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The i5 Playbook—Transforming Business Education with 5 Impactful Methods, published by the Principles for Responsible Management Education (PRME), offers concrete pedagogical approaches to develop responsible business leaders. The playbook presents five teaching methods, known as the "impactful five" or "i5," that focus on making learning meaningful, facilitating active engagement, designing for iteration, developing supportive social interaction, and fostering joy and well-being. The resource emphasizes the integration of cognitive, creative, emotional, and social skills in responsible leadership education. It is structured in a clear and accessible manner, providing definitions, literature links, high-impact actions, illustrations, and quick exercises. The playbook is an open access resource, making it widely accessible, and encourages educators to reflect on their own teaching practices as responsible leaders. It is recommended for anyone seeking to enhance their pedagogical skills and develop more responsible leaders. [Extracted from the article]

The nervous system, especially the human brain, is characterized by its highly complex network topology. The neurodevelopment of some of its features has been described in terms of dynamic optimization rules. We discuss the principle of adaptive rewiring, i.e., the dynamic reorganization of a network according to the intensity of internal signal communication as measured by synchronization or diffusion, and its recent generalization for applications in directed networks. These have extended the principle of adaptive rewiring from highly oversimplified networks to more neurally plausible ones. Adaptive rewiring captures all the key features of the complex brain topology: it transforms initially random or regular networks into networks with a modular small-world structure and a rich-club core. This effect is specific in the sense that it can be tailored to computational needs, robust in the sense that it does not depend on a critical regime, and flexible in the sense that parametric variation generates a range of variant network configurations. Extreme variant networks can be associated at macroscopic level with disorders such as schizophrenia, autism, and dyslexia, and suggest a relationship between dyslexia and creativity. Adaptive rewiring cooperates with network growth and interacts constructively with spatial organization principles in the formation of topographically distinct modules and structures such as ganglia and chains. At the mesoscopic level, adaptive rewiring enables the development of functional architectures, such as convergent-divergent units, and sheds light on the early development of divergence and convergence in, for example, the visual system. Finally, we discuss future prospects for the principle of adaptive rewiring. [ABSTRACT FROM AUTHOR]

Creative movement, in the form of music- and dance-based exercise and rehabilitation, can serve as a model for learning and memory, visuospatial orientation, mental imagery, and multimodal sensory-motor integration. This review summarizes the advancement in cognitive neuroscience aimed at determining cognitive processes and brain structural and functional correlates involved in dance or creative movement, as well as the cognitive processes which accompany such activities. We synthesize the evidence for the use of cognitive, motor, and cognitive-motor function in dance as well as dance's potential application in neurological therapy and neurorehabilitation. Finally, we discuss how partnered interaction and sensorimotor integration in dance, and "dancing robots" could shed light on future application of dance as rehabilitation, of dance used in technology and potential mechanisms of benefit from dance-based activities. [ABSTRACT FROM AUTHOR]

Video presentation has become ubiquitous in paradigms investigating the neural and behavioral responses to observed actions. In spite of the great interest in uncovering the processing of observed bodily movements and actions in neuroscience and cognitive science, at present, no standardized set of video stimuli for action observation research in neuroimaging settings exists. To facilitate future action observation research, we developed an open-access database of 135 high-definition videos of a male actor performing object-oriented actions. Actions from 3 categories: kinematically natural and goal-intact (Normal), kinematically unnatural and goal-intact (How), or kinematically natural and goal-violating (What), directed toward 15 different objects were filmed from 3 angles. Psychometric evaluation of the database revealed high video recognition accuracy (Mean accuracy = 88.61 %) and substantial inter-rater agreement (Fleiss' Kappa = 0.702), establishing excellent validity and reliability. Videos' exact timing of motion onset was identified using a custom motion detection frame-differencing procedure. Based on its outcome, the videos were edited to assure that motion begins at the second frame of each video. The videos' timing of category recognition was also identified using a novel behavioral up-down staircase procedure. The identified timings can be incorporated in future experimental designs to counteract jittered stimulus onsets, thus vastly improving the sensitivity of neuroimaging experiments. All videos, their psychometric evaluations, and the timing of their frame of category recognition, as well as our custom programs for performing these evaluations on our, or on other similar video databases, are available at the Open Science Framework (https://osf.io/zexc4/). [ABSTRACT FROM AUTHOR]

Background:. The present study aimed at deriving regression-based reliable change indices (RCIs) for the Montreal Cognitive Assessment (MoCA) in an Italian cohort of non-demented Parkinson's disease (PD) patients. Methods: N = 33 consecutive, non-demented PD patients were followed-up at a 5-to-8-month interval (M = 6.6; SD = 0.6) with the MoCA. Practice effects and test-retest reliability were assessed via dependent-sample t-tests and intra-class correlation (ICC) coefficients, respectively. RCIs were derived separately for raw and demographically adjusted MoCA scores according to a standardized regression-based approach by accounting for both baseline confounders (i.e., demographics, disease duration and Unified Parkinson's Disease Rating Scale scores) and retest interval. Results: No practice effects were found (t(32) = 0.29; p =.778), with acceptable test-retest reliability being detected (ICC = 0.67). MoCA scores at T0 proved to be the only significant predictor of T1 MoCA performances within both the model addressing raw scores and that addressing adjusted scores (ps < 0.001). Conclusions: The present study provides Italian practitioners and researchers with regression-based RCIs for the MoCA in non-demented PD patients, which can be reliably adopted for retest interval ≥ 5 and ≤ 8 months without encountering any practice effect. [ABSTRACT FROM AUTHOR]

