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Conceptual Framework

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Investigating social cognitive characteristics of social anxiety within the Bayesian framework PENG Yujia, WANG Yuxi, JU Qianqian, LIU Feng, XU Jia 2025, 33 (8):  1267-1274.  doi: 10.3724/SP.J.1042.2025.1267 Abstract ( 520 )   PDF (1160KB) ( 809 )   Peer Review Comments Social anxiety disorder (SAD) is among the most common anxiety disorders, marked by overwhelming fear and avoidance of social behaviors and social scenarios, and debilitates patients' lives and work. Previous studies have provided ample evidence of dysregulated social cognition in social anxiety, such as negative cognitive biases, demonstrating a negative processing of social information. However, the factors driving the dysregulated social cognition remain unclear, impeding the elucidation of the underlying computational neural mechanisms of social anxiety symptoms and guiding personalized interventions. Within the Bayesian framework, the current project proposed that the negative cognitive biases phenomenon may stem from negative prior expectations. By integrating psychophysics experiments, electroencephalography (EEG), functional magnetic resonance imaging (fMRI), computational modeling, and machine learning, we will systematically investigate prior expectations' characteristics, formation, and dynamic modulation. The key innovation of this project lies in three major contributions. First, this study will be the first to propose and quantitatively examine the impact of prior expectations on dysregulated social cognition. Previous socially anxious research has primarily focused on behavioral manifestations and their associations with social information processing, yet largely overlooked the role of prior expectations in shaping social cognitive distortions. By identifying the static features and dynamic formation process of prior expectations in dysregulated social cognition, our study expands existing cognitive-behavioral models of social anxiety, providing a more comprehensive framework for understanding its underlying mechanisms. Second, our project aims to construct a mechanistic framework of social anxiety that systematically links behavioral manifestations to cognitive mechanisms, and further to neural mechanisms. By integrating behavioral experiments, neuroimaging, and computational modeling as methodological tools, we are able to map distorted cognitive components onto their specific neural underpinnings. This integrative approach provides robust empirical evidence, thereby advancing the theoretical understanding of social anxiety and offering a foundation for future research and intervention development. Third, this project extends the investigation to the translational level by evaluating the potential of neural decoding feedback as an intervention for social anxiety. By leveraging real-time neural data to modulate maladaptive social-cognitive expectations, we aim to assess the feasibility of neurofeedback-based treatments in social anxiety, providing a potential pathway for developing novel, data-driven therapeutic strategies. In summary, this project not only advances the theoretical understanding of social anxiety but also explores its translational potential. By extending the traditional cognitive-behavioral model to incorporate prior expectations and constructing a comprehensive behavioral-cognitive-neural framework, it systematically maps the progressive linkage from behavioral manifestations to cognitive processes and neural underpinnings—offering a novel perspective for studying anxiety-related disorders. Importantly, this project goes beyond theoretical contributions by identifying specific intervention targets derived from our computational framework and assessing their clinical applicability through neurofeedback. By leveraging real-time neural decoding to modulate maladaptive prior expectations, we aim to evaluate the efficacy of a novel, data-driven intervention approach. This translational effort holds promise for the development of precision-targeted treatments that can significantly enhance therapeutic outcomes for individuals with SAD. By elucidating the mechanisms underlying dysregulated social cognition through an integrative, multi-level approach, this project lays the foundation for a paradigm shift in both research and clinical practice. We encourage the broader adoption of computational psychiatry methods, redefine the understanding of dysregulated social cognition in social anxiety, and bridge the gap between mechanistic theory and personalized intervention. Ultimately, this work paves the way toward a new era of individualized, mechanism-informed mental health care empowered by technological innovation and theoretical precision. References | Related Articles | Metrics

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Associations and their potential mechanisms between intergenerational caregiving and health outcomes among sandwich generation within four-generation families SHI Jiaming 2025, 33 (8):  1275-1291.  doi: 10.3724/SP.J.1042.2025.1275 Abstract ( 284 )   PDF (831KB) ( 679 )   Peer Review Comments The increasing life expectancy of older adults and the postponement of female reproductive age have made the coexistence of older adults and young children within families increasingly common. The multigenerational family structure, represented by “G1 → G2 → G3 → G4,” has become more prevalent, accounting for approximately 25% of households in China. Within these families, G2 constitutes the typical sandwich generation, simultaneously addressing the caregiving needs of both G1 (old adults) and G4 (young children). This caregiving dynamic can be categorized into four patterns: caregiving for parents, caregiving for grandchildren, dual caregiving for both parents and grandchildren, and providing no care. This study examines the influences of intergenerational caregiving patterns on health outcomes among the sandwich generation through a mixed-methods research design that integrates both quantitative and qualitative approaches. Specifically, it explores three key questions: (1) How are caregiving resources allocated within the sandwich generation? (2) What are the effects of different intergenerational caregiving patterns on the health outcomes of the sandwich generation? (3) What are the underlying mechanisms through which intergenerational caregiving patterns influence caregivers' health outcomes? Study 1 investigates two key sub-questions. First, it examines the overall intergenerational caregiving patterns and the underlying factors influencing resource allocation. This section serves as a preliminary analysis, leveraging multi-wave data from the China Health and Retirement Longitudinal Study (CHARLS) to assess the current state of the sandwich generation and their intergenerational caregiving responsibilities. The analysis focuses on empirically identifying caregiving patterns across various demographic groups, including gender, urban-rural residence, age, and employment status. Second, the study explores the competitive dynamics of resource allocation, specifically examining whether providing care for either parents or grandchildren diminishes the likelihood of simultaneously caring for the other. Study 2 investigates the impacts of intergenerational caregiving patterns on the health outcomes of the sandwich generation, drawing on three interrelated theoretical frameworks: intergenerational solidarity theory, the intergenerational stake hypothesis, and the intergenerational conflict perspective. These frameworks provide a conceptual foundation for analyzing how intergenerational caregiving patterns influence the health outcomes of the sandwich generation and guide the formulation of hypotheses for empirical analysis. Furthermore, Study 2 examines the role of care providers' cultural orientation (individualism vs. collectivism) in shaping caregiving experiences. Specifically, it explores the moderating effect of cultural orientation on the relationships between intergenerational caregiving patterns and the health of the sandwich generation. Study 3 develops a multiple mediation model grounded in role enhancement and role strain theories, incorporating both role enhancement (reciprocity and psychological mechanisms) and role strain (stress mechanisms) to examine the pathways through which intergenerational caregiving patterns influence the health of the sandwich generation. This analysis is conducted using quantitative research methods. Furthermore, Study 3 constructs an integrated analytical framework that encompasses the coercive effects of policy systems, the soft constraints of community sentiment, and the cultural identity of family responsibilities. Through qualitative research, this framework seeks to provide a deeper understanding of the mechanisms linking intergenerational caregiving patterns to the health outcomes of the sandwich generation. Finally, by integrating quantitative and qualitative approaches, the study offers a more comprehensive and nuanced explanation of these mechanisms, ensuring methodological complementarity and mutual validation. This study makes several key contributions. First, by examining the intergenerational caregiving responsibilities of the sandwich generation within multigenerational families, this research provides empirical evidence for understanding the simultaneous demands of elderly care and child-rearing in Chinese families amid modernization. Second, by investigating the effects and underlying mechanisms of intergenerational caregiving patterns on the health of the sandwich generation, this study establishes a comparative framework for evaluating how different caregiving patterns influence caregivers' health outcomes. Finally, addressing the influences of intergenerational caregiving patterns on health not only advances the goal of healthy aging but also informs the development of population service strategies that support both elderly care and child-rearing, with an emphasis on safeguarding caregiver health. References | Related Articles | Metrics

