High-level Processing Research Articles

Modulation of movement-related oscillatory signatures by cognitive interference in healthy aging.

Age-related changes in the neurophysiology underlying motor control are well documented, but whether these changes are specific to motor function or more broadly reflect age-related alterations in fronto-parietal circuitry serving attention and other higher-level processes remains unknown. Herein, we collected high-density magnetoencephalography (MEG) in 72 healthy adults (age 28-63years) as they completed an adapted version of the multi-source interference task that involved two subtypes of cognitive interference (i.e., flanker and Simon) and their integration (i.e., multi-source). All MEG data were examined for age-related changes in neural oscillatory activity using a whole-brain beamforming approach. Our primary findings indicated robust behavioral differences in task performance based on the type of interference, as well as stronger beta oscillations with increasing age in the right dorsolateral prefrontal cortices (flanker and multi-source conditions), left parietal (flanker and Simon), and medial parietal regions (multi-source). Overall, these data indicate that healthy aging is associated with alterations in higher-order association cortices that are critical for attention and motor control in the context of cognitive interference.

Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.

Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.

Dynamics of cognitive functions using the rehabilitation program of cognitive training

To study the efficiency of the neuropsychological rehabilitation of patients with mild cognitive impairments. The study included 103 elderly people, aged 59 to 88 years, including 90 women and 13 men with subjective complaints of memory loss (ICD-10: F06.70; F06.71; F06.78; F06.79). Participants were divided into experimental group (n=43) and comparison group (n=42). The estimated impact was the rehabilitation program «The psychosocial therapy and neurocognitive rehabilitation of elderly patients with cognitive impairments», within which the principle of complex stimulation of various parameters of the cognitive sphere was used in rehabilitation work with patients in the experimental group in accordance with the «Memory Clinic» program. The study was conducted using randomized, equalized comparison groups, and the principle of «triple-blind» research. Non-parametric statistics (SPSS) methods were used to assess differences. A significant difference between the comparison and experimental groups has been identified, primarily in relation to high-level mental processes associated with the function of the third structural-functional block according to A.R. Luria. After the training, the number of correct answers significantly increased (t(42)=-2.67, p<0.001) in the experimental group, while in the comparison group the indicator did not change (t(41)=0.50, p=0.617). The number of false alarms in the experimental group decreased significantly (t(42)=2.13, p=0.039). The results confirm the leading role of these processes in the hierarchy of mental functions, which suggests that they should primarily be targets of rehabilitation interventions.

The effects of reward and punishment on the performance of ping-pong ball bouncing.

Reward and punishment modulate behavior. In real-world motor skill learning, reward and punishment have been found to have dissociable effects on optimizing motor skill learning, but the scientific basis for these effects is largely unknown. In the present study, we investigated the effects of reward and punishment on the performance of real-world motor skill learning. Specifically, three groups of participants were trained and tested on a ping-pong ball bouncing task for three consecutive days. The training and testing sessions were identical across the three days: participants were trained with their right (dominant) hand each day under conditions of either reward, punishment, or a neutral control condition (neither). Before and after the training session, all participants were tested with their right and left hands without any feedback. We found that punishment promoted early learning, while reward promoted late learning. Reward facilitated short-term memory, while punishment impaired long-term memory. Both reward and punishment interfered with long-term memory gains. Interestingly, the effects of reward and punishment transferred to the left hand. The results show that reward and punishment have different effects on real-world motor skill learning. The effects change with training and transfer readily to novel contexts. The results suggest that reward and punishment may act on different learning processes and engage different neural mechanisms during real-world motor skill learning. In addition, high-level metacognitive processes may be enabled by the additional reinforcement feedback during real-world motor skill learning. Our findings provide new insights into the mechanisms underlying motor learning, and may have important implications for practical applications such as sports training and motor rehabilitation.

Dynamic 3D Point Cloud Sequences as 2D Videos.

Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in both spatial and temporal domains poses significant challenges to effective and efficient processing. Existing deep point cloud sequence modeling approaches imitate the mature 2D video learning mechanisms by developing complex spatio-temporal point neighbor grouping and feature aggregation schemes, often resulting in methods lacking effectiveness, efficiency, and expressive power. In this paper, we propose a novel generic representation called Structured Point Cloud Videos (SPCVs). Intuitively, by leveraging the fact that 3D geometric shapes are essentially 2D manifolds, SPCV re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points. The structured nature of our SPCV representation allows for the seamless adaptation of well-established 2D image/video techniques, enabling efficient and effective processing and analysis of 3D point cloud sequences. To achieve such re-organization, we design a self-supervised learning pipeline that is geometrically regularized and driven by self-reconstructive and deformation field learning objectives. Additionally, we construct SPCV-based frameworks for both low-level and high-level 3D point cloud sequence processing and analysis tasks, including action recognition, temporal interpolation, and compression. Extensive experiments demonstrate the versatility and superiority of the proposed SPCV, which has the potential to offer new possibilities for deep learning on unstructured 3D point cloud sequences. Code will be released at https://github.com/ZENGYIMING-EAMON/SPCV.

An intelligent Fusion Framework for Risk Assessment of Autonomous Ship through Functional Mapping Criterion Sub-Intervals into Single Interval Method

Nowadays, intelligent information technology can implement high-level information processing and decision-making activities that can support risk assessment of autonomous. Risk assessment is a critical process for deploying autonomous ships, ensuring these innovative vessels' safe and efficient operation. There is a need to identify, analyze, and mitigate potential risks associated with system reliability, collision avoidance, cybersecurity, environmental conditions, human interaction, regulatory compliance, sensor performance, data integrity, emergency response, and testing and validation. This work provides an overview of the essential considerations and objectives of risk assessment in autonomous boats. We used the multi-criteria decision-making model to deal with various criteria. The Ranking of Alternatives through Functional Mapping Criterion Sub-Intervals into Single Interval (RAFSI) method is applied to rank the alternatives. We used the ten criteria and twenty options in this study. The results show that the proposed framework can provide a comprehensive risk assessment framework that can enable stakeholders to gain insights into potential hazards and vulnerabilities unique to autonomous ships.

GANN: EEG-based Emotion Classification Using Context-Aware Gated Attention Neural Network

Electroencephalography (EEG) is a valuable, non-invasive method for monitoring the brain's electrical activity and assessing high-level cognitive processes, including emotions. Despite its benefits, interpreting EEG data is challenging, complex, and prone to human error. This study introduces an advanced machine learning model aimed at improving EEG-based emotion classification by fully utilizing the spatial and temporal characteristics of EEG channels. Our proposed model combines Gated Recurrent Units (GRU) and Attention Mechanisms, significantly enhancing feature learning and model generalization across diverse datasets. Achieving a classification accuracy of 94% on the EEG Brainwave Dataset, our model outperforms existing methods. This work not only refines emotion recognition techniques but also establishes a benchmark for future research in effectively utilizing EEG data.

Design and evaluation of a global workspace agent embodied in a realistic multimodal environment.

As the apparent intelligence of artificial neural networks (ANNs) advances, they are increasingly likened to the functional networks and information processing capabilities of the human brain. Such comparisons have typically focused on particular modalities, such as vision or language. The next frontier is to use the latest advances in ANNs to design and investigate scalable models of higher-level cognitive processes, such as conscious information access, which have historically lacked concrete and specific hypotheses for scientific evaluation. In this work, we propose and then empirically assess an embodied agent with a structure based on global workspace theory (GWT) as specified in the recently proposed "indicator properties" of consciousness. In contrast to prior works on GWT which utilized single modalities, our agent is trained to navigate 3D environments based on realistic audiovisual inputs. We find that the global workspace architecture performs better and more robustly at smaller working memory sizes, as compared to a standard recurrent architecture. Beyond performance, we perform a series of analyses on the learned representations of our architecture and share findings that point to task complexity and regularization being essential for feature learning and the development of meaningful attentional patterns within the workspace.