Behavioral perception-action dissociations are widely used to test models of high-level vision, but debates concerning their interpretation have underestimated the role of multisensory mechanisms in such tests. Sensorimotor tasks engage multisensory processing in fundamentally different ways than perceptual tasks, and these differences can modulate dissociations in different ways based on task features. To test this idea, we compared perception and action using a well-understood size-contrast effect, the Uznadze illusion, and manipulated both unimodal and crossmodal stimulation as well as conditions that are known to favor or hinder multisensory integration. Results demonstrated that similar or dissociable effects on perception and action can be observed depending on factors that are known to affect multisensory processing. Specifically, such factors can cause a visual task to be affected by the illusion or remain fully unaffected, whereas a visuomotor task can be affected by the illusion, remain immune from the illusion, or, unexpectedly, even show a clear reverse effect. These findings provide a novel perspective on a long standing debate in behavioral cognitive neuroscience. [ABSTRACT FROM AUTHOR]

A central pursuit of cognitive neuroscience is to find neural mechanisms of cognition, with research programs favoring different strategies to look for them. But what is a neural mechanism, and how do we know we have captured them? Here I answer these questions through a framework that integrates Marr's levels with philosophical work on mechanism. From this, the following goal emerges: What needs to be explained are the computations of cognition, with explanation itself given by mechanism—composed of algorithms and parts of the brain that realize them. This reveals a delineation within cognitive neuroscience research. In the premechanism stage, the computations of cognition are linked to phenomena in the brain, narrowing down where and when mechanisms are situated in space and time. In the mechanism stage, it is established how computation emerges from organized interactions between parts—filling the premechanistic mold. I explain why a shift toward mechanistic modeling helps us meet our aims while outlining a road map for doing so. Finally, I argue that the explanatory scope of neural mechanisms can be approximated by effect sizes collected across studies, not just conceptual analysis. Together, these points synthesize a mechanistic agenda that allows subfields to connect at the level of theory. [ABSTRACT FROM AUTHOR]

Cognitive science has demonstrated that we construct knowledge about the world by abstracting patterns from routinely encountered experiences and storing them as semantic memories. This preregistered study tested the hypothesis that caregiving‐related early adversities (crEAs) shape affective semantic memories to reflect the content of those adverse interpersonal‐affective experiences. We also tested the hypothesis that because affective semantic memories may continue to evolve in response to later‐occurring positive experiences, child‐perceived attachment security will inform their content. The sample comprised 160 children (ages 6–12 at Visit 1; 87F/73 M), 66% of whom experienced crEAs (n = 105). At Visit 1, crEA exposure prior to study enrollment was operationalized as parental‐reports endorsing a history of crEAs (abuse/neglect, permanent/significant parent‐child separation); while child‐reports assessed concurrent attachment security. A false memory task was administered online ∼2.5 years later (Visit 2) to probe the content of affective semantic memories–specifically attachment schemas. Results showed that crEA exposure (vs. no exposure) was associated with a higher likelihood of falsely endorsing insecure (vs. secure) schema scenes. Attachment security moderated the association between crEA exposure and insecure schema‐based false recognition. Findings suggest that interpersonal‐affective semantic schemas include representations of parent‐child interactions that may capture the quality of one's own attachment experiences and that these representations shape how children remember attachment‐relevant narrative events. Findings are also consistent with the hypothesis that these affective semantic memories can be modified by later experiences. Moving forward, the approach taken in this study provides a means of operationalizing Bowlby's notion of internal working models within a cognitive neuroscience framework. Research Highlights: Affective semantic memories representing insecure schema knowledge (child needs + needs‐not‐met) may be more salient, elaborated, and persistent among youths exposed to early caregiving adversity.All youths, irrespective of early caregiving adversity exposure, may possess affective semantic memories that represent knowledge of secure schemas (child needs + needs‐met).Establishing secure relationships with parents following early‐occurring caregiving adversity may attenuate the expression of insecure semantic memories, suggesting potential malleability.Affective semantic memories include schema representations of parent‐child interactions that may capture the quality of one's own attachment experiences and shape how youths remember attachment‐relevant events. [ABSTRACT FROM AUTHOR]

Thinking about our own death and its salience in relation to decision making has become a fruitful area of multidisciplinary research across the breadth of psychological science. By bringing together experts from philosophy, cognitive and affective neuroscience, clinical and computational psychiatry we have attempted to set out the current state of the art and point to areas of further enquiry. One stimulus for doing this is the need to engage with policy makers who are now having to consider guidelines on suicide and assisted suicide so that they may be aware of their own as well as the wider populations' cognitive processes when confronted with the ultimate truth of mortality. [ABSTRACT FROM AUTHOR]