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How and when leader humility promotes career sustainability under digital and AI Contexts ZHONG Jie, ZHENG Xiaoming 2025, 33 (8):  1292-1305.  doi: 10.3724/SP.J.1042.2025.1292 Abstract ( 210 )   PDF (1162KB) ( 291 )   Peer Review Comments In the context of digital and AI-driven environments, enhancing leader humility and leveraging its benefits to foster career sustainability have become critical challenges in organizational management. Existing research has primarily examined leader humility in traditional work settings, offering limited practical guidance for cultivating humility in AI-integrated organizations. Recent studies suggest that conventional leadership approaches are no longer sufficient for managing AI-driven workplaces and call for an expanded conceptualization of leadership (AlNuaimi et al., 2022). Responding to this call, this study redefines leader humility in the digital era by incorporating humility toward both employees and AI. In navigating human-AI collaboration, humble leaders should remain people-centered while strategically leveraging digital intelligence to support employee growth rather than replacing human contributions. Furthermore, prior research on the antecedents of leader humility has predominantly focused on individual leader traits, such as a growth mindset, while overlooking bottom-up influences from employees and the role of AI-generated feedback. This gap limits the practical recommendations for fostering leader humility in AI-driven organizations. In the digital workplace, leader humility extends beyond interpersonal interactions to include humility toward AI, emphasizing the importance of balancing human-machine interactions. Grounded in feedback intervention theory, this study examines how different sources of negative feedback—employee-driven versus AI-generated—affect leader humility. It further uncovers two key mechanisms: perceived face loss and self-reflection, which mediate this relationship. Additionally, it investigates the moderating effect of leader self-attribution, shedding light on its role in shaping how leaders interpret and respond to negative feedback. These findings address the previously overlooked influence of AI in shaping leader humility, offering both theoretical and practical insights into fostering leader humility in AI-integrated workplaces to support career sustainability. Moreover, existing studies have primarily defined career sustainability in general work environments, adopting a happiness-productivity framework that considers employee well-being and productivity as key indicators (De, Van der Heijden & Akkermans, 2020). Productivity encompasses both job performance and employability, assessing an individual's current work effectiveness and future career potential. Building on this foundation, this study refines the concept of career sustainability in AI-driven workplaces by identifying three key dimensions: employee well-being, job performance, and future career expansion behaviors. The latter is a novel construct introduced in this study, referring to employees' proactive efforts to maintain a competitive edge in AI-integrated workplaces. Employees can achieve this by strengthening interpersonal, teamwork, and communication skills to maximize human advantages while simultaneously improving AI literacy to optimize digital tools for workplace efficiency. Additionally, existing studies have overlooked how leader humility contributes to career sustainability. Drawing on conservation of resources (COR) theory, this study examines two pathways through which leader humility fosters career sustainability: reducing career replacement anxiety and enhancing relative advantage utilization. Furthermore, this study identifies the moderating role of leaders' digital literacy in shaping these effects. These findings offer new insights into how leader humility influences career sustainability in AI-driven environments, providing a foundation for further research into leadership-driven career development strategies in digital workplaces. In the era of digital intelligence, career development requires not only external incentives but also internal motivation (Zhang et al., 2021; Chen et al., 2022). Zhang et al. (2021) advocate for integrating self-leadership perspectives into organizational management research to address practical challenges in AI-driven workplaces. However, prior studies have not applied the proactive motivation model to examine how external leadership influences are internalized to drive employees toward career sustainability. This study proposes that leader humility fosters self-leadership by recognizing employees' strengths, learning from employees, and leveraging digital intelligence to identify and support career growth. These mechanisms transform external leadership influence into intrinsic motivation, empowering employees to engage in career expansion behaviors and achieve career sustainability. These findings enrich the understanding of how leader humility promotes self-leadership and provide theoretical insights into career development in AI-integrated workplaces. References | Related Articles | Metrics

