Under the guidance of traditional biomedical models, most studies have attributed the social communication deficits in autism to their own social shortcomings. However, the “Double Empathy” problem is rooted in the bidirectional nature of social communication. Autistic individuals face difficulties in integrating into the social environment not only because they themselves struggle to understand typical neurotypical individuals, but also because neurotypical individuals find it challenging to understand autism. Due to a mismatch in perception and understanding between autistic and neurotypical individuals, there is a bidirectional disconnect of information, thereby resulting in difficulties in their interactions. A number of researchers have studied the “Double Empathy” problem to understand the specific types of social signals between autistic and neurotypical individuals and the actual interactions between the two parties. Previous studies have shown that neurotypical individuals have difficulties understanding the psychological states of autistic individuals, often leading to the rapid formation of prejudices and a reduced willingness to interact with autistic individuals. However, autistic individuals experienced increased pleasure, better understanding, and reduced stress when interacting with others who was diagnosed as autism. Pooling findings from multiple multi-class research studies, it becomes evident that autistic individuals face a dual empathy challenge when socializing with neurotypical individuals. Further examination reveals that this issue arises from differences in behavioral expressions between both parties and the presence of stigmatization.

Certain interventions have aimed to adjust the behaviors of autistic individuals to conform to neurotypical norms, but they may not result in long-term benefits and could inadvertently encourage pretense, impose pressure to change, and heighten anxiety in autistic individuals. Several innovative interventions are available to tackle the root issues associated with the "Double Empathy" problem. One such intervention involves shared reading patterns, which facilitate joint contemplation of literature and improve comprehension of diverse thought processes. Additionally, peer support initiatives help in nurturing a positive self-image and fostering a stronger sense of belonging through mutual assistance. Furthermore, interpersonal synchronization has the potential to enhance social connections, promote closeness, and foster intimacy without attempting to alter behaviors that may be considered atypical for autistic individuals.

Several shortcomings in the current research on the “Double Empathy” problem point to areas for future investigation. Firstly, the research has limitations in its target group, as it does not encompass individuals in childhood—a crucial period for social interaction development—and employs a relatively small sample size. Future research should broaden its scope to include individuals of all ages to explore both commonalities and disparities between groups, thereby forming a developmental understanding of social interaction in both groups. Second, neurotypical individuals have difficulties understanding the expressions of autistic individuals, which may underestimate the empathy abilities of autistic individuals. Future research should consider the distinctive ways in which autistic individuals express empathy and respond to it. This can be achieved through interviews with autistic individuals, interactions with autistic individuals’ family members, and by incorporating multiple perspectives to gain a more accurate understanding of the intentions of autistic individuals. Additionally, research should explore how neurotypical individuals interpret the visual signals conveyed by autistic individuals. This could be accomplished using eye-tracking technology or brain imaging methods to delve deeper into the characteristics of these interactions. Thirdly, it's worth noting that the current studies are predominantly situated within a Western cultural context, lacking diversity in cultural backgrounds. Future research should pay attention to the potential impact of cultural environments on the “Double Empathy” problem.

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by high prevalence, heterogeneity, and lifelong impact. Individuals on the autism spectrum exhibit difficulities with social communication and restricted, repetitive patterns of behaviour, interests, or activities. The underlying neural mechanisms of ASD remain largely unknown. Multimodal magnetic resonance imaging (MRI) has emerged as a novel tool to unveil the neuroimaging mechanisms of ASD. Studies utilizing single-modal MRI have already revealed widespread abnormalities in brain structure, function, and network connectivity in individuals on the ASD. Here, we systematically reviewed the findings of ASD magnetic resonance brain imaging research, including three levels: structure, function, and brain network. ASD exhibits a wide range of anomalies, involving gray matter volume, cortical thickness, functional activation, functional connectivity, dynamic functional connectivity, white matter fiber connectivity, fractional anisotropy, mean diffusivity, and functional network properties. The affected regions include the amygdala, fusiform gyrus, orbitofrontal cortex, medial prefrontal cortex, anterior cingulate cortex, superior temporal sulcus, and insula, many of which are implicated in the 'social brain' network. Additionally, we summarized the research on multimodal fusion of MRI from three aspects, including image-level fusion, feature-level fusion, and decision-level fusion. Feature-level fusion analysis is the most commonly used analytical framework, including feature coupling, feature joint screening, similarity network model, and large-scale neural circuit model. However, research on multimodal fusion analysis in autism is still in its early stages. Furthermore, research on ASD classification based on magnetic resonance imaging is gradually emerging, including traditional machine learning frameworks and deep learning models, but the current classification accuracy still needs to be improved. Meanwhile, in order to parse the heterogeneity within ASD, investigators have identified 2~4 neurosubtypes based on multimodal images. In the diagnostic and statistical manual of mental disorders, ASD has been considered as a spectrum, that is, individuals on the autism spectrum do not cluter into different neurosubtypes; instead, individuals on the autism spectrum are organized along continous dimensions. However, it is difficult to detect the multidimensional space of neuroaabtomy or neurofunction in ASD due to ‘the curse of dimensionality’. Future research can be based on multimodal brain image fusion technology, developing a low-dimensional, personalized, and parameterized analytical framework to comprehensively reveal the mechanisms underlying neural abnormalities in ASD, search for imaging biomarkers with classification and recognition ability, and provide an objective basis for the auxiliary diagnosis and subtype classification of ASD. Through a review of ASD brain imaging research, it was found that most of the abnormal areas were concentrated in the 'social brain' network, which is the brain area most affected by ASD at different levels. Transcranial magnetic stimulation, as a non-invasive neuroregulatory technique, has been widely applied in clinical research and has become a new choice for the treatment of neurodevelopmental disorders and mental disorders, including ASD. We recommend that future research can use key nodes in the 'social brain' network, such as the dorsolateral prefrontal cortex, as stimulation areas to improve social impairments in ASD. Future research also needs to explore imaging biomarkers with early diagnostic capabilities based on multisite and large-sample data, establish generalizable and robust ASD early warning and diagnostic models, and achieve early diagnosis and intervention. On this basis, an efficacy evaluation model based on multimodal brain imaging is established, and different intervention strategies are formulated for different subtypes/dimensions of ASD, optimizing traditional single treatment plans and providing an objective basis for achieving precise diagnosis and treatment in clinical practice.