La carte conceptuelle : un soutien au transfert des apprentissages métalinguistiques

The Concept Map: A Support for Learning Transfer&#x0D; Based on the concept mapping technique, our research endeavors to experimentally address the challenges encountered by candidates taking the Baccalaureate exam, specifically related to their ability to employ interdisciplinary knowledge while inferentially processing texts in French as a foreign language (FLE). The study intersects two disciplines: French as a Foreign Language (FLE) and History courses in the first language (L1). Employing a three-phase action research approach—commencing with a pre-test involving a text recall task, followed by collaborative concept map design using the PREP technique (Langer, 1982-1984) as a metacognitive experience, and concluding with a post-test to validate our approach—we aim to surpass the limited capacity of working memory and facilitate in-depth processing of textual information. By incorporating the concept map into our experimental approach, we aim to simulate "reflection on cognition," aligning with the training in internal monitoring—a central goal in any educational institution. Our initiative addresses linguistic challenges arising from the mental transition between the first language (L1) and the second language (L2), with a particular focus on challenges associated with mental translation. Situated within a plurilingual context involving Classical Arabic (L1) and the second language, French (FLF), we seek to visualize the linguistic interactions occurring during information processing through schematic organization and hierarchization. Resulted revealed that the use of concept maps substantially supports strategies related to analogical reasoning and high-level text processing. This approach significantly enhances learners' lexical repertoire by shaping organizational strategies and facilitating the storage of conceptual knowledge, streamlining retrieval processes. While concept maps undeniably bolster language skills, our research goes beyond this acknowledgement. It not only highlights the positive impact of integrating concept maps for language skill improvement but also emphasizes the untapped potential for even greater significance and promise. The instructional intervention has shed light on specific elements educators should consider to optimize outcomes. As a tool for facilitating transfer learning, the concept map has proven significant, albeit not without some negative transfers. Adjusting teaching strategies based on individual student needs and characteristics is essential to harness the full potential of this approach.

Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset

Macro-management is a crucial aspect of real-time strategy (RTS) games like StarCraft II, which involves high-level decision-making processes such as resource allocation, unit production, and technology development. The MSC dataset, as presented in the original paper, provided an initial platform for researchers to investigate macro-management tasks using deep learning models. However, there are limitations to the dataset and existing baseline models that call for improvement. In this paper, we discuss the challenges and opportunities in enhancing macro-management research in StarCraft II. We propose improvements to the dataset by incorporating new features, addressing data imbalance, and updating preprocessing techniques. Furthermore, we review recent research and state-of-the-art methods in RTS games, such as attention mechanisms, graph neural networks, and reinforcement learning, that could be applied to improve existing tasks or introduce new research directions. We also present our experimental results, highlighting the effectiveness of the proposed improvements and novel approaches. Our goal is to inspire the research community to explore advanced AI techniques and strategies in macro-management and contribute to the development of more capable AI agents in complex RTS games.

Investigating the relationship between self-regulated learning, metacognition, and executive functions by focusing on academic transition phases: a systematic review

The importance of self-regulated learning (SRL) for academic learning and achievement is already well established. In terms of developing a comprehensive understanding of SRL, executive functions (EFs), which are seen as an important influential factor for learning and goal-oriented behavior, should be taken into consideration. Some authors have linked SRL and EF via metacognition (MC), which forms a fundamental component of SRL, and like EF, represents higher-level cognitive processes. Therefore, this systematic review searched education and psychology databases to determine the natural development and current state of research on the three constructs. Academic transitional periods were brought into focus because of their influence on learners’ well-being and academic achievement. An evaluation of 30 publications indicated that strong development of simple EFs occurs before the transition from preschool to primary school. Moreover, there is a decrease in the motivational component of SRL and the use of metacognitive SRL strategies during the transition from primary to secondary education but an increase in metacognitive awareness. Simple as well as complex EFs also increase during this transition, with a later developmental peak for complex EF. The transition from secondary to tertiary education is accompanied by positive developments in the cognitive and metacognitive components of SRL, with small increases for simple EFs, and larger increases for complex EFs. In conclusion, the findings suggest there is an early developmental maximum for EFs compared to SRL and MC, which supports the theory that EFs are the foundation for SRL and MC.