Meta-Analysis

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The functional brain networks of intergroup empathy bias: A meta-analysis based on fMRI studies SUN Luwen, ZHOU Yue, JIANG Zhongqing 2025, 33 (8):  1306-1320.  doi: 10.3724/SP.J.1042.2025.1306 Abstract ( 181 )   PDF (1322KB) ( 419 )   Peer Review Comments Intergroup Empathy Bias refers to the phenomenon characterized by differential empathic responsiveness toward in-group versus out-group members. The neurobiological substrates of this bias - particularly its associated functional neurocircuitry and neuroregulatory processes - remain incompletely characterized. To systematically identify consistent neuroanatomical regions implicated in intergroup empathy bias and elucidate their neurofunctional correlates, this investigation implements a tripartite methodological framework: Phase I utilizes Activation Likelihood Estimation (ALE) to systematically map convergent neuroanatomical patterns associated with intergroup empathy bias. Stratified subgroup analyses are implemented to investigate moderating variables: affective dimensions (nociceptive vs. emotional processing), social categorization paradigm (racial vs. non-racial grouping), and task design characteristics (implicit vs. explicit empathy paradigms). Phase II applies Meta-Analytic Connectivity Modeling (MACM) to delineate functional connectivity between identified neural hubs and distributed cortical networks. The final phase leverages Neurosynth - a comprehensive neuroimaging meta-analysis platform integrating data from over 14,000 task-based fMRI studies - to characterize functional profiles of the identified network during intergroup empathy processing. This study employs ALE meta-analysis to analyze neuroimaging coordinates from 19 independent experiments on intergroup empathy bias. Two suprathreshold activation clusters exhibit robust convergence: the left anterior insula (lAI) and medial prefrontal cortex (mPFC). This lateralization reflects differential functional specialization: left insular activity is modulated by social group categorization during affective processing, whereas right insular functions (e.g., attentional modulation, network reconfiguration) are categorization-insensitive. Critically, in-group conditions demonstrating mPFC activation magnitude proportional to negative affect intensity highlight this region's regulatory dominance. Post hoc subgroup analyses reveal task-dependent neural signatures: affective rating paradigms predominantly recruit the lAI through heightened subjective emotional resonance mechanisms, whereas emotion categorization tasks engage mPFC-mediated executive control circuitry via deliberate cognitive appraisal processes. Through MACM and Neurosynth functional decoding, this study reveals robust functional interconnectivity between the two neural clusters and distributed cortical/subcortical regions, indicating an evolutionarily optimized network architecture for intergroup empathy modulation. The network's operational mechanisms are conceptualized through three neurocognitive dimensions: (1) Executive regulation - mirroring Central Executive Network (CEN) dynamics via prefrontally mediated cognitive control; (2) Affective modulation - suppressing out-group empathy through dual pathways: impaired emotion recognition (ventral anterior cingulate cortex [vACC] hypoactivation) and diminished emotional resonance (reduced mirror neuron system efficacy); (3) Motivational valuation - striatal-orbitofrontal circuits perform neuroeconomic cost-benefit analyses, wherein in-group empathy demonstrates heightened utility in social exchange frameworks. By synthesizing neuroimaging meta-analytic evidence, this study delineates consistent neural substrates underlying intergroup empathy bias, thereby proposing a theoretical framework to guide subsequent research. Furthermore, these empirical insights provide a neural foundation for precision-targeted neuromodulatory interventions. Systematic identification of critical neuroanatomical regions and their networks enables the development of optimized neuroregulatory strategies. These strategies aim to ameliorate intergroup empathy bias, ultimately fostering societal cohesion and enhancing cooperative dynamics. References | Related Articles | Metrics

Research Method

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Aperiodic components of resting-state EEG/MEG: Analysis procedures, application advances and future prospects HU Jingyi, BAI Duo, LEI Xu 2025, 33 (8):  1321-1339.  doi: 10.3724/SP.J.1042.2025.1321 Abstract ( 215 )   PDF (1739KB) ( 664 )   Peer Review Comments Power spectral analysis is a common method in EEG/MEG data processing. In recent years, growing numbers of researchers have recognized that the aperiodic components of power spectra hold unique physiological significance and practical value. With the global adoption of toolkits such as SpecParam, the aperiodic analysis of resting-state EEG/MEG has garnered substantial attention. Here we provide a rapid-start guide for beginners in aperiodic analysis, offering tool comparisons and standardized workflows while synthesizing current research on the aperiodic activity of high-density resting-state EEG/MEG. Building on key findings from developmental neuroscience and neuropsychiatric disorders, we propose critical directions for advancing this field. First, we systematically compare widely-used aperiodic analysis tools (e.g., SpecParam, IRASA) across some dimensions like spectral parameterization approaches, algorithmic foundations, and fitting parameter spaces. Using the representative SpecParam and sleep deprivation dataset, we then demonstrate a whole-brain standardized analysis protocol for high-density EEG/MEG studies. This framework addresses some current limitations in official tool tutorials that predominantly employ single-electrode examples, while highlighting the necessity for future multi-electrode spatial analyses and group comparison. Accompanying analysis code is provided in supplementary materials for replication. Second, we consolidate major advancements of aperiodic analysis across neuroscience, psychology, and psychiatry. In developmental neuroscience, age-related aperiodic parameter flattening shows robust associations with cognitive decline and sleep deterioration. The aperiodic exponent emerges as a critical biomarker linking advanced cognitive functions, arousal states, and neurodevelopmental trajectories, offering electrophysiological insights into the behavioral mechanisms. In clinical psychiatry, significant aperiodic parameter alterations demonstrate diagnostic potential as the electrophysiological biomarkers for neuropsychiatric disorders. By disentangling periodic and aperiodic components through parameterization, this approach resolves previous contradictory findings while providing novel perspectives for assessing brain dysfunction. These applications underscore aperiodic analysis' cross-population validity and translational promise. Finally, we identify three critical research frontiers: 1) Current methodologies insufficiently address whole-brain spatial distributions of aperiodic activity, necessitating spatial feature characterization to elucidate neurophysiological generation mechanisms; 2) Standardized analytical pipelines must be established across tools to enhance reproducibility; 3) The physiological interpretation of aperiodic parameters requires expansion through excitation-inhibition (E:I) balance theory, particularly via direct neurotransmitter association studies. These proposed directions aim to bridge existing gaps and propel systematic development of aperiodic analysis methodologies. Future research should integrate multimodal neuroimaging techniques, innovative experimental paradigms, and mechanistic modeling to strengthen the theoretical foundations and clinical applications of EEG/MEG aperiodic analysis. References | Related Articles | Metrics

