in electroencephalography (EEG), task-based functional magnetic resonance imaging (fMRI) studies in awake and behaving infants, multivariate analyses of EEG and functional near-infrared spectroscopy (fNIRS), and the emergence of infant-friendly optically pumped magnetometry (OPM). The resulting neural data provide rich new measures of infant cognition, supplementing the limited behavioral data that can be obtained from preverbal infants. The functional properties of the infant brain are poorly understood. Recent advances in cognitive neuroscience are opening new avenues for measuring brain activity in human infants. These include novel uses of existing technologies such as electroencephalography (EEG) and magnetoencephalography (MEG), the availability of newer technologies including functional near-infrared spectroscopy (fNIRS) and optically pumped magnetometry (OPM), and innovative applications of functional magnetic resonance imaging (fMRI) in awake infants during cognitive tasks. In this review article we catalog these available non-invasive methods, discuss the challenges and opportunities encountered when applying them to human infants, and highlight the potential they may ultimately hold for advancing our understanding of the youngest minds. [ABSTRACT FROM AUTHOR]

The nested data structure is prevalent for cognitive measure experiments due to repeatedly taken observations from different brain locations within subjects. The analysis methods used for this data type should consider the dependency structure among the repeated measurements. However, the dependency assumption is mainly ignored in the cognitive neuroscience data analysis literature. We consider both statistical, and machine learning methods extended to repeated data analysis and compare distinct algorithms in terms of their advantage and disadvantages. Unlike basic algorithm comparison studies, this article analyzes novel neuroscience data considering the dependency structure for the first time with several statistical and machine learning methods and their hybrid forms. In addition, the fitting performances of different algorithms are compared using contaminated data sets, and the cross-validation approach. One of our findings suggests that the GLMM tree, including random term indices indicating the location of functional near-infrared spectroscopy optodes nested within experimental units, shows the best predictive performance with the lowest MSE, RMSE, and MAE model performance metrics. However, there is a trade-off between accuracy and speed since this algorithm is required the highest computational time. [ABSTRACT FROM AUTHOR]

Background: Restriction of food intake is a central pathological feature of anorexia nervosa (AN). Maladaptive eating behavior and, specifically, limited intake of calorie-dense foods are resistant to change and contribute to poor long-term outcomes. This study is a preliminary examination of whether change in food choices during inpatient treatment is related to longer-term clinical course. Methods: Individuals with AN completed a computerized Food Choice Task at the beginning and end of inpatient treatment to determine changes in high-fat and self-controlled food choices. Linear regression and longitudinal analyses tested whether change in task behavior predicted short-term outcome (body mass index [BMI] at discharge) and longer-term outcome (BMI and eating disorder psychopathology). Results: Among 88 patients with AN, BMI improved significantly with hospital treatment (p < 0.001), but Food Choice Task outcomes did not change significantly. Change in high-fat and self-controlled choices was not associated with BMI at discharge (r = 0.13, p = 0.22 and r = 0.10, p = 0.39, respectively). An increase in the proportion of high-fat foods selected (β = 0.91, p = 0.02) and a decrease in the use of self-control (β = −1.50, p = 0.001) predicted less decline in BMI over 3 years after discharge. Conclusions: Short-term treatment is associated with improvement in BMI but with no significant change, on average, in choices made in a task known to predict actual eating. However, the degree to which individuals increased high-fat choices during treatment and decreased the use of self-control over food choice were associated with reduced weight loss over the following 3 years, underscoring the need to focus on changing eating behavior in treatment of AN. [ABSTRACT FROM AUTHOR]

Our daily experiences unfold continuously, but we remember them as a series of discrete events through a process called event segmentation. Prominent theories of event segmentation suggest that event boundaries in memory are triggered by significant shifts in the external environment, such as a change in one's physical surroundings. In this review, we argue for a fundamental extension of this research field to also encompass internal state changes as playing a key role in structuring event memory. Accordingly, we propose an expanded taxonomy of event boundary-triggering processes, and review behavioral and neuroscience research on internal state changes in three core domains: affective states, goal states, and motivational states. Finally, we evaluate how well current theoretical frameworks can accommodate the unique and interactive contributions of internal states to event memory. We conclude that a theoretical perspective on event memory that integrates both external environment and internal state changes allows for a more complete understanding of how the brain structures experiences, with important implications for future research in cognitive and clinical neuroscience. [ABSTRACT FROM AUTHOR]

Introduction: Believing comprises multifaceted processes that integrate information from the outside world through meaning‐making processes with personal relevance. Methods: Qualitative Review of the current literature in social cognitive neuroscience. Results: Although believing develops rapidly outside an individual's conscious awareness, it results in the formation of beliefs that are stored in memory and play an important role in determining an individual's behavior. Primal beliefs reflect an individual's experience of objects and events, whereas conceptual beliefs are based on narratives that are held in social groups. Conceptual beliefs can be about autobiographical, political, religious, and other aspects of life and may be encouraged by participation in group rituals. We hypothesize that assertions of future gains and rewards that transcend but are inherent in these codices provide incentives to follow the norms and rules of social groups. Conclusion: The power of conceptual beliefs to provide cultural orientation is likely to fade when circumstances and evidence make it clear that what was asserted no longer applies. [ABSTRACT FROM AUTHOR]