Computational intelligence is used to create artificially intelligent systems with the ability to learn, adapt, and solve problems. Learners' computational abilities can be improved with the help of Cognitive Neuroscience techniques. Computational intelligence refers to the ability of a learner based on the complex operations of the brain in the four relevant dimensions: Visual (V), Aural (A), Kinematic (K), and Reading/Writing (R/W). Our proposed framework consists of electroencephalogram (EEG) data acquisition, signal extraction, and EEG signal categorization to assess students' cognitive learning abilities. Our proposed approach uses an EEG device equipped with a microprocessor, a Think Gear (TGAM) EEG sensor, and a PCB of 16 dry electrodes. The EEG device and the remote processing device are connected by Bluetooth. The EEG signal provides the students with neurofeedback on their cognitive learning capability. The feedback obtained through the learning process will be endowing to improvise computational intelligence. The statistical derivative, Pearson Co-efficient, is used to find the correlation among the derived attributes. The attributes considered are the learner's gender, stream, age, and geographical region. The results obtained highlight that gender, stream, and age have no correlation with the detectable learning index, and the most accurate learners are kinesthetic ones. Bi-modal learners, who can maintain focus while reading and using their kinematic abilities, had the secondhighest learning capacity. [ABSTRACT FROM AUTHOR]

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity. Key points: • Non-Linear brain activity trajectories: The study challenges linear brain activity changes with age, revealing distinct trajectories in oscillatory patterns based on frequency and brain region. • Contradicting age-related slowing: slow wave activity decreases and fast wave activity increases during healthy aging, contrasting with pathological conditions. • Cognitive implications and structural integrity: Oscillatory power changes correlate with cognitive decline and reduced structural integrity, challenging the brain scaffolding theory of aging and offering insights into neurodevelopment and aging. [ABSTRACT FROM AUTHOR]

Creativity is typically operationalized as divergent thinking (DT) ability, a form of higher-order cognition which relies on memory, attention, and other component processes. Despite recent advances, creativity neuroscience lacks a unified framework to model its complexity across neural, genetic, and cognitive scales. Using task-based fMRI from two independent samples and MVPA, we identified a neural pattern that predicts DT, validated through cognitive decoding, genetic data, and large-scale resting-state fMRI. Our findings reveal that DT neural patterns span brain regions associated with diverse cognitive functions, with positive weights in the default mode and frontoparietal control networks and negative weights in the visual network. The high correlation with the primary gradient of functional connectivity suggests that DT involves extensive integration from concrete sensory information to abstract, higher-level cognition, distinguishing it from other advanced cognitive functions. Moreover, neurobiological analyses show that the DT pattern is positively correlated with dopamine-related neurotransmitters and genes influencing neurotransmitter release, advancing the neurobiological understanding of creativity. This study used fMRI and MVPA to identify neural patterns predicting divergent thinking (DT). DT engages the default mode and frontoparietal control networks, opposing the visual network, and is linked to dopamine-related neurotransmitters and genes. [ABSTRACT FROM AUTHOR]

Simple Summary: After outlining the role of tDCS in enhancing physical performance, this study explores its mechanisms of action. The primary focus is on how tDCS improves motor abilities and motor skill by modulating the resting membrane potential of neurons, enhancing synaptic plasticity, and strengthening the functional connectivity of neural networks. tDCS has demonstrated significant potential in improving motor function and facilitating rehabilitation, which is particularly valuable for athletes, the elderly, and patients with neurological injuries. Its broader social impact lies in its accessibility as an economical, convenient, and effective tool that can be widely applied in sports science, rehabilitation medicine, and cognitive neuroscience to enhance individual performance and quality of life. Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that applies a stable, low-intensity (1–2 mA) direct current to modulate neuronal activity in the cerebral cortex. This technique is effective, simple to operate, affordable, and widely employed across various fields. tDCS has been extensively used in clinical and translational research, with growing applications in military and competitive sports domains. In recent years, the use of tDCS in sports science has garnered significant attention from researchers. Numerous studies have demonstrated that tDCS can enhance muscle strength, explosive power, and aerobic metabolism, reduce fatigue, and improve cognition, thereby serving as a valuable tool for enhancing athletic performance. Additionally, recent research has shed light on the physiological mechanisms underlying tDCS, including its modulation of neuronal resting membrane potential to alter cortical excitability, enhancement of synaptic plasticity to regulate long-term potentiation, modulation of neurovascular coupling to improve regional cerebral blood flow, and improvement of cerebral network functional connectivity, which activates and reinforces specific brain regions. tDCS also enhances the release of excitatory neurotransmitters, further regulating brain function. This article, after outlining the role of tDCS in improving physical performance, delves into its mechanisms of action to provide a deeper understanding of how tDCS enhances athletic performance and offers novel approaches and perspectives for physical performance enhancement. [ABSTRACT FROM AUTHOR]

