ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

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    When artificial intelligence faces human emotions: The impact mechanism of emotion expression in AI-empowered service robots on user experience
    LUO Lijuan, WANG Kang, HU Jinmiao, XU Sihua
    Advances in Psychological Science    2025, 33 (6): 1006-1026.   DOI: 10.3724/SP.J.1042.2025.1006
    Abstract3296)   HTML163)    PDF (1631KB)(8079)      

    The rapid advancement of next-generation AI technologies has fundamentally reshaped interaction patterns between users and service providers. Nowadays, users not only expect AI to effectively solve problems but also aspire to gain positive emotional experiences during the interaction process. However, current AI services still face challenges such as user resistance, low acceptance, and poor service experiences. Addressing how service robots can establish effective emotional communication with users to deliver personalized, intelligent, and empathetic service experiences has become a critical research frontier.

    This study investigates the holistic process of user-service robot interaction through the lens of AI-empowered emotion connection, establishing an integrated framework of "Emotion recognition, Emotion understanding, Emotional connection." We propose the following research framework and systematically investigates three principal research dimensions:

    (1) User emotion recognition and emotion matrix construction based on a multidimensional emotion computing model. A user emotion recognition computing model is developed using machine learning algorithms and decision-level weighted fusion to resolve inconsistencies in cross-dimensional emotional expressions. Building upon the established multidimensional emotion recognition model, the valence-arousal-dominance (VAD) model is adopted as the analytical framework. Through combined machine learning and qualitative analysis methods, we systematically characterize users' emotional responses across different service stages and contexts. This research concept helps build a bridge between emotion recognition and service interaction, laying the foundation for real-time emotional responses with service interactions.

    (2) The impact mechanisms of AI-empowered emotional expression content on user experience from the perspective of service journey. Human-robot interaction processes can be categorized into three sequential stages: initial service encounter, service usage, and service feedback. The initial encounter stage prioritizes AI emotional expression to stimulate user interest and establish trust, while the usage stage focuses on delivering affective experiences to enhance satisfaction. The feedback stage aims to mitigate user dissatisfaction and attain forgiveness. Aligning with stage-specific objectives, we propose differentiated emotional expression strategies. Drawing on Trust Theory, Cognitive Appraisal Theory, and Basic Psychological Needs Theory, we hypothesize that service robots' implementation of stage-specific emotional expressions (positive emotion in initial encounters, empathy during service usage, and gratitude in service feedback) can systematically enhance user experience. This study delves into the underlying mechanisms of AI-empowered emotional expression content on user experience at each service stage. Moreover, we also propose three moderating factors—the anthropomorphic features of AI, time pressure, and the types of explanatory information provided—as boundary conditions in different stages. This hypothesis framework enables the systematic investigation of when and why differentiated emotional content across service journey stages impacts user experience. This research concept fosters a holistic and dynamic understanding of service journey stages, highlighting the significance of leveraging AI emotional intelligence to activate user experience throughout the journey.

    (3) The impact mechanisms of AI-empowered emotional expression modalities on user experience from the perspective of service contexts. Service contexts are classified into hedonic-oriented and utilitarian-oriented scenarios, where user preferences diverge significantly. Hedonic contexts center on experiential values like enjoyment, pleasure, and emotional engagement, while utilitarian contexts emphasize functional benefits including practicality, efficiency, and utility. Through the theoretical lenses of Social Presence Theory, Psychological Distance Theory, and Emotions-as-Social-Information Theory, we hypothesize that service robots' implementation of embodied emotional expression modalities (mono-sensory vs. multisensory) in hedonic-oriented and utilitarian-oriented service contexts can significantly enhance user service experience. This study further examines the underlying mechanisms of AI-empowered embodied emotional expression modalities on user experience at each service context. Moreover, we also propose two moderating factors—relationship norm orientation and task complexity—as boundary conditions in different contexts. This hypothesis framework enables the systematic investigation of when and why emotional expression modalities across distinct service contexts impact user experience. This research concept fosters differentiated thinking on the modalities of AI's emotional expression in service contexts, shedding light on the importance of emotional modalities in both hedonic-oriented and utilitarian-oriented service contexts.