Despite occupying only 10% of the total volume of the human brain, the cerebellum contains over half of its neurons. Traditionally viewed as primarily responsible for coordinating motor movements, recent research has shown that the cerebellum is also closely associated with higher cognitive functions such as learning, attention, memory, and language, and participates in regulating various non-motor functions. Concurrently, abnormal cerebellar development has been closely linked to several neurodevelopmental disorders, including Autism Spectrum Disorder (ASD), characterized by social impairments, repetitive behaviors, and language deficits, often accompanied by sensory abnormalities. Clinical studies have found structural and functional alterations in the cerebellum of individuals with autism. For example, studies show that individuals with autism have reduced cerebellar volume, particularly in certain regions of the cerebellar hemispheres. Additionally, functional magnetic resonance imaging (fMRI) studies have found abnormal activity patterns in the cerebellum of autism patients when processing social information and performing executive function tasks. Interestingly, similar cerebellar phenotypes, such as cerebellar neuron dysplasia and dysfunction, have been observed in animal models of autism spectrum disorders. Importantly, specific knockout of autism susceptibility genes in cerebellar neurons can induce typical autism-like behaviors in model animals, suggesting that abnormal cerebellar development is a key pathological mechanism underlying autism spectrum disorders. This article will briefly summarize the relationship between the cerebellum and Autism Spectrum Disorder from both clinical and basic research perspectives, providing new insights for the diagnosis and treatment of autism.

Currently, research and clinical trials targeting the cerebellum as a treatment focus for ASD are gradually progressing. Early studies suggest that improving cerebellar function and structure can significantly enhance social and cognitive functions in ASD patients. For example, some studies have improved cerebellar structure and function through behavioral interventions and physical training, thereby enhancing social skills and cognitive abilities in ASD patients. Neuroregulation and pharmacological interventions are believed to potentially improve ASD symptoms by modulating the activity of cerebellar neural circuits. Techniques such as neurofeedback, deep brain stimulation, and neural circuit modulation have shown some effectiveness in certain ASD patients. These techniques intervene in cerebellar regions through electrical stimulation or neuroregulatory devices to improve the functionality and integration of neural circuits, thus alleviating core symptoms of ASD.

In the future, personalized treatment strategies based on the cerebellum will be at the forefront of ASD treatment. As our understanding of cerebellar neural circuits and functions deepens, future research will focus on developing more personalized and precise treatment approaches. By integrating genomics and brain imaging technologies, specific subtypes of cerebellar abnormalities in ASD patients can be accurately identified, allowing for the selection of the most effective treatment methods. For example, analyzing genetic variations in autism patients through genomics can provide a basis for individualized treatment. Collaborative interdisciplinary research teams will drive a comprehensive analysis of the relationship between the cerebellum and ASD, combining neuroscience, genetics, psychology, and clinical medicine to offer new perspectives and treatment pathways for understanding the complex neurodevelopmental mechanisms of ASD.

However, targeting the cerebellum for ASD treatment has certain limitations. Firstly, ASD-related neurodevelopmental abnormalities involve complex interactions between multiple brain regions, so relying solely on cerebellar treatment may not fully address the etiology and symptoms. For example, autism may involve abnormalities in both the cerebral cortex and the cerebellum, suggesting that a comprehensive treatment approach might be more effective. Secondly, individual differences and heterogeneity among ASD patients mean that treatment effects targeting the cerebellum may vary, and the long-term efficacy and safety of current treatment strategies still need to be validated. Future research should continue to explore multi-faceted treatment strategies, including personalized and comprehensive interventions considering multiple brain regions, to enhance treatment outcomes and quality of life. These studies will help develop more comprehensive and effective treatment plans, providing better prognosis and quality of life for ASD patients.

Brain activity is regulated by gene expression, and altered gene expression can reflect the transformed brain state. With the development of brain imaging genomics, it is now possible to detect genomic changes associated with brain structure and function, combined with the functional magnetic resonance imaging (fMRI) and transcriptome analysis across cortical regions. At present, this research approach has been gradually applied to neuropsychiatric diseases, including autism spectrum disorders (ASD), to explore the relationship between gene expression and brain activity. The study of the mechanism of genes related to brain activity in the pathogenesis of ASD can provide physiological reference for the analysis of brain activity changes, the exploration of pathogenesis and the development of clinical treatment strategies of ASD patients. In this review, we summarized the differences in brain structure and function between ASD patients and healthy individuals, and the important genes related to these differences, including ANK2, CD38, CHD2, CHD7, CHD8, CNTNAP2, DIP2A, SHANK3, OXTR, GABRQ and SCN1B. According to their specific functions, we divide these genes into four categories: neural development, synaptic transmission, neurotransmitters and excitation-inhibition balance.