Assessment of the regional potential of technological sovereignty of the Russian Federation

Aim. To identify the regions with a high potential for ensuring technological sovereignty of the Russian Federation (RF).Objectives. To perform a structural analysis of non-resource exports and imports of the RF regions; to propose a methodological approach to the development of typology of regions according to the criterion “the level of import dependence of production”; to identify the regional nuclei of technological sovereignty of the Russian Federation.Methods. Two-stage structural analysis of statistical data on regional exports and imports with the allocation of the share of goods with low and high level of processing, as well as with the definition of the purpose of products: production or for consumption by the population. The choice of the research period is conditioned by the absence in 2019 of the influence of the factor of the decline in global production during the COVID-19 pandemic in the following years, as well as the absence of the pressure exerted by sanctions restrictions.Results. 12 types of Russian regions were defined according to the criterion “high, medium and low levels of technological import dependence of the industrial complex”. The second level of the regional typologization depth is determined by the ratio of such indicators as the share of industrial technological export of the region in the country’s export, the share of industrial technological export of the region in the country’s technological export, the share of industrial technological import of the region in the country’s import, the share of industrial technological import of the region in the country’s technological import. Structural analysis allowed us to identify the regions - the nuclei of technological sovereignty of the country.Conclusions. First of all, it is necessary to study the experience of economic activity of seven regions that have exports of technological products at a high level with a low level of technological import dependence of industry. Among these regions are the Irkutsk, Arkhangelsk, Penza oblasts, the Republic of Bashkortostan, Altai Krai, the Republic of Mari El, and the Komi Republic. Two leading regions of the listed subjects of the Russian Federation deserve special attention: the Irkutsk Oblast and the Republic of Bashkortostan. It is important to highlight the most successful cases of their sectoral development to be used in other territories in order to ensure national technological sovereignty.

Experimental study on crashworthiness and lightweight of cutting-type energy-absorbing structure of magnesium alloy for trains

Cutting-type structures, because of their desirable mechanical characteristics, high energy absorption capabilities and excellent guiding performance, has been extensively employed in high-speed trains. Its lightweight design and optimization problems are also the hotspot of research directions in present. This paper designed a new-type structure, i.e., magnesium alloy cutting-type energy-absorbing structure (CTEAS), taking into account its excellent properties of high lightweight level and ease of processing demonstrated through AZ31B magnesium alloy tensile mechanical test. Subsequently, experimental study on magnesium alloy CTEAS are conducted by trolley impact tests of train considering different cutting depths and different velocity levels. The results confirmed the outstanding advantages, including high lightweight level and higher absorbed energy properties, and lower demands of knives, compared with the structures with Q345 steel. In addition, the mechanism of knives cracking and melting phenomenon found in the experiment is explored by finite element analytical method. The results show that the temperature difference between the high-temperature surface and the interior during the cutting Q345 steel process is maybe the key influencing factor leading to knives damage. This research provides an effective tool for the design and analysis of excellent performance energy absorbing structure of high-speed trains.

Hierarchical processing enabled by 2D ferroelectric semiconductor transistor for low-power and high-efficiency AI vision system

The growth of data and Internet of Things challenges traditional hardware, which encounters efficiency and power issues owing to separate functional units for sensors, memory, and computation. In this study, we designed an α-phase indium selenide (α-In2Se3) transistor, which is a two-dimensional ferroelectric semiconductor as the channel material, to create artificial optic-neural and electro-neural synapses, enabling cutting-edge processing-in-sensor (PIS) and computing-in-memory (CIM) functionalities. As an optic-neural synapse for low-level sensory processing, the α-In2Se3 transistor exhibits a high photoresponsivity (2855 A/W) and detectivity (2.91 × 1014 Jones), facilitating efficient feature extraction. For high-level processing tasks as an electro-neural synapse, it offers a fast program/erase speed of 40 ns/50 µs and ultralow energy consumption of 0.37 aJ/spike. An AI vision system using α-In2Se3 transistors has been demonstrated. It achieved an impressive recognition accuracy of 92.63% within 12 epochs owing to the synergistic combination of the PIS and CIM functionalities. This study demonstrates the potential of the α-In2Se3 transistor in future vision hardware, enhancing processing, power efficiency, and AI applications.