    This study advances the understanding of emotional expression mechanisms in service robots and user experience enhancement strategies within intelligent services. It offers significant theoretical contributions and practical insights. In terms of theoretical significance, this research enriches human-AI interaction theory by proposing a comprehensive framework for service robots' emotional expression mechanisms. It empirically demonstrates how AI-driven affective expressions activate and influence user experience while clarifying underlying mechanisms, thereby advancing the theoretical foundation for emotionally intelligent interaction design. In terms of practical significance, this research provides a new direction for the integrated development of AI and service industry, enabling service providers to optimize touchpoints across the service journey. More importantly, it underscores the value of affective intelligence, providing robust support for the high-quality and sustainable development of the service robotics industry.

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    Why do humans procrastinate? An interpretation based on a multi-modal and multi-omics perspective
    XIAO Yao, WANG Xueke, FENG Tingyong
    Advances in Psychological Science    2025, 33 (3): 520-536.   DOI: 10.3724/SP.J.1042.2025.0520
    Abstract2587)   HTML344)    PDF (1313KB)(4818)      

    Procrastination is a cross-culturally prevalent problematic behavior that significantly impacts learning, work, daily life, emotions, and overall well-being. While existing literature has explored its causes and influencing factors, a comprehensive understanding of procrastination’s etiology remains elusive. This paper adopts a multi-modal, multi-omics perspective to systematically review and analyze the cognitive mechanisms, neural foundations, genetic bases, and potential metabolic underpinnings of procrastination. We propose an integrated theoretical framework incorporating cognitive-neurological-genetic-microbial- metabolic based on it, aiming to elucidate the complex mechanisms underlying procrastination and provide a more comprehensive view of its occurrence, development, and formation. Future research should enrich molecular genetic, metabolic, and microbiome studies of procrastination, further integrate multi-modal and multi-omics research, and explore the developmental mechanisms of procrastination from a longitudinal perspective. These efforts will facilitate early detection, prevention, and precise intervention strategies for procrastination behavior.

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    The bidirectional trust in the context of new human-machine relationships
    XIE Yubin, ZHOU Ronggang
    Advances in Psychological Science    2025, 33 (6): 916-932.   DOI: 10.3724/SP.J.1042.2025.0916
    Abstract2004)   HTML116)    PDF (679KB)(5012)      

    In the context of the rapid development of artificial intelligence, the relationship between humans and machines is shifting from the traditional “auxiliary-subordinate” model to a “equal collaboration” or even “symbiotic coevolution” model. Most of research has mainly focused on one-directional trust from humans to machines, but as intelligent agent gains greater autonomy and decision-making capabilities, mutual trust is becoming a central issue in human-machine collaboration. This paper examines the mechanisms for building mutual trust between humans and machines. It also explores measurement methods, and the practical challenges involved. The goal is to provide theoretical support for the design and optimization of future intelligent systems.

    Building on existing human-machine trust frameworks, this paper proposes a dynamic model of mutual trust. It introduces a three-stage model: “dispositional trust-perceived trust-behavioral trust,” which covers both human-to-machine and machine-to-human trust. The model emphasizes perceived trust as the key bridge between dispositional trust and behavioral trust, highlighting its role in the transfer of trust between AI, intelligent agents, and humans. Dispositional trust: The initial stage of trust, which is based on an individual's inherent traits and is independent of specific contexts. It lays the foundation for the subsequent development of trust. Perceived trust: Gradually formed during interaction, this stage reflects the dynamic perception of the other party's behavior, attitude, and trustworthiness. It is the core of emotional trust transfer and dynamic adjustment. Behavioral trust: The final manifestation of trust, expressed through concrete behaviors such as dependence, cooperation, and action. It is post-action trust based on behavioral feedback, reflecting the ultimate outcome of the trust relationship.