Although previous studies have observed increased global network integration during tasks in persons with mild cognitive impairment (MCI), the association between this integration and actual task performance has remained unexplored. Understanding this link is crucial for uncovering the underlying mechanism behind these changes in network integration and their potential role in MCI. Here, to find such a link, we investigated brain network integration derived from electroencephalography recordings during a visual motion discrimination task in older adults with MCI and those with normal cognition. We focused on a critical period just before stimulus presentation, which is known to be important for task performance. Our results revealed that during this period, MCI patients exhibited increased network integration compared to controls. Interestingly, increased integration was associated with worse task performance in the MCI group, suggesting it was not beneficial. No such association was found in the control group. Notably, this difference existed despite similar overall task performance between the groups. This suboptimal integration pattern during the cognitive task might reflect network de‐differentiation due to disinhibition in MCI patients. Collectively, our study highlights the potential of analysing network integration during tasks to identify cognitive impairment and suggest a distinct role for network integration in MCI patients compared with healthy controls. [ABSTRACT FROM AUTHOR]

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

Neuroscientists routinely use reverse inference (RI) to draw conclusions about cognitive processes from neural activation data. However, despite its widespread use, the methodological status of RI is a matter of ongoing controversy, with some critics arguing that it should be rejected wholesale on the grounds that it instantiates a deductively invalid argument form. In response to these critiques, some have proposed to conceive of RI as a form of abduction or inference to the best explanation (IBE). We side with this response but at the same time argue that a defense of RI requires more than identifying it as a form of IBE. In this paper, we give an analysis of what determines the quality of an RI conceived as an IBE and on that basis argue that whether an RI is warranted needs to be decided on a case-by-case basis. Support for our argument will come from a detailed methodological discussion of RI in cognitive neuroscience in light of what the recent literature on IBE has identified as the main quality indicators for IBEs. [ABSTRACT FROM AUTHOR]

Accumulating evidence suggests that rhythmic temporal cues in the environment influence the encoding of information into long-term memory. Here, we test the hypothesis that these mnemonic effects of rhythm reflect the coupling of high-frequency (gamma) oscillations to entrained lower-frequency oscillations synchronized to the beat of the rhythm. In Study 1, we first test this hypothesis in the context of global effects of rhythm on memory, when memory is superior for visual stimuli presented in rhythmic compared with arrhythmic patterns at encoding [Jones, A., & Ward, E. V. Rhythmic temporal structure at encoding enhances recognition memory, Journal of Cognitive Neuroscience, 31, 1549–1562, 2019]. We found that rhythmic presentation of visual stimuli during encoding was associated with greater phase-amplitude coupling (PAC) between entrained low-frequency (delta) oscillations and higher-frequency (gamma) oscillations. In Study 2, we next investigated cross-frequency PAC in the context of local effects of rhythm on memory encoding, when memory is superior for visual stimuli presented in-synchrony compared with out-of-synchrony with a background auditory beat [Hickey, P., Merseal, H., Patel, A. D., & Race, E. Memory in time: Neural tracking of low-frequency rhythm dynamically modulates memory formation. Neuroimage, 213, 116693, 2020]. We found that the mnemonic effect of rhythm in this context was again associated with increased cross-frequency PAC between entrained low-frequency (delta) oscillations and higher-frequency (gamma) oscillations. Furthermore, the magnitude of gamma power modulations positively scaled with the subsequent memory benefit for in- versus out-of-synchrony stimuli. Together, these results suggest that the influence of rhythm on memory encoding may reflect the temporal coordination of higher-frequency gamma activity by entrained low-frequency oscillations. [ABSTRACT FROM AUTHOR]

Episodic memory may essentially be memory for one's place within a temporally unfolding scene from a first-person perspective. Given this, pervasively used static stimuli may only capture one small part of episodic memory. A promising approach for advancing the study of episodic memory is immersing participants within varying scenes from a first-person perspective. We present a pool of distinct scene stimuli for use in virtual environments and a paradigm that is implementable across varying levels of immersion on multiple virtual reality (VR) platforms and adaptable to studying various aspects of scene and episodic memory. In our task, participants are placed within a series of virtual environments from a first-person perspective and guided through a virtual tour of scenes during a study phase and a test phase. In the test phase, some scenes share a spatial layout with studied scenes; others are completely novel. In three experiments with varying degrees of immersion, we measure scene recall, scene familiarity-detection during recall failure, the subjective experience of déjà vu, the ability to predict the next turn on a tour, the subjective sense of being able to predict the next turn on a tour, and the factors that influence memory search and the inclination to generate candidate recollective information. The level of first-person immersion mattered to multiple facets of episodic memory. The paradigm presents a useful means of advancing mechanistic understanding of how memory operates in realistic dynamic scene environments, including in combination with cognitive neuroscience methods such as functional magnetic resonance imaging and electrophysiology. [ABSTRACT FROM AUTHOR]