The article discusses the concept of predictive processing in neuroscience and its implications for understanding perception, action, and clinical disorders. It challenges the traditional view of the brain as a passive receiver of sensory information and instead proposes that the brain actively constructs our experience of the world based on background beliefs and expectations. The article also explores the idea of active inference, which suggests that both perception and action aim to reduce prediction error. The author highlights the practical consequences of understanding predictive processing, including its relevance to clinical disorders and the optimization of our own lives. The article concludes by acknowledging the potential limitations of the predictive processing framework and the need for further scientific exploration. [Extracted from the article]

This article argues that every performance of habit‐driven action requires attention. I begin by revisiting the conception of habit‐driven actions as reducible to automatically performed responses to stimuli. On this conception, habitual actions are a counterexample to Wayne Wu's action‐centered theory of attention. Using the biased competition model of attention, and building on findings from affective cognitive neuroscience, I challenge this position. I claim that the performance of a habitual action requires experiential history to be exerting an influence that is best understood as implicit selection‐biasing. It follows from this that habit‐driven action is compatible with Wu's theory. [ABSTRACT FROM AUTHOR]

The understanding of artifacts and biological phenomena has often influenced each other. This work argues that at the core of these epistemic bridges there are shared teleological notions and explanations manifested in analogies between artifacts and biological phenomena. To this end, I first propose a focus on the logical structure of minimal teleological explanations, which renders said epistemic bridges more evident than an ontological or metaphysical approach to teleology, and which can be used to describe scientific practices in different areas by virtue of formal generality and minimalism (section 2). Second, I show how this approach highlights some epistemic features shared by the understanding of artifacts and biological phenomena, like a specific kind of epistemic circularity, and how functional analogies between artifacts and biological phenomena translate such epistemic circularity from one domain to the other (section 3). Third, I conduct a case study on the scientific practice around the brain's "compass", showing how the understanding of artifacts influences purpose ascription and measurement, and frames mechanisms in biology, especially in areas where purpose ascription is most difficult, like cognitive neuroscience (sections 4 and 5). [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]

Schizophrenia often occurs during youth, and psychosis risk syndrome occurs before the onset of psychosis. The aim of this study was to determine whether the visual event-related potential responses in youths with psychosis risk syndrome were defective in the presence of interference stimuli and associated with their clinical outcomes. A total of 223 participants, including 122 patients with psychosis risk syndrome, 50 patients with emotional disorders, and 51 healthy control subjects, were assessed. Baseline EEG was recorded during the three-stimulus visual oddball task. The event-related potentials induced by square pictures with different colors were measured. Almost all patients with psychosis risk syndrome were followed up for 12 months and were reclassified into three subgroups: conversion, symptomatic, and remission. The differences in baseline event-related potential responses were compared among the clinical outcome subgroups. The average N2 amplitude of the psychosis risk syndrome group was significantly less negative than that in the healthy control group (d=0.53). The baseline average N2 amplitude in the conversion subgroup was significantly less negative than that in the symptomatic (d=0.58) and remission (d=0.50) subgroups and in the healthy control group (d=0.97). The average N2 amplitude did not differ significantly between the symptomatic and remission subgroups (d=0.02). However, it was significantly less negative in the symptomatic and remission subgroups than in the healthy control group (d=0.46 and d=0.38). No statistically significant results were found in the P3 response. Youths with psychosis risk syndrome had significant N2 amplitude defects in attention processing with interference stimuli. N2 amplitude shows potential as a prognostic biomarker of clinical outcome in the psychosis risk syndrome. [ABSTRACT FROM AUTHOR]

Neural representations are internal brain states that constitute the brain's model of the external world or some of its features. In the presence of sensory input, a representation may reflect various properties of this input. When perceptual information is no longer available, the brain can still activate representations of previously experienced episodes due to the formation of memory traces. In this review, we aim at characterizing the nature of neural memory representations and how they can be assessed with cognitive neuroscience methods, mainly focusing on neuroimaging. We discuss how multivariate analysis techniques such as representational similarity analysis (RSA) and deep neural networks (DNNs) can be leveraged to gain insights into the structure of neural representations and their different representational formats. We provide several examples of recent studies which demonstrate that we are able to not only measure memory representations using RSA but are also able to investigate their multiple formats using DNNs. We demonstrate that in addition to slow generalization during consolidation, memory representations are subject to semantization already during short-term memory, by revealing a shift from visual to semantic format. In addition to perceptual and conceptual formats, we describe the impact of affective evaluations as an additional dimension of episodic memories. Overall, these studies illustrate how the analysis of neural representations may help us gain a deeper understanding of the nature of human memory. [ABSTRACT FROM AUTHOR]