The role of the primary sensorimotor system in generalized epilepsy: Evidence from the cerebello-cerebral functional integration.

The interaction between cerebellum and cerebrum participates widely in function from motor processing to high-level cognitive and affective processing. Because of the motor symptom, idiopathic generalized epilepsy (IGE) patients with generalized tonic-clonic seizure have been recognized to associate with motor abnormalities, but the functional interaction in the cerebello-cerebral circuit is still poorly understood. Resting-state functional magnetic resonance imaging data were collected for 101 IGE patients and 106 healthy controls. The voxel-based functional connectivity (FC) between cerebral cortex and the cerebellum was contacted. The functional gradient and independent components analysis were applied to evaluate cerebello-cerebral functional integration on the voxel-based FC. Cerebellar motor components were further linked to cerebellar gradient. Results revealed cerebellar motor functional modules were closely related to cerebral motor components. The altered mapping of cerebral motor components to cerebellum was observed in motor module in patients with IGE. In addition, patients also showed compression in cerebello-cerebral functional gradient between motor and cognition modules. Interestingly, the contribution of the motor components to the gradient was unbalanced between bilateral primary sensorimotor components in patients: the increase was observed in cerebellar cognitive module for the dominant hemisphere primary sensorimotor, but the decrease was found in the cerebellar cognitive module for the nondominant hemisphere primary sensorimotor. The present findings suggest that the cerebral primary motor system affects the hierarchical architecture of cerebellum, and substantially contributes to the functional integration evidence to understand the motor functional abnormality in IGE patients.

DYS-SLAM: A real-time RGBD SLAM combined with optical flow and semantic information in a dynamic environment1

Traditional Simultaneous Localization and Mapping application in dynamic situations is constrained by static assumptions. However, the majority of well-known dynamic SLAM systems use deep learning to identify dynamic objects, which creates the issue of trade-offs between accuracy and real-time. To tackle this issue, this work suggests a unique dynamic semantics method(DYS-SLAM) for semantic simultaneous localization and mapping that strikes a trade-off between high accuracy and high real-time performance. We propose M-LK, an enhanced Lucas-Kanade(LK) optical flow method. This technique keeps the continuous motion and greyscale consistency assumptions from the original method while switching out the spatial consistency assumption for a motion consistency assumption, reducing sensitivity to image gradients to identify dynamic feature points and regions efficiently. In order to increase segmentation accuracy while maintaining real-time performance, we develop a segmentation refinement scheme that projects 3D point cloud segmentation results into 2D object detection zones. A dense semantic octree graph is built in the interim to expedite the high-level process. Compared to the four equivalent dynamic SLAM approaches, experiments on the publicly available TUM RGB-D dataset demonstrate that the DYS-SLAM method offers competitive localization accuracy and satisfactory real-time performance in both high and low-dynamic scenarios.

Overnight exposure to pink noise could jeopardize sleep-dependent insight and pattern detection.

Accumulated evidence from the past decades suggests that sleep plays a crucial role in memory consolidation and the facilitation of higher-level cognitive processes such as abstraction and gist extraction. In addition, recent studies show that applying pink noise during sleep can further enhance sleep-dependent memory consolidation, potentially by modulating sleep physiology through stochastic resonance. However, whether this enhancement extends to higher cognitive processes remains untested. In this study, we investigated how the application of open-loop pink noise during sleep influences the gain of insight into hidden patterns. Seventy-two participants were assigned to three groups: daytime-wake, silent sleep, and sleep with pink noise. Each group completed the number reduction task, an established insight paradigm known to be influenced by sleep, over two sessions with a 12-h interval. Sleep groups were monitored by the DREEM 3 headband in home settings. Contrary to our prediction, pink noise did not induce an increase in insight compared to silent sleep and was statistically more similar to the wake condition despite evidence for its typical influence on sleep physiology. Particularly, we found that pink noise limited the time spent in the initial cycle of N1 just after sleep onset, while time spent in N1 positively predicted insight. These results echo recent suggestions that the time in the initial cycle of N1 plays a critical role in insight formation. Overall, our results suggest that open-loop pink noise during sleep may be detrimental to insight formation and creativity due to the alterations it causes to normal sleep architecture.