The P300 event‐related potential (ERP) is widely investigated in cognitive neuroscience, including related to aging, with smaller amplitudes and delayed latency consistently reported in Alzheimer's disease (AD). Given that AD‐related neurological changes begin years before symptom onset, ERPs in asymptomatic elders with AD risk may characterize early changes. ERPs are seldom studied in this population. Yet, healthy carriers of apolipoprotein‐E (APOE) ε4 have evidenced delayed P300 latencies, while P300 amplitude differences are seldom found. However, despite its frequent study, the specific cognitive processes reflected by P300 remain unclear. We propose that these challenges are due to the relatively long P300 window, which likely encompasses multiple underlying subprocesses that overlap in time. Temporal–spatial principal component analysis (tsPCA) maintains the high temporal resolution of EEG and is better suited to isolate processes that overlap in time. Thus, we interrogated APOE ε4 differences in P300 activity during successful stop‐signal inhibitory control in healthy, cognitively intact older adults (25 ε4−, 20 ε4+), using both conventional ERP metrics (i.e., mean and peak amplitude) and P300 tsPCA factors. P300 amplitudes did not differ by ε4 using conventional metrics. tsPCA revealed two P300 factors in each ε4 group: first, a Posterior P300 (attention allocation) factor, and second, a relatively Anterior P300 (performance monitoring, evaluating, and updating) factor. tsPCA uniquely revealed greater activity in ε4+ vs. ε4− in Anterior P300. ε4 groups had comparable task performance, suggesting that greater P300 activity in ε4+ likely reflects neural compensation for ε4‐related deficits, thereby enabling the maintenance of good task performance. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment presents in both adolescent-onset(ado-OP) and adult-onset psychosis(adu-OP). Age and neurodevelopmental factors likely contribute to cognitive differences. This study aimed to characterize cognitive functions in ado-OP compared to adu-OP in a clinical population with drug-naive first-episode psychosis(FEP). Methods: A total of 788 drug-naive patients with FEP and 774 sex- and age-matched healthy controls(HCs) were included. Participants were divided into four groups by whether they were under or over 21 years of age: adolescent-onset FEP(ado-FEP, n = 380), adult-onset FEP(adu-FEP, n = 408), adolescent HC(ado-HC, n = 334), and adult HC(adu-HC, n = 440). Comprehensive cognitive assessments were performed using the MATRICS Cognitive Consensus Battery(MCCB), covers six cognitive domains: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning, and problem-solving. Data analyses were conducted using correlation analyses and binary logistic regression. Results: The patterns of cognitive domain differences between ado-FEP and adu-FEP were found to be similar to those between ado-HC and adu-HC, whereas cognitive impairments appeared to be more pronounced in patients with adu-OP than ado-OP. The mazes subtest had the maximum effect size(ES) in the FEP(ES = 0.37) and HC(ES = 0.30) groups when comparing the adolescent and adult groups. Cognitive subtests were mostly significantly correlated with negative symptoms, especially for adolescents with FEP, in which all the subtests were significantly correlated with negative symptoms in the ado-FEP group. Better performance in the domains of spatial cognition and problem-solving abilities was more likely in the ado-FEP group than in the adu-FEP group. Conclusions: These findings suggest cognitive differences between adolescents and adults but similar patterns of affected domains in HCs and patients with FEP. Therefore, the development of targeted cognitive interventions tailored to the specific needs of different age groups appears warranted. [ABSTRACT FROM AUTHOR]

This article introduces a special issue on body representation and body transformations in neuropsychology and embodied neuroscience. It discusses the concept of body representations and their plasticity, exploring how factors such as aging, weight changes, technology, and social interactions influence them. The special issue includes papers on various aspects of body representation, including historical reviews, virtual reality assessments, and the impact of pain on body representation. The text emphasizes the need for an interdisciplinary approach to studying body representation and highlights the relevance of this research in understanding the complex interactions between the brain, body, and environment. It also introduces the concept of predictive processing and Bayesian inference as frameworks for understanding how the brain represents and updates internal models of the body. The document is a list of references to scientific articles and studies related to body schema and body image, covering a wide range of topics in this field. [Extracted from the article]

This document is a summary of a collection of articles on the roles of temporal precision, synchronization, and neural oscillations in brain function. The articles discuss innovative methodologies for analyzing brain oscillations and their importance in understanding neural circuit dynamics and brain functions. They also highlight the relevance of neural oscillations in speech and music processing, as well as in neurodevelopmental disorders like dyslexia and stuttering. The articles offer valuable perspectives for both research and clinical applications. [Extracted from the article]

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

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

We reconcile two significant lines of Cognitive Neuroscience research: the relationship between the structural and functional architecture of the brain and behaviour on the one hand and the functional significance of oscillatory brain processes to behavioural performance on the other. Network neuroscience proposes that the three elements, behavioural performance, EEG oscillation frequency, and network connectivity should be tightly connected at the individual level. Young and old healthy adults were recruited as a proxy for performance variation. An auditory inhibitory control task was used to demonstrate that task performance correlates with the individual EEG frontal theta frequency. Older adults had a significantly slower theta frequency, and both theta frequency and task performance correlated with the strengths of two network connections that involve the main areas of inhibitory control and speech processing. The results suggest that both the recruited functional network and the oscillation frequency induced by the task are specific to the task, are inseparable, and mark individual differences that directly link structure and function to behaviour in health and disease. [ABSTRACT FROM AUTHOR]