Cognitive neuroscience of diseases faces challenges dealing with complex structure–function associations, disease phenotype heterogeneity, lack of transdiagnostic models, and oversimplified cognitive approaches. Synergetics offers a unified framework to progressively tackle these challenges, emphasizing the complex dynamics of brain, body, and environmental interactions. We review recent advances in neuroscience, focusing on low-dimensional spatiotemporal hierarchies, whole-brain modeling and perturbation, interlevel integration, and naturalistic frameworks. The future of synergetics depends on handling variability, incorporating environmental factors, integrating more levels of analysis, and accessing large multimodal data sets, setting the stage for a transformative approach in clinical neuroscience. Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure–function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches. [ABSTRACT FROM AUTHOR]

Machine learning techniques have proven to be a useful tool in cognitive neuroscience. However, their implementation in scalp‐recorded electroencephalography (EEG) is relatively limited. To address this, we present three analyses using data from a previous study that examined event‐related potential (ERP) responses to a wide range of naturally‐produced speech sounds. First, we explore which features of the EEG signal best maximize machine learning accuracy for a voicing distinction, using a support vector machine (SVM). We manipulate three dimensions of the EEG signal as input to the SVM: number of trials averaged, number of time points averaged, and polynomial fit. We discuss the trade‐offs in using different feature sets and offer some recommendations for researchers using machine learning. Next, we use SVMs to classify specific pairs of phonemes, finding that we can detect differences in the EEG signal that are not otherwise detectable using conventional ERP analyses. Finally, we characterize the timecourse of phonetic feature decoding across three phonological dimensions (voicing, manner of articulation, and place of articulation), and find that voicing and manner are decodable from neural activity, whereas place of articulation is not. This set of analyses addresses both practical considerations in the application of machine learning to EEG, particularly for speech studies, and also sheds light on current issues regarding the nature of perceptual representations of speech. This work is a methodological contribution to the growing field of applying machine learning techniques to EEG data. We compare different approaches to EEG decoding and offer recommendations for best practices to researchers who are interested in using classification analyses. Then, we demonstrate how these techniques can be applied to questions in spoken language processing, providing theoretical insights into the nature of perceptual representations of speech that conventional analysis methods have been unable to address. [ABSTRACT FROM AUTHOR]

An intriguing question in cognitive neuroscience is whether alpha oscillations shape how the brain transforms the continuous sensory inputs into distinct percepts. According to the alpha temporal resolution hypothesis, sensory signals arriving within a single alpha cycle are integrated, whereas those in separate cycles are segregated. Consequently, shorter alpha cycles should be associated with smaller temporal binding windows and higher temporal resolution. However, the evidence supporting this hypothesis is contentious, and the neural mechanisms remain unclear. In this review, we first elucidate the alpha temporal resolution hypothesis and the neural circuitries that generate alpha oscillations. We then critically evaluate study designs, experimental paradigms, psychophysics, and neurophysiological analyses that have been employed to investigate the role of alpha frequency in temporal binding. Through the lens of this methodological framework, we then review evidence from between-subject, within-subject, and causal perturbation studies. Our review highlights the inherent interpretational ambiguities posed by previous study designs and experimental paradigms and the extensive variability in analysis choices across studies. We also suggest best practice recommendations that may help to guide future research. To establish a mechanistic role of alpha frequency in temporal parsing, future research is needed that demonstrates its causal effects on the temporal binding window with consistent, experimenter-independent methods. [ABSTRACT FROM AUTHOR]

For decades, it has been shown that alpha frequency is related to temporal binding window, and currently, such is the mainstream viewpoint [Noguchi, Y. Individual differences in beta frequency correlate with the audio–visual fusion illusion. Psychophysiology, 59, e14041, 2022; Gray, M. J., & Emmanouil, T. A. Individual alpha frequency increases during a task but is unchanged by alpha‐band flicker. Psychophysiology, 57, e13480, 2020; Hirst, R. J., McGovern, D. P., Setti, A., Shams, L., & Newell, F. N. What you see is what you hear: Twenty years of research using the sound-induced flash illusion. Neuroscience & Biobehavioral Reviews, 118, 759–774, 2020; Keil, J. Double flash illusions: Current findings and future directions. Frontiers in Neuroscience, 14, 298, 2020; Migliorati, D., Zappasodi, F., Perrucci, M. G., Donno, B., Northoff, G., Romei, V., & Costantini, M. Individual alpha frequency predicts perceived visuotactile simultaneity. Journal of Cognitive Neuroscience, 32, 1–11, 2020; Keil, J., & Senkowski, D. Individual alpha frequency relates to the sound-induced flash illusion. Multisensory Research, 30, 565–578, 2017; Minami, S., & Amano, K. Illusory jitter perceived at the frequency of alpha oscillations. Current Biology, 27, 2344–2351, 2017; Cecere, R., Rees, G., & Romei, V. Individual differences in alpha frequency drive crossmodal illusory perception. Current Biology, 25, 231–235, 2015]. However, recently, this stance has been challenged [Buergers, S., & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732–742, 2022]. Moreover, both stances appear to have their limitations regarding the reliability of results. Therefore, it is of paramount importance to develop new methodology to gain more reliable results. Perceptual training seems to be such a method that also offers significant practical implications. [ABSTRACT FROM AUTHOR]

Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural–functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural–functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural–functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility. [ABSTRACT FROM AUTHOR]

The daily habits and behaviors of modern society people harm the environment more than expected. It is well known that in order to protect the inherent values of the natural assets of the world we live in, and to sustain the mutual interaction of people with the environment in a productive manner, necessary precautions should be taken to establish and maintain pro-environmental behaviors. However, people act in different levels of proenvironmental behavior. Although most of the people state that they have a pro-environmental attitude, they cannot transform their attitudes into actual behaviors in their daily lives for various reasons. Examining the mechanisms underlying this phenomenon, known as the attitude-behavior gap, may contribute to the development of interventions that can help to understand how these behaviors differ among individuals and to eliminate this difference between the observed pro-environmental behaviors. At this point, neuroscience studies can provide a more comprehensive explanation of the reasons for the behavioral differences between these individuals, by comparing the performance of individuals with and without pro-environmental behaviors and those who have adopted and not adopting the environmentalist view, on behavioral tasks, and by simultaneously examining the physiological responses that may be related to sustainable behavior. This article will discuss how neuroscience and neuropsychological approach can provide an explanation for these questions through their unique methods. [ABSTRACT FROM AUTHOR]

Neuroscience heavily relies on animal welfare in laboratory rodents as it can significantly affect brain development, cognitive function and memory formation. Unfortunately, laboratory animals are often raised in artificial environments devoid of physical and social stimuli, potentially leading to biased outcomes in behavioural assays. To assess this effect, we examined the impact of social and physical cage enrichment on various forms of motor coordination. Our findings indicate that while enriched-housed animals did not exhibit faster learning in eyeblink conditioning, the peak timing of their conditioned responses was slightly, but significantly, improved. Additionally, enriched-housed animals outperformed animals that were housed in standard conditions in the accelerating rotarod and ErasmusLadder test. In contrast, we found no significant effect of enrichment on the balance beam and grip strength test. Overall, our data suggest that an enriched environment can improve motor performance and motor learning under challenging and/or novel circumstances, possibly reflecting an altered state of anxiety. [ABSTRACT FROM AUTHOR]

Neurofeedback training based on brain rhythm is appealing for both cognitive neuroscience and disorder treatment. However, electrodes that can integrate dual functions of recording and feedback without signal compromise still lack. Herein, an anisotropic cellulose carbon sponge (CCS) electrode by tailoring conductivity through the temperature that has the dual ability to record electrophysiological signals and sense pressures as feedback is developed. The electrode is fabricated via a gradient temperature annealing on CCS, which involves lower (300 °C) and higher (1000 °C) temperature treatments, namely CCS‐LT and CCS‐HT. CCS‐LT exhibits perpendicular resilience to the alignment direction of CCS, and CCS‐HT possesses lower sheet resistance (≈13 Ω sq−1) parallel to the alignment direction. As a pressure sensor, CCS‐LT can achieve excellent sensitivity (1.04 kPa−1), fast response time (27 ms), and reliable stability for 1000 cycles. CCS‐HT can serve as skin electrodes for electrophysiological sensing. The anisotropic functionality of annealed CCS allows it to be used in one sensing and regulation closed‐loop system driven by nerves, achieving accurate acquisition of human brain rhythm and machine movement information simultaneously. This not only helps rapid tracking of brain activity but also facilitates feedback regulation to enhance human brain function, having great potential applications in neurofeedback and disease rehabilitation. [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]

Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer a primer on defining properties of these modeling techniques. We then reflect on new modes of investigation in which LLMs can be used to reframe classic neuroscience questions to deliver fresh answers. We reason that LLMs have the potential to (1) enrich neuroscience datasets by adding valuable meta-information, such as advanced text sentiment, (2) summarize vast information sources to overcome divides between siloed neuroscience communities, (3) enable previously unthinkable fusion of disparate information sources relevant to the brain, (4) help deconvolve which cognitive concepts most usefully grasp phenomena in the brain, and much more. Bzdok et al. discuss large language models (LLMs) as a recently emerged toolkit in an age when neurobiology too has become "computable." [ABSTRACT FROM AUTHOR]

Spatial navigation is more difficult and effortful for older than younger individuals, a shift which occurs for a variety of neurological, physical, and cognitive reasons associated with aging. Despite a large body of evidence documenting age-related deficits in spatial navigation, comparatively less research addresses how to facilitate more effective navigation behavior for older adults. Since navigation challenges arise for a variety of reasons in old age, a one-size-fits-all solution is unlikely to work. Here, we introduce a framework for the variety of spatial navigation challenges faced in aging, which we call LOST-Location, Orientation, Spatial mapping, and Transit. The LOST framework builds on evidence from the cognitive neuroscience of spatial navigation, which reveals distinct components underpinning human wayfinding. We evaluate research on navigational aids--devices and depictions--which help people find their way around; and we reflect on how navigation aids solve (or fail to solve) specific wayfinding difficulties faced by older adults. In summary, we emphasize a bespoke approach to improving spatial navigation in aging, which focuses on tailoring navigation solutions to specific navigation challenges. Our hope is that by providing precise support to older navigators, navigation opportunities can facilitate independence and exploration, while minimizing the danger of becoming lost. We conclude by delineating critical knowledge gaps in how to improve older adults' spatial navigation capacities that the novel LOST framework could guide to address. [ABSTRACT FROM AUTHOR]