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Classification consistency for measuring classification reliability of psychological and educational tests CHEN Jingyi, SONG Lihong, WANG Wenyi 2025, 33 (8):  1340-1357.  doi: 10.3724/SP.J.1042.2025.1340 Abstract ( 165 )   PDF (863KB) ( 231 )   Peer Review Comments The reliability of norm-referenced tests is not appropriate for classification tests or criterion-referenced tests. Classification consistency is a crucial metric in psychological and educational measurement, reflecting the probability that examinees will receive the same classification categories on two independent administrations of a test or two parallel tests. It is widely utilized in evaluating the classification reliability of psychological assessments, educational tests, and medical diagnostic tests. Since administering a test twice or parallel tests is often challenging in practice due to increased testing time and test construction expense, many methods are focused on estimating classification consistency based on results from a single test administration in psychological and educational measurement. These methods are designed to provide important psychometric properties for assessing and improving the reliability and fairness of tests. The purpose of the study firstly focused on the investigation of the general framework for estimating classification consistency based on criterion-referenced tests. The general procedures for estimating classification consistency based on a single test administration can be briefly summed up as follows: (a) determining the probabilities of examinees being classified into each category according to classification criteria, (b) following an independent and identically distributed based on the assumption that two administrations of a test or two parallel forms are independent, (c) computing the sum of the squared probabilities of their classification across all categories, which refers to the conditional classification consistency for an examinee, and (d) obtaining marginal classification consistency based on a person or distribution method. Following the general framework for estimating classification consistency, the methods have been developed for the estimation of single-administration classification consistency by the consideration of measurement error, conditional standard error of measurement, classification probabilities, and simulated retest classification errors under different psychometric models. This article describes the ideal and procedures of the representative methods in details under classical measurement theory (CTT), item response theory (IRT), cognitive diagnostic models (CDM), and machine learning models (MLM). The theoretical foundations, computational steps, and applications of representative methods were systematically introduced under each model. CTT-based methods provide classification consistency of observed test scores. For example, the Livingston and Lewis approach utilizes test score distributions and test reliability to estimate classification consistency. The Lee method employs a compound multinomial distribution for establishing the conditional distribution of total summed scores and applies it to compute the expected probabilities of each examinee falling into each category of performance levels. However, the limitation of CTT is that parameters are sample and test dependent. IRT-based methods estimate classification consistency of observed test scores or latent ability through modeling the probability of item response based on latent ability and item parameters. The Rudner's approach estimates conditional classification consistency by incorporating conditional standard error of measurement, which can be computed from an individual's test information function. The Lee's and Guo's methods employ the conditional distribution of total summed scores or likelihood functions to compute the expected classification probabilities of each examinee, respectively. These methods require relatively large sample sizes to calibrate item parameters. CDM-based methods are designed to evaluate classification consistency of attribute pattern, attribute status, and the number of skills mastered. These methods provide a finer-grained approach to report reliability of cognitive diagnostic assessments. For example, attribute-level consistency indices and pattern-level consistency indices quantify classification reliability at a more fine-grained level and holistic levels, respectively. MLM-based methods provide data-driven insights into classification reliability. These methods can learn complex relationships between test items from test data, offering dynamic and potentially more accurate estimations of classification consistency, compared to traditional psychometric approaches. Beyond the introduction to the method of classification consistency, this study provides applications of classification consistency indices, illustrating their use in educational, psychological, and diagnostic assessments. Four examples were illustrated about how to apply classification consistency indices for evaluating test reliability. A comparative analysis of these methods reveals that CTT-based methods offer simplicity and ease of computation, while they may lack precision for CRT. IRT-based methods enhance estimation precision but require more complex assumptions. CDM-based methods are suitable for formative assessment. Machine learning methods, though promising, are still in the early stages of integration within psychometrics and require further validation for practical implementation. Future research should investigate the approach of estimating confidence intervals for classification consistency, as current methods primarily provide point estimates. Additionally, more extensive empirical studies of MLM-based classification consistency estimations are necessary. Researchers and practitioners are encouraged to incorporate and report classification consistency more frequently to enhance the overall quality and fairness of CRT. By systematically reviewing existing methodologies and their applications, this study highlights the significance of reporting classification consistency for CRT. References | Related Articles | Metrics

Regular Articles

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Eye movement characteristics, cognitive characteristics and neural mechanisms of speed reading SUI Xue, AN Yusi, XU Yinan, LI Yutong 2025, 33 (8):  1358-1366.  doi: 10.3724/SP.J.1042.2025.1358 Abstract ( 279 )   PDF (476KB) ( 766 )   Peer Review Comments In the process of reading, the reader needs to understand the literal meaning of the text, combine the preceding and subsequent texts with the reader's knowledge and experience, and establish a coherent mental representation through reasoning (Cai & Liao, 2024; Silawi et al., 2020). The cognitive process of adult readers is basically the same, but there are individual differences in the speed of text information extraction. According to the speed, reading can be divided into slow reading, normal reading and speed reading. speed reading is a kind of reading that is much faster than usual. It is a kind of reading method that readers can understand more reading materials in a short time (Rayner et al., 2016). Speed-reading requires readers to not only have a fast reading Speed, but also ensure the accurate understanding of the reading content, which is limited by speed-accuracy Tradeoff (SAT). In this review, it is found that when reading fast, the reading speed is accelerated, the cognitive processing time is shortened, and the cognitive activity of readers is adjusted to grasp the main idea of the text and ignore the details. In order to improve the reading speed, reader will not deeply process the text, do not do the logical exploration of the text information, and do not do the deep integration processing. The above changes in cognitive processes were also supported by changes in eye movement processes. Speed reading is no longer word-by-word like natural reading, but whole sentences and even whole paragraphs. The above changes in eye movement behavior also correspond to changes in the cognitive process of speed reading. Among them, the fixation time is shortened, only the gist of the text can be grasped, and the details are ignored. However, the saccadic distance is enlarged, the fixation times are reduced, the text cannot be deeply processed, and the text information cannot be logically explored; The number of regression is reduced, which is not conducive to deep integration processing. Studies on the brain mechanism of speed reading have found that speed reading involves dynamic connections of multiple brain regions (Lee & Stoodley, 2024). With the acceleration of reading speed, there were significant changes in the occipital and temporal lobes, indicating that there were functional connections between the occipital and temporal brain regions. The changes in reading speed mainly changed the connections of the brain regions in the left hemisphere. In short, rapid reading focuses on mastering the main idea of the text, but ignores the details. It is difficult to explore the logic between the previous and later information too much, and it is difficult to carry out in-depth integrated analysis. From the eye movement process, the fixation time is shortened, the fixation frequency is reduced, the fixation range is expanded, and the regression is reduced. The realization of speed reading mainly depends on the activity of the occipitotemporal region, some regions have increased activation inhibition, and some network connections are enhanced. The problems to be solved in the future are as follows: (1) The essence of the relationship between the realization of speed reading and the change of external eye movement behavior and internal cognitive process; (2) The relationship and mechanism between internal speech reduction and overall perception; (3) Explore the neural network related to speed reading; (4) The influence of reading materials and question setting in the speed reading experiment. References | Related Articles | Metrics