Sound recognition is effortless for humans but poses a significant challenge for artificial hearing systems. Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have recently surpassed traditional machine learning in sound classification. However, current DNNs map sounds to labels using binary categorical variables, neglecting the semantic relations between labels. Cognitive neuroscience research suggests that human listeners exploit such semantic information besides acoustic cues. Hence, our hypothesis is that incorporating semantic information improves DNN's sound recognition performance, emulating human behaviour. In our approach, sound recognition is framed as a regression problem, with CNNs trained to map spectrograms to continuous semantic representations from NLP models (Word2Vec, BERT, and CLAP text encoder). Two DNN types were trained: semDNN with continuous embeddings and catDNN with categorical labels, both with a dataset extracted from a collection of 388,211 sounds enriched with semantic descriptions. Evaluations across four external datasets, confirmed the superiority of semantic labeling from semDNN compared to catDNN, preserving higher-level relations. Importantly, an analysis of human similarity ratings for natural sounds, showed that semDNN approximated human listener behaviour better than catDNN, other DNNs, and NLP models. Our work contributes to understanding the role of semantics in sound recognition, bridging the gap between artificial systems and human auditory perception. [ABSTRACT FROM AUTHOR]

Attention is a fundamental cognitive mechanism whose primary function is to regulate and organize the flow of perceptions and actions shaping our mental life. Early cognitive models have highlighted aspects of sustained, selective, and executive control as essential components of attention. These three broad aspects can be further subdivided into subordinate operations, depending on whether the particular function is mostly driven by external stimulation (bottom-up attention) or relies on endogenous processes such as voluntary intentions or expectations (top-down attention). After several decades of cognitive neuroscience research, these different functions have been associated with specific circuits of brain regions. Based on the cognitive neuroscience framework, this paper presents a theory of attention development and discusses behavioral and brain evidence regarding the development of attention function during the first years of life. [ABSTRACT FROM AUTHOR]

Many patients with substance use disorders (SUDs) present cognitive deficits, which are associated with clinical outcomes. Neuropsychological remediation might help rehabilitate cognitive functions in these populations, hence improving treatment effectiveness. Nardo and colleagues (Neuropsychology Review, 32, 161–191, 2022) reviewed 32 studies applying cognitive remediation for patients with SUDs. They underlined the heterogeneity and lack of quality of studies in this research field but concluded that cognitive remediation remains a promising tool for addictive disorders. We capitalize on the insights of this review to identify the key barriers that currently hinder the practical implementation of cognitive remediation in clinical settings. We outline five issues to be addressed, namely, (1) the integration of cognitive remediation in clinical practices; (2) the selection criteria and individual factors to consider; (3) the timing to be followed; (4) the priority across trained cognitive functions; and (5) the generalization of the improvements obtained. We finally propose that cognitive remediation should not be limited to classical cognitive functions but should also be extended toward substance-related biases and social cognition, two categories of processes that are also involved in the emergence and persistence of SUDs. [ABSTRACT FROM AUTHOR]

This article explores the challenges and opportunities of incorporating cognitive neuroscience into the classroom. While neuroscience research can provide insights into learning, researchers caution that translating this research into education is complex due to the differences in methodology and the complexity of education itself. It is important to be cautious when applying neuroscientific claims to classrooms and to avoid excessive reliance on science. However, with strong evidence and research methodologies, a connection between research and practice can be established. The article also discusses evidence-based strategies for creating a low-stress learning environment and optimizing learning in adolescents, as well as the role of neuroplasticity in learning. It emphasizes the importance of mastery learning, which suggests that every child can achieve similar levels of achievement given the right opportunities and time. The article also highlights the role of practice and revision in strengthening neural pathways and enhancing cognition, suggesting that retrieval practice, such as quizzes, is more effective for long-term memory retention compared to recapping and restudying. The use of mind maps as a tool for effective practice and revision is also mentioned. The article concludes by acknowledging the potential of neuroscience in education if used carefully and without falling into neuromyths. [Extracted from the article]

A major scientific objective of cognitive neuroscience is to define cortico-cortical functional connections supporting cognitive functions. Here, we use an original approach combining frequency-tagging and direct electrical stimulation (DES) to test for bidirectional and cross-hemispheric category-specific modulations within the human cortical face network. A unique patient bilaterally implanted with depth electrodes in multiple face-selective cortical regions of the ventral occipito-temporal cortex (VOTC) was shown 70 s sequences of variable natural object images at a 6 Hz rate, objectively identifying deviant face-selective neural activity at 1.2 Hz (i.e., every five images). Concurrent electrical stimulation was separately applied for 10 seconds on four independently defined face-selective sites in the right and left VOTC. Upon stimulation, we observed reduced or even abolished face-selective neural activity locally and, most interestingly, at distant VOTC recording sites. Remote DES effects were found up to the anterior temporal lobe (ATL) in both forward and backward directions along the VOTC, as well as across the two hemispheres. This reduction was specific to face-selective neural activity, with the general 6 Hz visual response being mostly unaffected. Overall, these results shed light on the functional connectivity of the cortical face-selective network, supporting its non-hierarchical organization as well as bidirectional effective category-selective connections between posterior 'core' regions and the ATL. They also pave the way for widespread and systematic development of this approach to better understand the functional and effective connectivity of human brain networks. [ABSTRACT FROM AUTHOR]