This document is a list of references for various research articles related to the cognitive neuroscience of self-awareness. The articles cover a range of topics, including the definition and measurement of self-awareness, its effects and functions, and its role in mental health conditions. The references provide a starting point for library patrons conducting research on self-awareness and offer a diverse range of perspectives to explore further. [Extracted from the article]

This article delves into the debate between Predictive Processing (PP) models of cognition and enactivist views on representation. The author argues against the enactivist critique of PP's non-semantic representational content, suggesting that a distinction between different senses of 'use' can resolve this issue. The article also explores the question of whether iconic representations have content or are merely characterized by covariance. The author proposes that the adequacy of iconic representations can be understood in terms of their function and properties as evolutionarily entrenched orientational maps. The article concludes by suggesting that the author's view can be labeled as a version of 'contentless representationalism'. Overall, the article provides insights into the debate between enactivism and predictive processing and offers a nuanced perspective on the nature of representation. [Extracted from the article]

Over the past several decades, the field of cognitive neuroscience has drawn with increasing frequency on the figure of the "neural orchestra," a metaphor mapping the activities of localized cortical areas onto sections of a musical ensemble. Popularized by (among others) the neuroscientist Elkhonon Goldberg, the trope is often hailed by contemporary scientists as a new invention--a modern alternative to computational paradigms of cognition. And yet, as I argue here, the concept of the brain orchestra is far from new. Neural-orchestral cartographies originated in the phrenological theory of Franz Joseph Gall in the 1810s, then threaded through medical, ethnographic, anthropological, and pedagogical writing of the later nineteenth century, from the phreno-magnetic tracts of Mariano Cubí y Soler to the psychiatric diagnoses of Théodule-Armand Ribot. Fusing metaphysical concepts of harmony with theories of hierarchical cerebral organology, symphonic minds were products of an epistemological overlap between neural and musical discourses. Crucial to both domains was the newly powerful figure of the podium conductor, conceived as an overarching form of attentional control yoking players into powerful cognitive, musical, and political wholes. The result was a musical neurophysiology refracted through the lenses of imperial technocracy and authoritarian forms of centralization. Today, the historical and neuropolitical forces that generated the Romantic mind orchestra have been largely forgotten, but they continue to exert a spectral influence, hovering behind our conception of the "well-conducted mind," our descriptions of "orchestrated" attention, and our psychopolitical fear of distraction. [ABSTRACT FROM AUTHOR]

This article from the Journal of Neuropsychology introduces a special issue focused on cognitive neurosurgery. The field of cognitive neurosurgery originated in the 1940s with the work of Brenda Milner and Wilder Penfield, who explored the role of the hippocampus in memory and the human motor cortex. Today, cognitive neurosurgery involves collaboration between neurosurgeons, neuropsychologists, and cognitive science experts to study the brain and cognition. The special issue discusses topics such as postoperative impairments, individualized assessments, and neuropsychological functions beyond language or memory. The articles highlight the importance of standardized testing materials and the use of tasks during surgery to spare damage to cognitive processes. The authors emphasize the need for comprehensive neuropsychological assessments and further research on peri- and intraoperative testing protocols. [Extracted from the article]

Patients with anterior temporal lobe (ATL) resection due to mesial temporal lobe epilepsy (MTLE) have difficulties at identifying familiar faces and explicitly remembering newly learned faces but their ability to individuate unfamiliar faces remains largely unknown. Moreover, the extent to which their difficulties with familiar face identity recognition and learning is truly due to the ATL resection remains unknown. Here, we report a study of 24 MTLE patients and matched healthy controls tested with an extensive set of seven face and visual object recognition tasks (including three tasks evaluating unfamiliar face individuation) before and about 6 months after unilateral (nine left, 15 right) ATL resection. We found that ATL resection has little or no effect on the patients' preserved pre‐surgical ability to perform unfamiliar face individuation, both at the group and individual levels. More surprisingly, ATL resection also has little effect on the patients' performance at recognizing and naming famous faces as well as at learning new faces. A substantial proportion of right MTLE patients (33%) even improved their response times on several tasks, which may indicate a functional release of visuo‐spatial processing after resection in the right ATL. Altogether this study shows that face recognition abilities are mainly unaffected by ATL resection in MTLE, either because the critical regions for face recognition are spared or because performance at some tasks is already lower than normal preoperatively. Overall, these findings urge caution when interpreting the causal effect of brain lesions on face recognition ability in patients with ATL resection due to MTLE. They also illustrate the complexity of predicting cognitive outcomes after epilepsy surgery because of the influence of many different intertwined factors. [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]