ANK2 mutations in patients with ASD can lead to social behavior disorders associated with higher executive functions, that’s because ANK2 is a regulator of early neurogenesis which regulates neural stem cell differentiation and neuronal migration, and its expression level affects the expression of other genes involved in neural development. CD38 deficiency in ASD patients can induce dysfunction of PI3K/AKT pathway, which affects normal neuronal growth and migration. CHD2, CHD7 and CHD8 are involved in the development of brain progenitors, the cortex and brain regions through different transcription factors. CNTNAP2, an ASD risk gene mainly affecting frontal lobe connectivity, functions to participate in the dendritic growth of cortical interneurons. ANK2, CD38, CHD2, CHD7, CHD8 and CNTNAP2, these genes all affect the brain structure and function of patients with ASD by participating in the process of neural development, so they are classified as neural development. DIP2A interacts with postsynaptic actin binding proteins, and its absence results in defective dendritic spine morphology, altered postsynaptic density structure, reduced number of synaptic transmission, reduced amplitude of miniature excitatory postsynaptic current and decrease of cortical acetylation, ultimately it will affect the excitability of brain activity. SHANK3 encodes multi-domain scaffold proteins in postsynaptic densification, which plays an important role in synaptic formation and dendritic spine maturation. Both DIP2A and SHANK3 act on the synaptic structure of neurons, and cause related symptoms of ASD by affecting the synaptic transmission function of neurons. The OXTR encodes the oxytocin receptor protein, which influences the binding of the neurotransmitter oxytocin, and which in turn affects brain activity in ASD patients. The down-regulation of GABRQ expression can reduce the number of GABA receptor subunits in parietal lobe, cerebellum and frontal lobe, and affect the activity of GABA-mediated neurons in the neural circuits of brain, which is the physiological basis of the excitation-inhibition imbalance of cerebral cortex in ASD patients. SCN1B of ASD patients is abnormally expressed in PV+ inhibitory interneurons, resulting in the abnormal activity of PV+ inhibitory interneurons in the cortex, which triggers a cortical excitation-inhibitory imbalance, and subsequently leads to cognitive and emotional disorders. GABRQ and SCN1B cause related symptoms in patients with ASD by affecting the excitation-inhibition balance of the cerebral cortex.

ASD is a complex multi-gene and multi-factor disease. The extensive phenotype of ASD is related to the mutation and deletion of one or a group of related genes, and is affected by environmental, social and other external factors. We classified the pathogenic mechanism of these risk genes into four different categories. Although some genes have cross-effects in different categories, it has more practical significance to take refined analysis for investigating how different genes affect brain activity and ASD pathogenesis, and also it provides potential ideas for treatment.

One of the core symptoms of Autism Spectrum Disorder (ASD) is social communication impairment, with atypical facial emotion expressions (FEEs) being a prominent feature. FEEs have the potential to serve as a biomarker for early ASD screening. Following the PRISMA guidelines, this study systematically reviewed the literature up to 2024, identifying six studies that investigated atypical FEEs in children with ASD aged 8 months to 6 years. The review aimed to characterize these atypical expressions and evaluate the application of computer vision technology for ASD identification.

The findings reveal that children with ASD exhibit three main atypical characteristics in FEEs: (1) Predominance of neutral expressions and reduced positive expressions. Children with ASD often display neutral or minimal emotional facial expressions during daily interactions and in response to emotional stimuli, reflecting challenges in emotional perception, social context comprehension, and emotional regulation. While the frequency of positive expressions increases with age, it remains significantly lower than that of neutral expressions. (2) Low frequency of social smiles. Social smiles, a hallmark of early social behavior, appear less frequently in children with ASD compared to typically developing (TD) peers. This difference is evident as early as infancy and persists throughout development. (3) Deficits in facial expression imitation. Compared to TD children, children with ASD show reduced intensity and frequency of imitation when observing others' facial expressions, particularly in recognizing and imitating complex emotional expressions. These deficits are closely linked to social cognition impairments and difficulties in emotional processing.

The growing demand for early ASD screening has driven advancements in computer vision and artificial intelligence technologies, providing new tools for the automatic recognition of FEEs. Compared to traditional methods, such as manual evaluations and electromyography (EMG), computer vision-based approaches offer significant advantages: (1) Non-invasive assessment. These techniques use cameras for data collection without disrupting the child, enabling the capture of natural facial expressions. (2) Multimodal data integration. By combining facial expression data with behavioral and physiological signals, these methods improve the accuracy and efficiency of emotion recognition. (3) Scalability. Computer vision automatic recognition technology overcome the efficiency limitations of traditional tools, supporting large-scale screening and facilitating early intervention.

Despite these advancements, challenges remain. Future research should prioritize the following: (1) Developing emotion-inducing paradigms that mimic naturalistic scenarios to enhance ecological validity; (2) Exploring the diverse features of FEEs in ASD across varying contexts and emotional valences to identify unique expression patterns; and (3) Improving the accuracy and sensitivity of computer vision automatic recognition to ensure their applicability across different age groups and cultural backgrounds. Addressing these challenges will provide robust support for early screening and intervention for children with ASD.

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain modulation technique that has gained increasing attention in the field of autism spectrum disorder (ASD) intervention in recent years. This study systematically reviews the literature on rTMS interventions for ASD from 2014 to 2024, focusing on its mechanisms of action, target selection, and the relationship with core symptoms, as well as future optimization directions.