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

Cognitive ontology has become a popular topic in philosophy, cognitive psychology, and cognitive neuroscience. At its center is the question of which cognitive capacities should be included in the ontology of cognitive psychology and cognitive neuroscience. One common strategy for answering this question is to look at brain structures and determine the cognitive capacities for which they are responsible. Some authors interpret this strategy as a search for neural mechanisms, as understood by the so-called new mechanistic approach. In this article, I will show that this new mechanistic answer is confronted with what I call the triviality problem. A discussion of this problem will show that one cannot derive a meaningful cognitive ontology from neural mechanisms alone. Nonetheless, neural mechanisms play a crucial role in the discovery of a cognitive ontology because they are epistemic proxies for best systematizations. [ABSTRACT FROM AUTHOR]

Objective Two studies were performed to validate a brief measure of cognitive insight and compare it to an empirical model – the Cognitive Awareness Model (CAM). Method A pilot study included 31 (52% male; M age = 69.42) patients from an outpatient neuropsychological assessment clinic. Seven patients were diagnosed with likely Alzheimer's dementia (AD), 15 mild cognitive impairment (MCI), and 9 no diagnosis (i.e. cognitively normal; CN). The Cognitive Coding Form (CCF) and several other measures were administered. Study 2 entailed archival data extraction of 240 patients (80 CN, 80 MCI, and 80 AD; 53.3% female; M age = 72.8) to examine whether the CCF predicts memory (Wechsler Memory Scale – IV) and executive functioning (Trail-Making Test B). Results The pilot study found preliminary evidence of convergent and discriminant validity for the 8-item CCF. Study 2 confirmed that both patient-reported cognitive concerns (F (2,237) = 10.40, p <.001, ω2 =.07, power =.99) and, more strongly, CCF informant-patient discrepancy scores (F (2,237) = 24.52, p <.001, ω2 =.16, power =.99) can distinguish CNs from those with MCI and AD. A regression indicated that depression (5.5%; β = -.38, p <.001) and TMT-B (13%; β = -.43, p <.001), together accounted for 18.5% of the variance in insight (R2 =.19, F (2,219) = 26.10, p <.001), supporting the CAM. Conclusions These studies establish an efficient measure of insight with high clinical utility and inform the literature on the role of insight in predicting performance in those with Alzheimer's pathology. [ABSTRACT FROM AUTHOR]

Human learners acquire abstract concepts from limited experience. The effort to explain this capacity has fueled debate between symbolic and connectionist approaches and motivated proposals for neuro-symbolic systems. The relational bottleneck principle suggests a novel way to bridge the gap. By restricting information processing to focus only on relations, the approach encourages abstract symbol-like mechanisms to emerge in neural networks. We present an information theoretic formulation and review neural network architectures that implement the principle, enabling rapid learning and systematic generalization of relational patterns. The approach can explain phenomena ranging from the development of numerical abstractions to capacity limits in cognition; is consistent with findings from cognitive neuroscience; and offers a principle for designing more powerful artificial learning systems. A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain. [ABSTRACT FROM AUTHOR]

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

Construction activities are often conducted in outdoor and harsh environments and involve long working hours and physical and mental labor, which can lead to significant mental fatigue among workers. This study introduces a novel and non-invasive method for monitoring and assessing mental fatigue in construction workers. Based on cognitive neuroscience theory, we analyzed the neurophysiological mapping of spontaneous mental fatigue and developed multimodal in-ear sensors specifically designed for construction workers. These sensors enable real-time and continuous integration of neurophysiological signals. A cognitive experiment was conducted to validate the proposed mental fatigue assessment method. Results demonstrated that all selected supervised classification models can accurately identify mental fatigue by using the recorded neurophysiological data, with evaluation metrics exceeding 80%. The long short-term memory model achieved an average accuracy of 92.437%. This study offers a theoretical framework and a practical approach for assessing the mental fatigue of on-site workers and provides a basis for the proactive management of occupational health and safety on construction sites. [ABSTRACT FROM AUTHOR]

Abstract Postoperative cognitive dysfunction, a common neurological complication in the perioperative period, seriously affects patient survival and prognosis. Its high incidence has made the study of postoperative cognitive dysfunction challenging. Whether the clinical application of dexmedetomidine, a potential neuroprotective drug, can reduce the incidence of postoperative cognitive dysfunction is controversial, although several potential mechanisms by which dexmedetomidine improves postoperative cognitive dysfunction have been identified; therefore, this remains an area in need of further exploration.

Resumen: La neuroeducación permite comprender el proceso de aprendizaje desde la neurociencia. Este conocimiento facilita a los académicos entender la importancia de las emociones y de las funciones ejecutivas en el aprendizaje. A partir de este fundamento neurocientífico, este artículo aborda las principales características de los procesos cerebrales que se dan en metodologías activas. Ello permite al lector valorar su importancia y comprender por qué las metodologías activas deben ser consideradas como metodologías de primera elección para el logro de aprendizajes profundos. De manera particular, se revisa la estrategia de la simulación clínica alineada con las metodologías activas utilizadas en la formación de profesionales de la salud. A modo de conclusión, se releva la importancia de la neuroeducación para una implementación efectiva y consciente de la simulación clínica, no sólo desde la técnica de la ejecución, sino desde la fundamentación neurocientífica, permitiendo el óptimo beneficio por parte de los estudiantes del área de la salud. Abstract: Neuroeducation allows us to understand the learning process from a neuroscience standpoint. This knowledge makes it easier for academics to understand the importance of emotions and executive functions in learning. Based on this neuroscientific foundation, this article addresses the main characteristics of the brain processes that occur in active methodologies. This allows the reader to assess their importance and understand why active methodologies should be considered first choice methodologies for achieving deep learning. In particular, the clinical simulation strategy is reviewed, aligned with the active methodologies used in the training of health professionals. In conclusion, the importance of neuroeducation is highlighted for an effective and conscious implementation of clinical simulation, not only from the execution technique, but also from the neuroscientific foundation, which allows optimal benefit on the part of the students in the area of health.