We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the temporal pattern sequences are constructed using the learned spatial patterns as an alphabet of elements. The decoupling, motivated by cognitive neuroscience research, provides the flexibility for fast and adaptive learning with dynamic changes to data and concept drift and as such is better suited for real-time learning and prediction. The algorithm further addresses several key computational requirements for predicting the next occurrences based on real-life spatiotemporal data, which have been found to be challenging with current state-of-the-art algorithms. Firstly, spatial and temporal patterns are detected using unsupervised learning from unlabeled data streams in changing environments; secondly, vector symbolic architecture (VSA) is used to manage variable-length sequences; and thirdly, hyper dimensional (HD) computing-based associative memory is used to facilitate the continuous prediction of the next occurrences in sequential patterns. The algorithm has been empirically evaluated using two benchmark and three time-series datasets to demonstrate its advantages compared to the state-of-the-art in spatiotemporal unsupervised sequence learning where the proposed ST-SOM algorithm is able to achieve 45% error reduction compared to HTM algorithm. [ABSTRACT FROM AUTHOR]

The document "Neural Correlates and Molecular Mechanisms of Memory and Learning" provides an in-depth exploration of the neural and molecular mechanisms involved in memory and learning. It discusses the role of specific brain regions and neurotransmitters in shaping memory and facilitating learning. The document includes a range of studies on topics such as multisensory stimulation, fear extinction learning, and dopamine dynamics. It emphasizes the complexity of memory and learning processes and the need for further research in this field. The text also highlights the importance of preclinical research and computational medicine in understanding cognitive functions and developing personalized treatments. [Extracted from the article]

Memory and learning are interdependent processes that involve encoding, storage, and retrieval. Especially memory retrieval is a fundamental cognitive ability to recall memory traces and update stored memory with new information. For effective memory retrieval and learning, the memory must be stabilized from short-term memory to long-term memory. Hence, it is necessary to understand the process of memory retention and retrieval that enhances the process of learning. Though previous cognitive neuroscience research has focused on memory acquisition and storage, the neurobiological mechanisms underlying memory retrieval and its role in learning are less understood. Therefore, this article offers the viewpoint that memory retrieval is essential for selecting, reactivating, stabilizing, and storing information in long-term memory. In arguing how memories are retrieved, consolidated, transmitted, and strengthened for the long term, the article will examine the psychological and neurobiological aspects of memory and learning with synaptic plasticity, long-term potentiation, genetic transcription, and theta oscillation in the brain. [ABSTRACT FROM AUTHOR]

Objectives Older adults show memory benefits for self-relevant and emotional content, but there are individual differences in this effect. It has been debated whether processing of self-relevant and emotional information relies on similar processes to one another. We examined whether variation in frontal lobe (FL) function among older adults related similarly to the processing of self-relevant information as it did to emotional information, or whether these relations diverged. Methods While undergoing fMRI, participants (ages 60–88) viewed positive, negative, and neutral objects, and imagined placing those objects in either their home or a stranger's home. Participants completed a surprise memory test outside of the MRI. In a separate session, a cognitive battery was collected and composite scores measuring FL and medial temporal lobe function were computed and related to the behavioral memory performance and the neural engagement during fMRI. Results Behaviorally, FL function related to memory for self-relevant, but not emotional content. Older adults with higher FL function demonstrated reduced self-bias in memory performance. During the processing of self-relevant stimuli, independent of emotion, levels of activity in the middle frontal gyrus showed positive associations with FL function. This relationship was not driven by compensatory activity or disruptions to nonself-relevant neutral content. Discussion These findings point to divergence in the cognitive functions relating to memory enhancements for self- and emotional-relevance. The results further suggest self-relevance as a mnemonic device for older adults, especially in those with lower FL function. [ABSTRACT FROM AUTHOR]

We outline how a predictive processing framework based on active inference can help overcome the limitations of fragmented feed-forward processing theories and more comprehensively explain the role of the amygdala in anxiety, fear, and danger detection. This framework integrates theoretical predictions with empirical findings, suggesting that the central amygdala subnucleus acts as a Bayesian regulator of an interoceptive self-model, sending top-down predictions to the basolateral amygdala for efficient perception. Fear conditioning is discussed as an example of proactive homeostatic regulation, where exteroceptive cues are used to anticipate negative interoceptive experiences. By extension, other amygdala functions (e.g., its role in approach/avoidance behavior or attention) can be explained using the same computational principles within a hierarchical active inference model. The amygdala is a heterogeneous network of subcortical nuclei with central importance in cognitive and clinical neuroscience. Various experimental designs in human psychology and animal model research have mapped multiple conceptual frameworks (e.g., valence/salience and decision making) to ever more refined amygdala circuitry. However, these predominantly bottom up-driven accounts often rely on interpretations tailored to a specific phenomenon, thus preventing comprehensive and integrative theories. We argue here that an active inference model of amygdala function could unify these fractionated approaches into an overarching framework for clearer empirical predictions and mechanistic interpretations. This framework embeds top-down predictive models, informed by prior knowledge and belief updating, within a dynamical system distributed across amygdala circuits in which self-regulation is implemented by continuously tracking environmental and homeostatic demands. [ABSTRACT FROM AUTHOR]