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The cognitive mechanisms of cross-situational word learning deficits in children with autism spectrum disorder YANG Tongshu, HUANG Yanli, XIE Jiushu 2025, 33 (8):  1367-1378.  doi: 10.3724/SP.J.1042.2025.1367 Abstract ( 196 )   PDF (570KB) ( 269 )   Peer Review Comments Cross-situational word learning deficits are prevalent in children with autism spectrum disorder (ASD). These deficits significantly stunt their language acquisition and affect the development of their communication and social interaction skills. Previous studies have examined the causes of these learning difficulties from various theoretical perspectives to develop diverse language intervention approaches. However, most studies have predominantly focused on the deficits of general cognitive abilities in children with ASD, such as the deficits of memory and attention. Little studies have examined the critical role of language-specific learning mechanisms. As a result, it is not clear what language-specific learning mechanisms contribute to the cross-situational word learning deficits in children with ASD. To fill this gap, the present review systematically synthesizes the progress in research on cross-situational word learning in children with ASD and delves into the underlying causes of their learning difficulties by examining the intrinsic mechanisms of language acquisition. Specifically, the present review first summarizes recent advancements in word learning in children with ASD, highlighting that children with ASD have some specific impairments in cross-situational word learning, such as slower learning speed. To better understand these specific impairments, the present review first distinguishes between implicit and explicit word-learning processes in word learning. After reviewing previous studies, the present review concluded that among children with ASD, implicit learning appears to be relatively intact, whereas explicit learning may be impaired. This divergence carries profound implications for uncovering how children with ASD acquire new words across different situations. Then, the present review focuses on the mechanisms of associative learning and hypothesis testing in cross-situational word learning, examining their relationships with implicit and explicit learning. Specifically, associative learning seems to engage implicit processes, while hypothesis testing relies on explicit learning. Since children with ASD demonstrate relatively intact implicit learning but impaired explicit learning, this study proposes that their associative learning remains preserved, while their hypothesis testing abilities are compromised. To promote the cross-situational word learning ability of children with ASD, it is necessary to fully leverage their intact associative learning and promote their impaired hypothesis testing. Therefore, the present review investigates the factors influencing associative learning and hypothesis testing, particularly referent diversity and word frequency distribution. Referent diversity may help children with ASD strengthen word-object associations and make full use of their relatively intact association learning mechanism, thus improving both the effectiveness of cross-situational word learning and generalization. The Zipfian distribution is a long-tailed skewed distribution, which reinforces the use of the mutual exclusivity strategy. Therefore, the Zipfian distribution may help to improve the hypothesis testing of ASD children. Based on the points discussed above, the present review proposes a novel hybrid synergistic model that integrates both implicit and explicit learning mechanisms in cross-situational word learning. This model posits that cross-situational word learning in children with ASD may be enhanced by simultaneously leveraging both implicit and explicit learning systems. For instance, implicit learning may benefit from repeated exposure to varied situations, while explicit learning may be promoted through structured and targeted interventions. The hybrid synergistic model first reveals the cognitive mechanisms underlying cross-situational word learning in children with ASD and provides instructive implications for intervention. Specifically, the hybrid synergistic model proposes that enhancing referent diversity may improve associative learning, thereby improving the efficiency of cross-situational word learning in children with ASD. When children are exposed to diverse referents for a target word, the children are more likely to form robust and flexible word-referent associations. Additionally, the hybrid synergistic model recommends leveraging Zipfian distribution to optimize hypothesis testing mechanisms. This approach may facilitate the formation of accurate hypotheses about word meanings by providing children with frequent and predictable exposure to high-utility words, thereby mitigating explicit learning deficits. In summary, the hybrid synergistic model not only advances the theoretical understanding of cross-situational word learning in children with ASD but also provides a valuable framework for developing innovative language intervention methods tailored to the unique learning profiles of children with ASD. By integrating insights from both implicit and explicit learning mechanisms, the hybrid synergistic model proposes a comprehensive approach to addressing the language acquisition challenges faced by children with ASD. Furthermore, the model highlights the importance of individual differences in learning styles and cognitive profiles when designing interventions. For example, some children with ASD may benefit more from implicit learning strategies, while others may require additional support for explicit learning processes. Tailoring interventions to each child's specific needs may maximize the effectiveness of interventions and help children with ASD achieve their full potential in language development. References | Related Articles | Metrics