Abstract Background Cognitive impairment presents in both adolescent-onset(ado-OP) and adult-onset psychosis(adu-OP). Age and neurodevelopmental factors likely contribute to cognitive differences. This study aimed to characterize cognitive functions in ado-OP compared to adu-OP in a clinical population with drug-naive first-episode psychosis(FEP). Methods A total of 788 drug-naive patients with FEP and 774 sex- and age-matched healthy controls(HCs) were included. Participants were divided into four groups by whether they were under or over 21 years of age: adolescent-onset FEP(ado-FEP, n = 380), adult-onset FEP(adu-FEP, n = 408), adolescent HC(ado-HC, n = 334), and adult HC(adu-HC, n = 440). Comprehensive cognitive assessments were performed using the MATRICS Cognitive Consensus Battery(MCCB), covers six cognitive domains: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning, and problem-solving. Data analyses were conducted using correlation analyses and binary logistic regression. Results The patterns of cognitive domain differences between ado-FEP and adu-FEP were found to be similar to those between ado-HC and adu-HC, whereas cognitive impairments appeared to be more pronounced in patients with adu-OP than ado-OP. The mazes subtest had the maximum effect size(ES) in the FEP(ES = 0.37) and HC(ES = 0.30) groups when comparing the adolescent and adult groups. Cognitive subtests were mostly significantly correlated with negative symptoms, especially for adolescents with FEP, in which all the subtests were significantly correlated with negative symptoms in the ado-FEP group. Better performance in the domains of spatial cognition and problem-solving abilities was more likely in the ado-FEP group than in the adu-FEP group. Conclusions These findings suggest cognitive differences between adolescents and adults but similar patterns of affected domains in HCs and patients with FEP. Therefore, the development of targeted cognitive interventions tailored to the specific needs of different age groups appears warranted.

Abstract The rapid advancement of artificial intelligence (AI) has sparked renewed discussions on its trustworthiness and the concept of eXplainable AI (XAI). Recent research in neuroscience has emphasized the relevance of XAI in studying cognition. This scoping review aims to identify and analyze various XAI methods used to study the mechanisms and features of cognitive function and dysfunction. In this study, the collected evidence is qualitatively assessed to develop an effective framework for approaching XAI in cognitive neuroscience. Based on the Joanna Briggs Institute and preferred reporting items for systematic reviews and meta‐analyses extension for scoping review guidelines, we searched for peer‐reviewed articles on MEDLINE, Embase, Web of Science, Cochrane Central Register of Controlled Trials, and Google Scholar. Two reviewers performed data screening, extraction, and thematic analysis in parallel. Twelve eligible experimental studies published in the past decade were included. The results showed that the majority (75%) focused on normal cognitive functions such as perception, social cognition, language, executive function, and memory, while others (25%) examined impaired cognition. The predominant XAI methods employed were intrinsic XAI (58.3%), followed by attribution‐based (41.7%) and example‐based (8.3%) post hoc methods. Explainability was applied at a local (66.7%) or global (33.3%) scope. The findings, predominantly correlational, were anatomical (83.3%) or nonanatomical (16.7%). In conclusion, while these XAI techniques were lauded for their predictive power, robustness, testability, and plausibility, limitations included oversimplification, confounding factors, and inconsistencies. The reviewed studies showcased the potential of XAI models while acknowledging current challenges in causality and oversimplification, particularly emphasizing the need for reproducibility.

Abstract Sound recognition is effortless for humans but poses a significant challenge for artificial hearing systems. Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have recently surpassed traditional machine learning in sound classification. However, current DNNs map sounds to labels using binary categorical variables, neglecting the semantic relations between labels. Cognitive neuroscience research suggests that human listeners exploit such semantic information besides acoustic cues. Hence, our hypothesis is that incorporating semantic information improves DNN’s sound recognition performance, emulating human behaviour. In our approach, sound recognition is framed as a regression problem, with CNNs trained to map spectrograms to continuous semantic representations from NLP models (Word2Vec, BERT, and CLAP text encoder). Two DNN types were trained: semDNN with continuous embeddings and catDNN with categorical labels, both with a dataset extracted from a collection of 388,211 sounds enriched with semantic descriptions. Evaluations across four external datasets, confirmed the superiority of semantic labeling from semDNN compared to catDNN, preserving higher-level relations. Importantly, an analysis of human similarity ratings for natural sounds, showed that semDNN approximated human listener behaviour better than catDNN, other DNNs, and NLP models. Our work contributes to understanding the role of semantics in sound recognition, bridging the gap between artificial systems and human auditory perception.