Identifying neural correlates of conscious perception is a fundamental endeavor of cognitive neuroscience. Most studies so far have focused on visual awareness along with trial-by-trial reports of task-relevant stimuli, which can confound neural measures of perceptual awareness with postperceptual processing. Here, we used a three-phase sine-wave speech paradigm that dissociated between conscious speech perception and task relevance while recording EEG in humans of both sexes. Compared with tokens perceived as noise, physically identical sine-wave speech tokens that were perceived as speech elicited a left-lateralized, near-vertex negativity, which we interpret as a phonological version of a perceptual awareness negativity. This response appeared between 200 and 300 ms after token onset and was not present for frequency-flipped control tokens that were never perceived as speech. In contrast, the P3b elicited by task-irrelevant tokens did not significantly differ when the tokens were perceived as speech versus noise and was only enhanced for tokens that were both perceived as speech and relevant to the task. Our results extend the findings from previous studies on visual awareness and speech perception and suggest that correlates of conscious perception, across types of conscious content, are most likely to be found in midlatency negative-going brain responses in content-specific sensory areas. [ABSTRACT FROM AUTHOR]

This comprehensive review delves into the cognitive neuroscience of hypnosis and variations in hypnotizability by examining research employing functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG) methods. Key focus areas include functional brain imaging correlations in hypnosis, EEG band oscillations as indicators of hypnotic states, alterations in EEG functional connectivity during hypnosis and wakefulness, drawing critical conclusions, and suggesting future research directions. The reviewed functional connectivity findings support the notion that disruptions in the available integration between different components of the executive control network during hypnosis may correspond to altered subjective appraisals of the agency during the hypnotic response, as per dissociated and cold control theories of hypnosis. A promising exploration avenue involves investigating how frontal lobes' neurochemical and aperiodic components of the EEG activity at waking-rest are linked to individual differences in hypnotizability. Future studies investigating the effects of hypnosis on brain function should prioritize examining distinctive activation patterns across various neural networks. [ABSTRACT FROM AUTHOR]

Psychiatric research is undergoing significant advances in an emerging subspeciality of computational psychiatry, building on cognitive neuroscience research by expanding to neurocomputational modeling. Here, we illustrate some research trends in this domain using work on proactive cognitive control deficits in schizophrenia as an example. We provide a selective review of formal modeling approaches to understanding cognitive control deficits in psychopathology, focusing primarily on biologically plausible connectionist-level models as well as mathematical models that generate parameter estimates of putatively dissociable psychological or neural processes. We illustrate some of the advantages of these models in terms of understanding both cognitive control deficits in schizophrenia and the potential roles of effort and motivation. Further, we highlight critical future directions for this work, including a focus on establishing psychometric properties, additional work modeling psychotic symptoms and their interaction with cognitive control, and the need to expand both behavioral and neural modeling to samples that include individuals with different mental health conditions, allowing for the examination of dissociable neural or psychological substrates for seemingly similar cognitive impairments across disorders. [ABSTRACT FROM AUTHOR]

Real-world environments present a substantial challenge for human decision-makers due to their complexity, a characteristic that is often not replicated in reinforcement learning lab tasks. Experiments that blend the control of lab tasks with the complexity of naturalistic environments have begun to identify processes that support naturalistic decision-making. Humans use structure and regularity across naturalistic environments to enable effective and efficient decision-making in challenging and complex real-world situations. Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully. [ABSTRACT FROM AUTHOR]

The origins of antisocial behaviour are complex and vary between individuals. We need to improve our understanding of the transactional and iterative unfolding of brain and cognitive development within a relational context. Without this study of the 'embedded brain', we cannot fully understand the emergence and maintenance of antisocial behaviour. To advance the study of the 'embedded brain', the field needs to unify ways of measuring antisocial behaviour and generate more reliable and sensitive measures of brain, cognition, and social environment. Improved understanding of different developmental pathways to antisocial behaviour can help personalise interventions and improve their effectiveness. Antisocial behaviour (ASB) incurs substantial costs to the individual and society. Cognitive neuroscience has the potential to shed light on developmental risk for ASB, but it cannot achieve this potential in an 'essentialist' framework that focuses on the brain and cognition isolated from the environment. Here, we present the case for studying the social transactional and iterative unfolding of brain and cognitive development in a relational context. This approach, which we call the study of the 'embedded brain', is needed to fully understand how risk for ASB arises during development. Concentrated efforts are required to develop and unify methods to achieve this approach and reap the benefits for improved prevention and intervention of ASB. [ABSTRACT FROM AUTHOR]

The ongoing reproducibility crisis in psychology and cognitive neuroscience has sparked increasing calls to re-evaluate and reshape scientific culture and practices. Heeding those calls, we have recently launched the EEGManyPipelines project as a means to assess the robustness of EEG research in naturalistic conditions and experiment with an alternative model of conducting scientific research. One hundred sixty-eight analyst teams, encompassing 396 individual researchers from 37 countries, independently analyzed the same unpublished, representative EEG data set to test the same set of predefined hypotheses and then provided their analysis pipelines and reported outcomes. Here, we lay out how large-scale scientific projects can be set up in a grassroots, community-driven manner without a central organizing laboratory. We explain our recruitment strategy, our guidance for analysts, the eventual outputs of this project, and how it might have a lasting impact on the field. [ABSTRACT FROM AUTHOR]