Regarding the mechanisms of action, (1) low-frequency rTMS modulates cortical GABAergic neurotransmission to restore the excitatory-inhibitory (E/I) balance, thereby significantly improving repetitive and stereotypical behaviors as well as executive function deficits in ASD; (2) high-frequency rTMS enhances synaptic long-term potentiation (LTP) and regulates neurotransmitter transmission to increase the excitability of target regions and associated neural networks, contributing to the alleviation of social interaction impairments in ASD.

From the perspective of target selection, studies indicate that low-frequency rTMS targeting the dorsolateral prefrontal cortex (DLPFC) has shown significant effects in improving repetitive and stereotypical behaviors and executive function in ASD. High-frequency rTMS targeting the temporo-parietal junction (TPJ, including pSTS and IPL) has been found to significantly enhance social behaviors and language abilities in ASD patients. Intervention cycles are generally set to 15-18 sessions, with stimulation intensity adjusted based on individual motor thresholds (80%-100%) to ensure a balance between efficacy and safety.

Although rTMS interventions have shown some positive results, existing studies still have several limitations. This review highlights the following issues: (1) random double-blind designs are not widely employed, with most studies lacking strict experimental controls, and only a few studies using sham stimulation as a control; (2) target localization is mainly based on standard electrode caps or the “5 cm rule,” which has limited precision, with only a few studies using individualized brain imaging for localization; (3) stimulation parameters (such as frequency, intensity, and duration) are not standardized and require optimization; (4) the critical period effect of rTMS interventions for ASD, particularly during the highly plastic period of early childhood (0-6 years), remains unclear and warrants further investigation.

To enhance the scientific rigor and clinical efficacy of rTMS interventions for ASD, future studies should focus on designing strict random double-blind trials, preferably using sham stimulation as a control to mitigate placebo effects. Additionally, multimodal techniques such as functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), and electroencephalography (EEG) should be employed to elucidate the underlying neural mechanisms. Furthermore, the effects of rTMS at different age stages and its personalized therapeutic outcomes should be systematically evaluated. Lastly, exploring the optimization of stimulation parameters could improve the therapeutic effects and provide a basis for individualized treatments.

Autism spectrum disorder (ASD) is a neurodevelopmental condition. In addition to core symptoms like social impairments and restricted repetitive behaviors, about half of individuals with ASD also experience gastrointestinal symptoms and inflammatory bowel disease (IBD). IBD is a kind of chronic disease associated with immune dysregulation, gut microbiome alterations, micronutrient malabsorption and anaemia, which may be perinatal factors associated with ASD. It's likely that comorbidities like IBD are diagnosed in children with ASD.

This article examines the relationship between IBD and ASD in children by analyzing the relevant domestic and foreign literature. Firstly, the results of the analysis indicate a considerable correlation between childhood IBD and ASD; nevertheless, the effect of ASD on IBD seems to be greater than that of IBD on ASD in children, necessitating more thorough study to substantiate this claim. Secondly, we provide explanations for the potential causes of the lack of a genetic correlation between juvenile ASD and IBD. Furthermore, we analyze the differences in the results between the two phases of the study from the perspective of parental IBD and childhood ASD, and we also suggest possible factors that might have affected this outcome. Finally, in terms of the therapeutic approach, we think that treating IBD in children can have an impact on ASD. To achieve this, we are attempting to implement a strategy that combines multiple therapeutic approaches, such as a combination of nutritional therapy and basic medication, treatment with biologics, and behavioral or psychological interventions.

The following aspects may need to be considered in future research: First, additional empirical evidence is required to support the claim that there is a causal association between ASD and IBD rather than reverse causality. The quantity of samples involved, age, ASD and IBD diagnosis criteria, severity, and analytic models employed will all affect the final experimental conclusions. Therefore, in order to get more persuasive results, it is important to make the elements impacting the confirmation of forward or reverse causality as consistent as feasible. Second, in order to avoid bias in experimental results caused by ignoring the influence of any of these components, the genetic association between IBD and ASD should be established by taking into account the roles of dominant genes, recessive genes, and environmental factors. Third, despite earlier research demonstrating a connection between ASD and IBD, the pathophysiological processes underlying the two conditions remain unclear. Using bioinformatics methods to explore potential regulatory factors, follow-up studies could examine the pathophysiology and common influencing factors of both disorders. Fourth, it's challenging to figure out if childhood ASD and parental IBD are causally related. In order to prove the association, further and more conclusive investigations will be required in the future due to the divergent opinions found in the present studies. Furthermore, while investigating this relationship, we should take into account the roles performed by the mother and father as well as control for or exclude out the influence of additional factors that may result in childhood ASD. Specific requirements for sample selection and classification should also be followed. Fifth, there's a possibility that clinicians' diagnosis of IBD symptoms in children's ASD and IBD research was inadequate. Future research may consequently require to develop a more objective way to diagnose IBD symptoms through clinical diagnosis, the use of Bristol Stool Scale and so on.

In conclusion, this article may offer novel ideas and approaches for investigating the etiology of ASD in children as well as for clinical therapy, since it examines the connection between IBD and ASD and offers some potential strategies for intervening in children's ASD through the treatment of IBD.