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Dynamic experience of power: The effects and theoretical explanations of power fluctuation YUE Siyi, SUN Yu, ZHAN Xiangping, MA Hongyu, WANG Huaiyong 2025, 33 (8):  1379-1394.  doi: 10.3724/SP.J.1042.2025.1379 Abstract ( 224 )   PDF (596KB) ( 470 )   Peer Review Comments Although power fluctuation is prevalent in organization settings, our understanding of it rather limited. Previous studies have explored power fluctuation from diverse theoretical perspectives. Through a review of relevant literature, it is found that power fluctuation may have dual effects: it can induce both beneficial and detrimental consequences. However, the mechanisms underlying why and how power fluctuation leads to differential outcomes remain unclear. Therefore, the purpose of this review is to propose an integrated framework to explain the double-edged sword effect of power fluctuation. This contributes to broadening the scope of power research and offering a new perspective for optimizing organizational management and job design. By conceptualizing power fluctuation as a stressor, this review argues that the cognitive appraisal theory of stress can provide a robust explanation for its double-edged sword effect and underlying mechanisms. Therefore, building on the cognitive appraisal theory of stress and incorporating theoretical perspectives from related research, we propose that the impact of power fluctuation—whether positive or negative—depends primarily on individuals' cognitive appraisals and coping strategies. Specifically, when experiencing power fluctuation, individuals engage in cognitive appraisal to judge the stressor and their ability to cope. Integrating the lens of approach-inhibition-avoidance theory to elucidate the negative emotions induced by power fluctuation, we contend that if individuals perceive power fluctuation as an insurmountable obstacle, they may experience negative emotions and adopt a threat appraisal. Consequently, they may prioritize emotion-focused coping strategies to manage these negative emotions. However, if individuals view power fluctuation as a chance for self-improvement and believe in their ability to overcome it, they may adopt a challenge appraisal. In this case, they are more inclined to employ problem-focused coping strategies to effectively leverage potential opportunities. Furthermore, individuals' diverse appraisals and coping strategies result in differential outcomes. By integrating insights from boundary theory and social distance theory, this study seeks to understand the specific manifestations of stress responses to power fluctuation. Specifically, when emotion-focused coping strategies hinder individuals from fulfilling the demands and responsibilities of multiple power roles, they are likely to experience adverse outcomes, such as reduced well-being and increased burnout. In contrast, when problem-focused coping strategies enable individuals to adapt effectively to the shifting boundaries of social interactions, positive outcomes, such as enhanced team performance and increased cooperation, may result. Building on these theoretical foundations, the current study posits that cognitive appraisal and coping serve as key psychological mechanisms underlying the double-edged sword effect of power fluctuation. In addition, the ultimate goal of power fluctuation research is to foster the healthy growth and sustainable development of both individuals and organizations. From an applied perspective, the Janus-faced nature of power fluctuation necessitates strategies to maximize its benefits while minimizing its drawbacks. Therefore, by considering key moderating factors, the integrated framework proposes that the appraisal and coping processes depend on individuals' coping potential and externally available resources, ultimately determining whether power fluctuation manifests as adaptive or adverse consequences. In conclusion, this integrative framework extends the existing literature in two key ways. First, it delineates the psychological processes (i.e., appraisal and coping) as critical mediators linking power fluctuation to substantive outcomes. Second, it provides multidimensional mitigation mechanisms for both individuals and organizations to alleviate the negative impacts of power fluctuation. Nevertheless, as an emerging research field, power fluctuation still has many unresolved questions. In view of this, this review provides a strategic agenda highlighting directions for future research. In the future, it is necessary to further clarify the conceptualization and measurement of power fluctuation, deepen the understanding of its mechanisms, explore the heterogeneity among different trajectories of fluctuation, and combine power fluctuation research with broader fields such as decision-making, so as to advance both theoretical development and practical applications of power fluctuation. References | Related Articles | Metrics

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Employee online knowledge seeking: Concept, antecedents, and mechanisms WU Yidan, LI Bin 2025, 33 (8):  1395-1407.  doi: 10.3724/SP.J.1042.2025.1395 Abstract ( 273 )   PDF (586KB) ( 407 )   Peer Review Comments Digitalization greatly changes how knowledge is stored and how individuals access it. In response to this transformation, many organizations have created internal knowledge repositories and communication platforms aimed at storing knowledge and promoting knowledge exchange among employees. Consequently, online knowledge seeking has gradually become the mainstream way for employees to seek knowledge. However, the literature on online knowledge seeking remains unclear. Previous studies have primarily focused on knowledge sharers, while the extant research results on employees' knowledge seeking are fragmented. There is a lack of systematic understanding of the antecedents of their behaviors, especially online knowledge seeking. To address these gaps, this study reviews the concepts, antecedents, and mechanisms underlying employee online knowledge seeking. The goal is to gain a comprehensive understanding of the current state of research in this area and to develop an integrated theoretical framework. This study aims to lay a solid theoretical foundation for future research and offer practical guidance for business managers. Firstly, based on a review of related studies, this study conceptualized employee online knowledge seeking. Online knowledge seeking represents an evolution of traditional face-to-face knowledge seeking, driven by technological advancements and emphasizing the adoption of technology by individuals. Therefore, we defined it as the purposeful behavior of employees searching for and acquiring knowledge through computer-mediated online communication systems. In addition, we categorized the various manifestations of employee online knowledge seeking based on four key dimensions: knowledge content, network tools, acquisition strategy, and seeking object. These are classified into exploitative and exploratory knowledge seeking, intra-organizational and inter-organizational network knowledge seeking, static and dynamic knowledge acquisition strategy, as well as seeking knowledge from a specific-object and open-object. Secondly, in order to clarify the differences between online knowledge seeking and face-to-face knowledge seeking, this study examined their connections and distinctions across six dimensions: communication medium, temporal-spatial requirements, seeking object, scope of knowledge, feedback documentation, and skill requirements. Furthermore, we discussed the unique value of online knowledge seeking among the new generation of employees, which aligns with their interpersonal communication habits. Additionally, the study explored employees' preferences for two knowledge seeking approaches from the perspectives of individual factors (such as perceptions of social psychological costs) and situational factors (such as the nature of the organization). Finally, this study systematically examined the factors influencing employee online knowledge seeking through three dimensions: technological, organizational, and environmental. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), the study delved into the mechanisms of employee online knowledge seeking and proposes five propositions. The results indicate that employees' performance expectancy prior to online knowledge seeking positively influences their intention to seek knowledge online, while effort expectancy negatively predicts their behavioral intention. Meanwhile, individuals' perceptions of social influence and facilitating conditions positively affect their willingness to online knowledge seeking. In addition, the strength of employees' intention to seek knowledge online positively predicts their actual behavior, and employees adopt different knowledge-seeking strategies in their actual online knowledge seeking behavior depending on their specific purposes. Although this study systematically explored the antecedents and mechanisms of employee online knowledge seeking, related research is still in its infancy. In the future, researchers could further develop a model of choice preferences between face-to-face and online knowledge seeking to understand employees' preferences for these two modes under the influence of multiple interacting factors. At the same time, future studies could adopt various theoretical perspectives, such as the Job demands-resources model (JD-R), and integrate qualitative or quantitative research methods to continuously advance the investigation into the antecedents and mechanisms of employee online knowledge seeking. Additionally, by incorporating behavioral data and quantifying relevant indicators, researchers could further validate the framework of antecedents and formation mechanisms of employee online knowledge seeking proposed in this study, thereby providing robust empirical evidence for this field. References | Related Articles | Metrics