Local but not global graph theoretic measures of semantic networks generalize across tasks.

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory.

Neurophysiological explorations across the spectrum of psychosis, autism, and depression, during wakefulness and sleep: protocol of a prospective case–control transdiagnostic multimodal study (DEMETER)

BackgroundQuantitative electroencephalography (EEG) analysis offers the opportunity to study high-level cognitive processes across psychiatric disorders. In particular, EEG microstates translate the temporal dynamics of neuronal networks throughout the brain. Their alteration may reflect transdiagnostic anomalies in neurophysiological functions that are impaired in mood, psychosis, and autism spectrum disorders, such as sensorimotor integration, speech, sleep, and sense of self. The main questions this study aims to answer are as follows: 1) Are EEG microstate anomalies associated with clinical and functional prognosis, both in resting conditions and during sleep, across psychiatric disorders? 2) Are EEG microstate anomalies associated with differences in sensorimotor integration, speech, sense of self, and sleep? 3) Can the dynamic of EEG microstates be modulated by a non-drug intervention such as light hypnosis?MethodsThis prospective cohort will include a population of adolescents and young adults, aged 15 to 30 years old, with ultra-high-risk of psychosis (UHR), first-episode psychosis (FEP), schizophrenia (SCZ), autism spectrum disorder (ASD), and major depressive disorder (MDD), as well as healthy controls (CTRL) (N = 21 × 6), who will be assessed at baseline and after one year of follow-up. Participants will undergo deep phenotyping based on psychopathology, neuropsychological assessments, 64-channel EEG recordings, and biological sampling at the two timepoints. At baseline, the EEG recording will also be coupled to a sensorimotor task and a recording of the characteristics of their speech (prosody and turn-taking), a one-night polysomnography, a self-reference effect task in virtual reality (only in UHR, FEP, and CTRL). An interventional ancillary study will involve only healthy controls, in order to assess whether light hypnosis can modify the EEG microstate architecture in a direction opposite to what is seen in disease.DiscussionThis transdiagnostic longitudinal case–control study will provide a multimodal neurophysiological assessment of clinical dimensions (sensorimotor integration, speech, sleep, and sense of self) that are disrupted across mood, psychosis, and autism spectrum disorders. It will further test the relevance of EEG microstates as dimensional functional biomarkers.Trial registrationClinicalTrials.gov Identifier NCT06045897.

Electrophysiological effects of smartphone notifications on cognitive control following a brief mindfulness induction

Smartphone use is nearly ubiquitous, with 93% of adults among economically developed countries, including the United States, Canada, Israel, and South Korea owning a smartphone (Taylor & Silver, 2019). Multiple studies have demonstrated the distracting effects of smartphone notifications on behavioral measures of cognition. Fewer studies have examined the effects of notifications on neural activity underlying higher-level cognitive processes or behavioral inductions to reduce smartphone-related distraction. Using EEG spectral frequency power densities, we assessed the effects of smartphone notifications (vs. control trials) on engagement of attentional shifting processes involved in cognitive control during a Navon Letter visual oddball task. Participants were randomly assigned to a brief mindfulness induction (N = 44) or a neutral narration control condition (N = 43). Overall, participants had lower theta-band power, but higher alpha- and beta-band power densities on target letter trials preceded by smartphone notifications. Additionally, participants in the mindfulness (vs. control) condition had a larger attention shifting oddball assessed via theta power density and theta/beta ratio (TBR) values—reflecting increased engagement of cognitive control—particularly on smartphone notification (vs. control) trials. Altogether, these results provide evidence supporting the idea that smartphone notifications can decrease activity of neural correlates of cognitive control, and offer the promise of a brief mindfulness induction to buffer against the effects of smartphone notifications on cognitive control. The findings indicate a need for further research on mindfulness inductiosn as a means to reduce potential distraction caused by smartphones.