The present study explores the innovative application of Temporal Interference Stimulation (TIS) for addressing working memory deficits (WMD) in schizophrenia (SCZ), emphasising its potential efficacy, brain region specificity, and underlying cross-frequency coupling mechanisms. This research represents a pioneering contribution to the field of non-invasive brain stimulation techniques, offering a novel approach to the treatment of schizophrenia by targeting theta oscillations in specific brain regions, namely the inferior parietal lobule (IPL) and the dorsolateral prefrontal cortex (DLPFC).

This study introduces TIs as a novel and promising neuromodulation tool. Compared to traditional transcranial alternating current stimulation (tACS), TIs demonstrate superior focality and the ability to induce specific neural responses. This project is based on preliminary evidence indicating that theta oscillation TIs can markedly improve WMD in SCZ patients and animal models. This evidence justifies further investigation into the clinical effectiveness and neural underpinnings of this technique.

In a randomised controlled trial, 100 patients presenting with a first episode of schizophrenia will be allocated to four groups. The study will employ two forms of non-invasive brain stimulation: TIs and tACS, with both targeting the IPL. The objective is to validate the therapeutic impact of TIs on WMD and to compare its efficacy with tACS by collecting behavioural and electroencephalogram (EEG) data pre- and post-intervention. A comparative analysis is essential for establishing TIs as a potential alternative to tACS in the treatment of SCZ.

In a subsequent cohort, the focus shifts to the DLPFC, allowing us to explore the brain region specificity of TIs. By contrasting the effects of TIs targeting the DLPFC versus the IPL, we aim to determine if TIs exhibit region-specific improvements in WMD, thereby enhancing our understanding of how different brain regions contribute to WMD and respond to TIs intervention.

Moreover, this project aims to elucidate the electroencephalogram (EEG) mechanisms associated with the therapeutic intervention (TI), with a particular focus on cross-frequency coupling. It is hypothesised that TIs may modulate WMD by affecting the energy of theta oscillations, inter-regional phase synchronisation, and theta-gamma cross-frequency coupling. By analysing these EEG parameters before and after the intervention and comparing them across groups, the aim is to identify the neural mechanisms that support or underpin the behavioural improvements observed.

The innovation of this study lies in its multifaceted approach to understanding and treating WMD in SCZ. By integrating behavioural assessments, electroencephalogram (EEG) recordings and clinical interventions, we are not only validating TIs as an efficacious treatment but also elucidating its mechanisms of action. This comprehensive investigation has the potential to pave the way for more targeted and effective treatments for patients with schizophrenia, and it may also facilitate the application of TIs to other neuropsychiatric disorders.

In conclusion, this project represents a substantial advancement in the field of neuromodulation for SCZ, offering a novel therapeutic strategy with potential implications for clinical practice and theoretical understanding of WMD. The findings may not only validate TIs as an effective intervention for SCZ but also provide insights into the brain's functional specificity and cross-frequency coupling, thus contributing to a broader understanding of cognitive deficits and their remediation.

Many researchers have explored the factors influencing creativity through meta-analytic methods. However, most studies have focused on positive or neutral factors, such as self-esteem, organizational trust, and leadership styles, while largely neglecting negative ones. Social exclusion, as a pervasive and threatening stimulus, has been the subject of debate regarding its potential effects on creativity, yet it lacks systematic reviews and analyses. In real-life interpersonal interactions, negative experiences can influence creativity in complex and diverse ways through changes in emotional states, cognitive resource allocation, and motivational systems. Some researchers argue that social exclusion depletes cognitive resources, slows cognitive processing, and reduces cognitive fluency, thereby inhibiting creativity. Others suggest that the negative experience of social exclusion may stimulate individuals' creative motivation, enhancing creativity. This study aims to integrate existing empirical findings on the relationship between social exclusion and creativity using meta-analytic methods, with particular attention to moderating variables such as participant characteristics, cultural contexts, and research methodologies.

Through a comprehensive literature search, 56 studies (70 independent effect sizes, 22,781 participants) meeting the inclusion criteria were identified. A random-effects model was employed to conduct the meta-analysis using Comprehensive Meta-Analysis 3.0 software to test the proposed hypotheses. The heterogeneity test revealed significant variability among the 70 independent effect sizes, confirming the appropriateness of using a random-effects model for the subsequent analysis. Furthermore, assessments using the Funnel Plot, Classical fail-safe indicator, and Egger’s regression test of the intercept indicated no significant publication bias.

The main effect analysis revealed a significant negative correlation between social exclusion and creativity (r = −0.19, 95% CI [−0.25, −0.12]). This result supports the threat-rigidity theory, which posits that social exclusion, as a threatening stimulus, consumes cognitive resources, impairing an individual's ability to suppress irrelevant information and switch flexibly between different pieces of information. This not only hinders the individual's ability to focus attention on useful information during the initial search phase, but also limits their ability to make flexible cognitive shifts during the restructuring phase, leading to a state of cognitive rigidity and difficulty in generating creative ideas.