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Dialect stereotypes in advertising: Effects and theoretical mechanism LING Bin, LIU Yingkai 2025, 33 (8):  1408-1424.  doi: 10.3724/SP.J.1042.2025.1408 Abstract ( 288 )   PDF (675KB) ( 308 )   Peer Review Comments In the current social context characterized by the coexistence of globalization and multiculturalism, dialects, as linguistic variants, play a significant role in advertising and marketing. Dialect stereotypes refer to people's fixed and generalized cognitive evaluations of dialects, which are inherently complex and multidimensional, unconsciously influencing individuals' attitudes and behaviors toward dialect advertisements. There persists a systematic gap in addressing three critical questions: (1) What is the concrete meaning of dialect stereotypes, (2) how dialect stereotypes affect the persuasive effectiveness of advertisements across different theoretical perspectives, and (3) what specific boundary conditions moderate these effects. First, this paper commences by precisely defining dialects and related concepts such as accents, colloquialisms, jargon, internet slang, and phonetic variations. Next, it systematically elaborates dialect stereotypes through three core dimensions: (a) linguistic features that cover phonological patterns, lexical choices, phrasal structures, and pragmatic conventions; (b) user image constituted by status, solidarity, and dynamism; and (c) social culture manifested through regional identity shaping, emotional resonance intensification, local culture representation, and character persona formation, which collectively delineate the research scope of dialects and their stereotypes in advertising. Then, building upon four theoretical frameworks—markedness theory, dual process theory, social identity theory, and spatial presence—the paper thoroughly explores the underlying mechanisms of dialect stereotype effects in advertising and reveals hierarchical differences in explanatory level and focus. Specifically, markedness theory, rooted in dialect linguistic features, emphasizes how the markedness of dialect advertisements influences consumer expectation levels, with particular attention to advertisement comprehensibility and memorability. Dual process theory focuses on speaker image, detailing the distinct roles of system 1 (automatic processing) and system 2 (controlled processing) in enhancing brand awareness and advertisement credibility. Social identity theory addresses the cultural connotations of dialects and users' identity, highlighting cognitive and affective responses such as in-group favoritism and out-group discrimination. Spatial presence concerns how dialect linguistic and cultural attributes synergistically create immersive experiences that shape product authenticity perceptions and consumer experience. Concurrently, the paper clarifies the explanatory boundaries of these theories and analyzes five moderating factors: consumers' individual traits, product attributes, brand characteristics, spokespersons' individual traits, and advertising appeals. By delineating theoretical distinctions and interconnections, this paper identifies distinct cognitive processing pathways associated with each theory. Markedness theory plays a pivotal role during initial information processing, demonstrating how dialect advertisements rapidly capture consumer attention through distinctive linguistic features. Dual process theory further refines subsequent processing stages: System 1 triggers automated processing based on preexisting dialect stereotypes, eliciting social identity effects, while System 2 engages consumers in deliberate evaluation of advertisement content. At this stage, Spatial presence explains how dialect linguistic characteristics and cultural attributes enhance product authenticity perceptions and enrich consumers' sensory experiences. Given the potential simultaneity of these interrelated cognitive processes, the paper proposes integrative possibilities across theoretical frameworks: System 1 processing in dual-process theory corresponds to stereotype-based cognitive-affective responses encompassing social identity effects, whereas markedness theory implicitly incorporates fluency processing of dialect information when explaining advertisement markedness. These theoretical intersections provide novel directions for future research. At the same time, the paper further advances a theoretical model of dialect stereotype effects in advertising, which can offer some implications for advancing theoretical frameworks and mechanistic investigations in dialect advertising research. Future research should prioritize three directions. First, diversifying linguistic forms in dialect advertisements through innovative approaches such as dialect-standard language hybrid advertisements and specific dialectal features (e.g., inverted sentence structures, retroflex suffixes, reduplication patterns). Second, examining how cultural values—particularly collectivism-individualism orientations, temporal perspectives, and cultural identification levels—moderate advertising persuasion effects through empirical validation of underlying mechanisms. Third, exploring artificial intelligence (AI) applications in dialect advertising, including investigating the effectiveness of dialect advertisements in AI environments, identifying key influencing factors, and developing theoretical foundations for integrating dialect advertising design with AI technologies to drive innovation in advertising practices. References | Related Articles | Metrics

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Marketing effect of virtual influencers and its mechanisms in the context of AI technology LI Yan, CHEN Wei, WU Ruijuan 2025, 33 (8):  1425-1442.  doi: 10.3724/SP.J.1042.2025.1425 Abstract ( 322 )   PDF (769KB) ( 392 )   Peer Review Comments With the rapid advancement of AI technology, virtual influencers have become an increasingly prominent presence on social media. These AI-driven digital personas not only attract attention by sharing engaging content but also directly promote and offer products or services to their followers, facilitating transactions and exerting a profound influence on consumer behavior. As a result, an increasing number of brands are actively leveraging virtual influencers to enhance brand visibility, promote products, and establish deeper connections with their audiences. The application of virtual influencers in the advertising and marketing industry has already shown promising results, highlighting their potential as an effective tool for consumer engagement. Powered by AI technology, virtual influencers can realistically mimic human characteristics and personalities, enabling them to precisely appeal to specific market segments. By meticulously designing their appearance, behavior, and communication style, these virtual entities create a strong sense of relatability, effectively engaging audiences. As an emerging marketing approach, virtual influencer marketing has already demonstrated considerable effectiveness in practice. However, despite its increasing adoption, theoretical research on this phenomenon remains in its early stages, necessitating a systematic review of existing studies to assess its current research status and development trends. To this end, this study first clarifies and defines the concept and connotation of virtual influencers. At the character design level, it identifies several key factors that influence the marketing effectiveness of virtual influencers, including character backstory, distinctive personality, emotional module, and controlling entity. These elements play a crucial role in shaping consumer perceptions and engagement levels. Furthermore, based on the two dimensions of form realism and behavioral realism, this study innovatively categorizes virtual influencers into six distinct types: spokesperson humanlike virtual influencers, influencer humanlike virtual influencers, spokesperson anime-like virtual influencers, idol anime-like virtual influencers, mascot nonhumanlike virtual influencers, and storyteller nonhumanlike virtual influencers. The study further investigates the mechanisms and moderating factors that contribute to both the positive and negative marketing effects of virtual influencers. Compared to human influencers, virtual influencers present significant advantages that can lead to more favorable marketing outcomes for businesses: (1) Companies can maintain complete control over the behavior and performance of virtual influencers, ensuring a high degree of brand alignment; (2) Virtual influencers can effectively capture consumers' attention and create a sense of novelty, thereby enhancing consumers' perception of advertising and brand innovation; (3) The use of virtual influencers allows brands to mitigate risks associated with human endorsers, such as controversies, scandals, or reputational damage, thereby ensuring greater stability in long-term marketing campaigns. However, the use of virtual influencers may also lead to certain negative effects: (1) Algorithm aversion may cause consumers to resist AI-generated virtual digital humans, particularly when they have encountered algorithmic errors, unnatural interactions, or unsatisfactory results; (2) According to the uncanny-valley effect, virtual influencers that appear excessively human-like may evoke discomfort or negative psychological reactions among consumers; (3) Consumers may develop an awareness of falsity of these virtual influencers, leading to distrust, which in turn negatively impacts their credibility and endorsement effectiveness. Furthermore, this study examines several crucial moderating factors from both the advertising design and consumer behavior perspectives. These factors include source transparency (i.e., whether the AI-generated nature of the virtual influencer is disclosed), product categories and characteristics, application scenarios, and individual differences among consumers. Finally, this study summarizes the marketing effects of virtual influencers and explores future research directions in key areas such as technological empowerment, underlying mechanisms, marketing outcomes, and ethical considerations. Additionally, it examines the broad development prospects of virtual influencer marketing, including applications in advertising endorsements, brand personification, and customer engagement. This study also identifies critical challenges, such as technological and cost barriers, market acceptance, content management, and legal and ethical concerns. This study not only contributes to the academic understanding of virtual influencer marketing but also provides practical guidance and recommendations for businesses and marketing practitioners regarding the application and strategic planning of virtual influencers. References | Related Articles | Metrics