Exploring the neural mechanisms of awareness is a fundamental task of cognitive neuroscience. There is an ongoing dispute regarding the role of the prefrontal cortex (PFC) in the emergence of awareness, which is partially raised by the confound between report- and awareness-related activity. To address this problem, we designed a visual awareness task that can minimize report-related motor confounding. Our results show that saccadic latency is significantly shorter in the aware trials than in the unaware trials. Local field potential (LFP) data from six patients consistently show early (200-300ms) awareness-related activity in the PFC, including event-related potential and high-gamma activity. Moreover, the awareness state can be reliably decoded by the neural activity in the PFC since the early stage, and the neural pattern is dynamically changed rather than being stable during the representation of awareness. Furthermore, the enhancement of dynamic functional connectivity, through the phase modulation at low frequency, between the PFC and other brain regions in the early stage of the awareness trials may explain the mechanism of conscious access. These results indicate that the PFC is critically involved in the emergence of awareness. [ABSTRACT FROM AUTHOR]

Fear extinction is a phenomenon that involves a gradual reduction in conditioned fear responses through repeated exposure to fear-inducing cues. Functional brain connectivity assessments, such as functional magnetic resonance imaging (fMRI), provide valuable insights into how brain regions communicate during these processes. Stress, a ubiquitous aspect of life, influences fear learning and extinction by changing the activity of the amygdala, prefrontal cortex, and hippocampus, leading to enhanced fear responses and/or impaired extinction. Glucocorticoid receptors (GRs) are key to the stress response and show a dual function in fear regulation: while they enhance the consolidation of fear memories, they also facilitate extinction. Accordingly, GR dysregulation is associated with anxiety and mood disorders. Recent advancements in cognitive neuroscience underscore the need for a comprehensive understanding that integrates perspectives from the molecular, cellular, and systems levels. In particular, neuropharmacology provides valuable insights into neurotransmitter and receptor systems, aiding the investigation of mechanisms underlying fear regulation and potential therapeutic targets. A notable player in this context is cortisol, a key stress hormone, which significantly influences both fear memory reconsolidation and extinction processes. Gaining a thorough understanding of these intricate interactions has implications in terms of addressing psychiatric disorders related to stress. This review sheds light on the complex interactions between cognitive processes, emotions, and their neural bases. In this endeavor, our aim is to reshape the comprehension of fear, stress, and their implications for emotional well-being, ultimately aiding in the development of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Working memory is integral to a range of critical cognitive functions such as reasoning and decision‐making. Although alterations in working memory have been observed in neurodivergent populations, there has been no review mapping how cognitive load is measured in common neurodevelopmental conditions such as attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia. This scoping review explores the neurophysiological measures used to study cognitive load in these specific populations. Our findings highlight that electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the most frequently used methods, with a limited number of studies employing functional near‐infrared spectroscopy (fNIRs), magnetoencephalography (MEG) or eye‐tracking. Notably, eye‐related measures are less commonly used, despite their prominence in cognitive load research among neurotypical individuals. The review also highlights potential correlates of cognitive load, such as neural oscillations in the theta and alpha ranges for EEG studies, blood oxygenation level‐dependent (BOLD) responses in lateral and medial frontal brain regions for fMRI and fNIRS studies and eye‐related measures such as pupil dilation and blink rate. Finally, critical issues for future studies are discussed, including the technical challenges associated with multimodal approaches, the possible impact of atypical features on cognitive load measures and balancing data richness with participant well‐being. These insights contribute to a more nuanced understanding of cognitive load measurement in neurodivergent populations and point to important methodological considerations for future neuroscientific research in this area. [ABSTRACT FROM AUTHOR]

Recently, multi-voxel pattern analysis has verified that information can be removed from working memory (WM) via three distinct operations replacement, suppression, or clearing compared to information being maintained (Kim et al., 2020). While univariate analyses and classifier importance maps in Kim et al. (2020) identified brain regions that contribute to these operations, they did not elucidate whether these regions represent the operations similarly or uniquely. Using Leiden-community-detection on a sample of 55 humans (17 male), we identified four brain networks, each of which has a unique configuration of multi-voxel activity patterns by which it represents these WM operations. The visual network (VN) shows similar multi-voxel patterns for maintain and replace, which are highly dissimilar from suppress and clear, suggesting this network differentiates whether an item is held in WM or not. The somatomotor network (SMN) shows a distinct multi-voxel pattern for clear relative to the other operations, indicating the uniqueness of this operation. The default mode network (DMN) has distinct patterns for suppress and clear, but these two operations are more similar to each other than to maintain and replace, a pattern intermediate to that of the VN and SMN. The frontoparietal control network (FPCN) displays distinct multi-voxel patterns for each of the four operations, suggesting that this network likely plays an important role in implementing theseWMoperations. These results indicate that the operations involved in removing information fromWMcan be performed in parallel by distinct brain networks, each of which has a particular configuration by which they represent these operations. [ABSTRACT FROM AUTHOR]