Subgroup analysis and meta-regression further revealed that this relationship was moderated by participants' age and the measurement tools used for assessing social exclusion and creativity, but not by the type of social exclusion (social rejection vs. social ignorance), participants' gender, or cultural background. Specifically, the findings indicated that the negative correlation between social exclusion and creativity weakens as participants’ age increases. This result may be related to individuals' decreasing sensitivity to negative social situations and their growing coping experience. The frequency of reported experiences of exclusion is negatively correlated with age, reflecting a decreased sensitivity to negative stimuli in older individuals. Furthermore, with increased life experience, older individuals tend to be more adept at using effective emotional regulation strategies and social support systems to mitigate the negative effects of social exclusion. When measured using quantitative questionnaires, social exclusion significantly inhibits individual creativity, whereas social exclusion induced through experimental paradigms is associated with enhanced creativity. Experimental priming paradigms typically provoke temporary experiences of exclusion, and this short-term stressful environment may help stimulate an individual's creative motivation, enhancing creative performance. In contrast, social exclusion measured through questionnaires is often associated with chronic stress and negative emotions, which can lead to the depletion of cognitive resources, thereby suppressing creative motivation and behavior. Regarding creativity measurement, social exclusion was found to significantly diminish individuals’ latent creative potential and observable creative behaviors, while leaving their cognitive abilities related to creative thinking unaffected. This may be related to the counteracting effects of social exclusion on different types of creative thinking. Divergent thinking and convergent thinking are two of the most typical forms of creativity. Social exclusion tends to trigger negative emotional reactions and thinking patterns, thereby impairing performance on divergent thinking tasks. However, the relationship between social exclusion and convergent thinking may be positively correlated. When individuals experience social exclusion, they may actively search for and integrate information related to their connection with the group to assess whether there is a chance for re-acceptance, a thought process similar to convergent thinking. Therefore, the negative impact of social exclusion on divergent thinking and its potential positive effect on convergent thinking may counterbalance each other, resulting in an overall insignificant effect.

This meta-analysis holds both theoretical and practical significance. Theoretically, it addresses the ongoing debate regarding the relationship between social exclusion and creativity. The findings support the Threat-Rigidity Theory, which suggests that social exclusion, as a threatening stimulus, depletes cognitive resources. This depletion diminishes individuals' ability to suppress irrelevant information and flexibly shift between concepts, leading to a rigid cognitive state that hinders creative idea generation. Practically, the study highlights the importance of providing positive feedback and encouragement, particularly to minors and individuals experiencing prolonged social exclusion, to mitigate its negative effects and foster a more supportive environment for creativity.

The rapid advancement of digital technology has heightened the complexity and competitiveness of the social environment, profoundly affecting employees' work patterns. While effectively helping enterprise employees improve work efficiency, it also brings more psychological anxiety, lack of concentration and other problems to employees, further reducing work performance. In this context, employee mindfulness—characterized by attention, awareness, and non-judgmental acceptance —has garnered significant attention. Numerous studies have examined the relationship between employee mindfulness and job performance. However, there remain notable inconsistencies and divergences in the research findings that merit further exploration. This meta-analysis examined the relationship between employee mindfulness and job performance based on self-regulation theory, and investigated the mediating mechanisms and moderating factors that influence this relationship.

This paper meta-analyzed both Chinese and English primary studies on the topic of employee mindfulness and job performance. Based on the reference to existing research to clarify the search keywords for job performance, work engagement, and job burnout, mindfulness was combined with the keywords for job performance, work engagement, and job burnout respectively, and searches were conducted in domestic and international databases to search and screen relevant studies. Finally, 220 independent samples from 188 empirical studies (with a total sample N = 73899) were included. Based on these studies, we conducted heterogeneity and publication bias analysis, main effects analysis, meta-analytic structural equation modeling (MASEM) and moderating effect analysis. Specifically, this study first employed a comprehensive approach by using funnel plots, fail-safe numbers, Egger's regression coefficients, and Begg's intercept to assess publication bias. Secondly, Hunter and Schmidt’s method were used to analyze the main effect between employee mindfulness and job performance. Thirdly, the meta-analytic structural equation modeling was used to test the mediating effect of work engagement and job burnout. Finally, the moderating effect of cultural differences (individualism-collectivism) on the relationship between employee mindfulness, work engagement, and job burnout were tested through Hunter and Schmidt’s subgroup analysis method.

The results of heterogeneity analysis show a high level of heterogeneity among the variables and the publication bias test revealed that there was no substantial publication bias in the studies. The main effects analysis revealed that employee mindfulness is positively associated with job performance, task performance, contextual performance, and work engagement, while it is negatively associated with job burnout. The result of MASEM showed that: Work engagement and job burnout play a significant mediating role in the relationship between employee mindfulness and both task performance and contextual performance. Furthermore, the mediating effect of work engagement is more pronounced between mindfulness and task performance compared to its role in the relationship between mindfulness and contextual performance. The results of subgroup analysis show that: The relationship between employee mindfulness and work engagement is moderated by individualism-collectivism culture. In the context of collectivism, the relationship between employee mindfulness and work engagement is stronger.

The research findings based on meta-analysis offer comprehensive and reliable conclusions that clarify the relationship between employee mindfulness and job performance, addressing the discrepancies in existing research regarding the outcomes of employee mindfulness and work performance. These insights not only have certain reference significance for future theoretical explorations in the field of employee mindfulness and job performance, but also provide valuable references for cultivating a mindful workforce in the process of organizational practice, thereby improving employees' work status and performance levels. This ensures that enterprises can maintain long-term, stable development in the current increasingly complex and rapidly changing environment. Moreover, this study also explored whether individualism-collectivism plays a moderating role in the relationship between employee mindfulness and work engagement, as well as between employee mindfulness and job burnout. It not only provides a reference for a deeper understanding of the boundary conditions under which employee mindfulness affects individual work engagement and job burnout, but also offers a reasonable explanation for the differences in the effects of mindfulness across various cultural contexts.