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A decade review and future prospects of community identity research in China CHI Liping, XIN Ziqiang 2025, 33 (8):  1443-1456.  doi: 10.3724/SP.J.1042.2025.1443 Abstract ( 248 )   PDF (564KB) ( 404 )   Peer Review Comments A community is a social life collective composed of residents living in the same geographical area who share psychological and spiritual connections. The formation of communities relies on common psychological foundations among residents and their identification with the community. Western scholars typically use "sense of community" to describe this psychological state, but its theoretical frameworks and measurement tools have multiple limitations when applied to Chinese cultural contexts. Consequently, Chinese scholars prefer the concept of "community identity" and have developed localized measurement instruments. Research on community identity not only helps understand the psychological states of community residents but also provides theoretical support for community governance and psychological well-being construction. Community identity is defined as a two-dimensional structure comprising functional identity and emotional identity. Functional identity refers to residents' satisfaction with and recognition of community functions, such as convenience, management quality, and environmental conditions. Emotional identity denotes the affective bond and acceptance between residents and their community, manifested as special emotional connections and a sense of belonging. Based on this framework, researchers developed the Community Identity Scale containing 8 items measuring both dimensions. For instance, "Living in this community makes daily life very convenient" and "I highly approve of this community's management standards" assess functional identity, while "My residential community holds special emotional significance for me" and "This community gives me a sense of home" measure emotional identity. The scale demonstrates good reliability and validity across multiple studies and has been widely applied in psychology, sociology, public administration, and other disciplines. Building on this two-dimensional framework, this paper systematically reviews how resident characteristics and community living features influence community identity, examines its psychological and behavioral consequences, and proposes future research directions. Findings indicate that demographic factors including gender, age, education level, income, and household registration exert varying degrees of influence. Age shows positive correlation with emotional identity, whereas education and income significantly affect functional identity. Community living characteristics like length of residence, housing type, and community environment also shape identity formation. Duration of residence significantly impacts emotional identity, while housing type and community environment operate through "need satisfaction" and "Mutual embedding" mechanisms respectively. Community identity formation positively influences proximal variables such as neighborhood interaction, community participation, and psychological ownership of community, which subsequently affect distal outcomes including sense of control, life satisfaction, and altruistic behaviors. Despite progress in Chinese community identity research, limitations persist in theoretical construction, inadequate examination of community-level factors, lack of temporal perspectives, methodological homogeneity, and insufficient intervention studies. Future research should improve in five aspects: Firstly, the theoretical construction of community identity generation mechanisms requires strengthening. The validated two-dimensional structure reveals distinct dimensions: Functional identity relates to "threshold factors" like community type, property rights, education and income, reflecting satisfaction with community functions. Emotional identity emerges from "Mutual embedding" between residents and communities, involving mutual integration of individual life courses and community networks. This Mutual embedding requires time and correlates with age, residence duration, and household registration. The proposed "need satisfaction" and "Mutual embedding" mechanisms require further testing, along with exploring new research questions about cross-level variable interactions, temporal dimensions, shared meaning construction, and agent-environment dynamics. Secondly, influencing factors of community identity should be explored from both individual and community levels. Research should adopt a person-community interaction perspective using multilevel linear models to investigate how community-level characteristics (property management quality, neighborhood committee performance, service provision) interact with individual variables (life satisfaction, service needs) to shape community identity. Thirdly, the dynamic evolution of community identity needs to be examined across individual and community temporal dimensions. Longitudinal studies should track identity development across individual life courses (considering survey timing and generational effects) and community histories (construction phases, renovation events). For instance, analyzing how elevator installation in old neighborhoods transforms strangers into community members. Dynamic reciprocal relationships between identity, neighborhood interaction, and participation should also be examined. Fourthly, the characteristics of community identity should be clarified through both qualitative and quantitative approaches. Quantitative studies should incorporate person-centered analyses like latent profile analysis to identify identity subgroups. Qualitative research should employ interviews, case studies, and digital ethnography to assess policy implementation and inform theory building. Finally, evidence-based intervention experiments and localized action research rooted in community-specific knowledge should be advanced. Evidence-based interventions should target distinct mechanisms: Enhancing emotional identity through interdependent self-construal priming. Action research should engage residents and workers in solving practical issues (e.g., using photovoice methods for waste sorting challenges), simultaneously improving governance and fostering identity. Cultivating community identity serves as a crucial pathway for advancing community psychological well-being, shifting current governance paradigms from institutional overemphasis to human-centered psychological objectives. References | Related Articles | Metrics