Spatial navigation, as a complex cognitive behavior involving human-environment interaction, depends on the support of cognitive maps. Current research on spatial navigation primarily focuses on vision-dominated terrestrial environments. However, with the growing exploration of aerial, space, and maritime domains, the demands on individuals’ spatial cognitive abilities and navigation strategies are evolving. Compared to conventional ground-based navigation environments, these domains typically offer fewer effective visual cues. This study defines such conditions as visually cue-restricted environments, encompassing aerial and space environments, as well as dark settings, low-light conditions, and visually obstructed environments (e.g., dense fog).

In visually cue-restricted environments, individuals face significant navigation challenges due to blurred visual attributes and a limited field of view. These difficulties are particularly pronounced in professional contexts, such as those encountered by pilots and astronauts. For example, during high-altitude flights, the scarcity of effective visual cues, the effects of inertia, the limitations of bodily movement information, and the low visibility of nighttime conditions collectively reduce navigation efficiency. These factors place higher demands on pilots' spatial cognitive abilities, particularly in constructing cognitive maps. This study aims to investigate how the dynamic processing mechanisms of cognitive maps support spatial navigation, ultimately seeking to enhance individuals' adaptability to visually cue-restricted environments.

This study summarizes the environmental and cognitive elements included in cognitive maps from the perspective of human-environment interaction. Based on existing research categorizing spatial environment knowledge, key elements from the perspective of route knowledge include visual elements of landmarks, semantic features, and effectiveness. From orientation knowledge perspective, the critical components involve landmark visibility, spatial axes, spatial boundaries, and turns or intersections. Additionally, from the cognitive processing perspective of individuals, cognitive maps may also incorporate event elements, representing the states of individuals at different times and places, which are closely tied to episodic memory. These elements in cognitive maps do not exist independently but are interrelated, collectively influencing the spatial navigation process. Elements in cognitive maps are organized in forms such as Euclidean space, cognitive graphs, and schemas.

Building on this foundation, this study proposes a two-stage dynamic processing mechanism for cognitive maps in navigation within visually cue-restricted environments: the construction stage and the updating-correction stage. During the construction stage, individuals gather environmental information through multiple sensory channels and integrate these elements into a cognitive map. In the updating-correction stage, individuals abstract spatial mental models from the cognitive map based on environmental features and navigation goals. They perform reasoning and decision-making to plan routes, achieving spatial orientation through multisensory integration. As navigation progresses, individuals dynamically update and correct the cognitive map with environmental information to support navigation in visually cue-restricted environments, a process that is further regulated by metacognitive monitoring. This dynamic processing mechanism plays a unique role in navigation under visually restricted conditions.

By elucidating the dynamic processing mechanisms of cognitive maps under visually cue-restricted conditions, this study provides theoretical insights into changes in individuals’ spatial navigation behaviors across complex environments. It also broadens future research directions in applications such as human-computer collaborative navigation systems and spatial navigation training.

With the growing popularity of online video learning, the question of how to improve the effectiveness of online learning has received increasing attention from researchers. Teachers play important roles in instructional videos, and their social cues, such as facial expressions, eye gaze, and gestures, can affect video learning performance. Among them, facial expression (i.e., positive, negative, neutral) is an important nonverbal cue for teachers. It is one of the boundary conditions in video learning where the teachers' presence affects the learning effect, which can affect the learning performance and the students' subjective experience by influencing the students' emotions compared to cues such as eye gaze and gesture. However, there are few studies on the effects of real teachers' facial expressions on video learning, and there are inconsistencies among the findings, so there is an urgent need to review and summarize the mechanisms by which teachers' facial expressions affect video learning and to provide new directions for future research. This paper focuses on the research issue of the effect of real teachers' facial expressions on learning in video learning, summarizes 17 previous empirical studies, and attempts to analyze the results of these studies.

Based on the median effect size, this review indicates that, for the learning performance, teachers' positive facial expressions can promote the retention (d _retention = 0.53) and transfer (d _transfer = 0.41) test. Moreover, positive facial expressions can improve performance on the delayed joint test (retention test and transfer test) more than on the immediate joint test. For the students' attention, positive teacher facial expressions have a small effect on the fixation time on learning content (d_{gaze time on slide} = 0.26) and the teachers' face (d _{gaze time on teachers' face} = 0.30). For the subjective experience, teachers' positive facial expressions can evoke students' positive emotions (d _{positive emotions} = 0.55), arousal (d _arousal = 0.84), motivation (d _motivation = 0.90), and decrease negative emotions (d _{negative emotions} = -0.52). However, teachers' positive facial expressions have a weaker effect on reducing the cognitive load (d _{cognitive load} = -0.17) and improving parasocial interaction (d _{parasocial interaction} = 0.23).

Based on the existing theories and the results of empirical studies, this review proposes a mechanism by which the facial expressions of real teachers affect video learning. Teachers' facial expressions can influence learning performance through three paths: cognitive, emotional, and social perception. Specifically, in the cognitive path, cognitive load theory (CLT) suggests that teachers' facial expressions distract students' attention from the learning content and increase students' cognitive load. However, according to the results of this review, although teachers' facial expressions increase attention to the teachers' faces, they do not increase cognitive load but rather encourage students to have the same emotions as the teacher, which in turn positively affects learning performance by increasing arousal and motivation. At the same time, facial expressions can also increase attention to the learning content, which in turn encourages students to increase their mental effort and have a positive learning performance. In the emotional path, in line with the cognitive-affective-social theory of learning in digital environments (CASTLE) and emotional contagion theory (ECT), the teacher's facial expressions, when recognized by students, produce the same emotions as those of the teacher, increasing arousal and motivation, and ultimately improving learning performance. In the social perception path, facial expression as a social cue for the teacher can facilitate the establishment of the student's cognitive schema, which produces a higher level of parasocial interaction, which in turn has an impact on learning performance, consistent with the para-social interaction theory (PSIT).

In conclusion, we suggest that teachers in instructional videos use positive facial expressions to help students better understand the learning content, focus their attention, evoke positive emotions and motivation, and enhance the interaction between teachers and students. Further research is required to elucidate the role of teachers' facial expressions in video learning, particularly with regard to experimental design, moderating variables, and underlying mechanisms.

Compared with other rehabilitation methods, vocational rehabilitation is considered an “advanced form” of social reintegration for patients with severe mental disorders. Currently, few studies have explored the mechanism of vocational rehabilitation, and the theoretical explanation of its effectiveness is inconsistent. Based on this, combining the recovery concept and the characteristics of patients with mental disorders, this paper proposes an integrated model of vocational rehabilitation in terms of cognitive recovery, motivational recovery and behavioral recovery. The model aims to explore the practical mechanisms that promote the re-socialization of patients with mental disorders, specifically in the following areas:

First, the cognitive recovery mechanism involves strengthening working memory and cognitive intervention training. On the one hand, vocational rehabilitation expands the memory capacity of patients with mental disorders through working memory training, improves their impaired cognitive functions, and ultimately produces good work performance. On the other hand, through cognitive intervention training, vocational rehabilitation helps patients with mental disorders to increase cognitive flexibility, establish positive work beliefs, and ultimately promote re-socialization.

Secondly, the mechanism of motivational recovery consists of two paths: satisfying the needs of competence and enhancing self-efficacy. For example, vocational rehabilitation fulfills the competency and relational needs of patients with mental disorders through various skills training(e.g., work skills, social skills), fostering strong self-efficacy and increasing the likelihood of employment and social reintegration; Likewise, vocational rehabilitation utilizes problem-solving training to empower psychologically for patients with mental disorders, enhancing their sense of self-efficacy and building psychological resilience, which helps them achieve social integration.

Third, the behavioral recovery mechanism consists of two elements: diversified vocational behavioral goals and employment support services. Cognitively, vocational rehabilitation helps patients with mental disorders establish vocational behavioral goals through diversified vocational training, thus producing good social integration effects. From an operational point of view, the employment support services of vocational rehabilitation enhance the continuity of employment behaviors, so that patients with mental disorders can obtain stable job opportunities, thus highlighting the effectiveness of the practice in terms of social reintegration.

In summary, compared with other models, the strengths of this model are reflected in the following four aspects: reflecting the functional position of vocational rehabilitation “re-socialization”, clarifying the systematic mechanism of action, incorporating psychological outcomes, and the relevance to other groups of mental disorders.

With the widespread use of intensive longitudinal data in the social sciences, how to analysis intensive longitudinal mediation (ILM) effect has attracted the attention of many researchers. A conventional approach is using multilevel models or multilevel structural equation models. In that case, the temporal sequence of variables is ignored, with the dynamic relationship between variables remaining unexplored.

In this paper, taking 1-1-1 [i.e., 1 (independent at Level 1) - 1 (mediator at Level 1) - 1 (dependent at Level 1)] ILM model as an example, we summarize five types of ILM analysis approaches: 1) multilevel autoregressive model (MAM); 2) residual multilevel autoregressive model (RMAM); 3) dynamic structural equation model (DSEM); 4) residual dynamic structural equation model (RDSEM). 5) cross-classified dynamic structural equation model (cross-classified DSEM). In both RMAM and RDSEM, we first statistically remove the trend from the variables with a regression of each variable on time, and then construct the ILM model using the residuals from the previous step.

There are two methods developed for analyzing the ILM effect. One of them is based on manifest variables. The multilevel model and the temporal sequence of variables are combined to develop MAM, which is extended to RMAM when detrending is required. The other method is based on latent variables. The multilevel structural equation model and the temporal sequence of variables are combined to develop DSEM, which is extended to RDSEM when detrending is required.

In the present study, we propose a procedure to conduct ILM analysis. The first step is to decide whether one would like to get ILM effects with time changes. If yes, cross-classified DSEM should be adopted to analyze ILM effects with time changes. It is a suitable detrending approach to include time as a time-varying covariate in the cross-classified DSEM. Otherwise, one will proceed with the second step, which is to decide whether it is necessary to detrend. If detrending is necessary, RDSEM should be adopted to analyze ILM effects with individual changes. Otherwise, DSEM should be adopted to analyze ILM effects with individual changes. The third step is to check whether RDSEM or DSEM converges. If either of them converges, their result should be reported. Otherwise, MAM or RMAM should be adopted to analyze ILM effects with individual changes. This paper exemplifies how to conduct the proposed procedure and provides corresponding Mplus and R codes.

Directions for future research on mediation analysis of intensive longitudinal data are discussed at the end of the paper. According to the level of the variable, there are seven ILM models: 1-1-1, 2-1-1, 2-2-1, 2-1-2, 1-2-2, 1-2-1 and 1-1-2. However, only the first four mediation models are discussed in the existing literature, and it is found that as long as there is a level-2 variable in the ILM model, the mediation effect can only occur at level-2. The analytical methods of the last three models, 1-2-2, 1-2-1 and 1-1-2 ILM, can be inferred from those of the first four.