    The advantages of this model are reflected in several key aspects. First, its dynamic evolutionary characteristics allow the model to fully capture the development of trust from dispositional trust to perceived trust and finally to behavioral trust, effectively accommodating the complexity and variability of trust relationships in human-machine interactions. Second, the model emphasizes bidirectional trust transfer, focusing on the interaction between humans and intelligent agents. It highlights the role of perceived trust as the crucial bridge between dispositional trust and behavioral trust, offering in-depth insights into its significance in emotional trust transfer and dynamic adjustment, thus providing unique guidance for optimizing human-machine interaction. Third, the model introduces an expanded perspective on dispositional trust by incorporating algorithmic trust, exploring the sources of initial trust in algorithms and the impact of individual algorithm aversion, thereby offering a new theoretical foundation for algorithmic trust research. Lastly, the model provides an in-depth analysis of behavioral trust, emphasizing the impact of machine behavior on human-machine trust, such as the negative effect on “perceived trustworthiness” when a machine denies a human request, and revealing the emotional and behavioral consequences of trust misalignment.

    The purpose of this theoretical model is to develop methods for measuring and modeling human-machine mutual trust based on the characteristics of different scenarios. Building on a review of existing measurement methods and drawing from interpersonal trust measurement experience, this paper introduces a framework and methods for mutual trust between humans and machines. The study focuses on several key areas: developing stage-specific measurement tools for dispositional trust, perceived trust, and behavioral trust; exploring multidimensional, multilevel methods that combine subjective reports, physiological signals, and behavioral data to create a dynamic monitoring and calibration system; and adapting interpersonal trust quantification methods to design trust modeling tools suited to human-machine interactions. Ultimately, this research aims to provide a systematic, operable framework for measuring and modeling mutual trust, laying the foundation for dynamic evaluation and intelligent adjustment.

    In terms of application, this paper examines the practical value of mutual trust through case studies in autonomous driving and aviation. It also discusses current challenges, such as individual differences that hinder trust development, the lack of standardized tools for measuring machine trust, and the unclear long-term psychological effects of mutual trust on users. The paper calls for further research to refine trust measurement tools, address issues of “over-trust” or “mistrust” in human-machine trust alignment and define the boundaries of machine trust behavior within ethical and legal frameworks. By integrating theoretical and methodological innovations, this paper offers new directions for research on trust mechanisms in human-machine collaboration and provides valuable guidance for the development of efficient and safe intelligent systems.

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    Promoting or inhibiting? The double-edged sword effect of acceptance of generative AI advice on creativity
    ZONG Shuwei, YANG Fu, LONG Lirong, HAN Yi
    Advances in Psychological Science    2025, 33 (6): 905-915.   DOI: 10.3724/SP.J.1042.2025.0905
    Abstract1848)   HTML92)    PDF (576KB)(3591)      

    Generative artificial intelligence (GAI), as an emerging artificial intelligence technology, has showcased remarkable creativity across diverse domains. However, members of an organization are the main carriers of corporate innovation and creativity. A pivotal question persists in academic circles: Will the integration of GAI technology into the workplace and the promotion of its work suggestions or solutions among organizational members stimulate or stifle their creativity? This question remains largely unexplored, particularly regarding the phenomenon of advice taking from GAI within the realm of organizational management. This study aims to address this significant gap. Initially, grounded in the context of organizational management, we elucidate the connotation and dimensions of advice taking from GAI, addressing the fundamental inquiry: “what constitutes advice taking from GAI?”. Subsequently, we investigate the double-edged sword effects of advice taking from GAI on creativity, examining its effects at both the employee and team levels through the lenses of social cognition and goal orientation. We endeavor to clarify the issue, “what is the relationship between advice taking from GAI and creativity?”. Finally, we synthesize an exploration of the boundary conditions for the effect of advice taking from GAI, seeking to answer: “under what circumstances does advice taking from GAI yield positive outcomes?”

    This study makes three contributions. First, we explore the dimensions and measurement of the construct of advice taking from GAI, paving the way for new research directions in advice taking. In the digital era, AI has become a novel source for organizations to acquire unique information and generate personalized recommendations. Conducting in-depth academic research in this area contributes to a better understanding of the effectiveness of advice taking from GAI. Unfortunately, existing studies primarily focus on the immediate effects of GAI on idea generation, neglecting that the application of GAI in organizations is a multi-stage process, rather than a simple tool usage. In response, study 1 introduces a refined framework comprising three stages: advice solicitation, advice evaluation, and advice adoption, highlighting the iterative interaction process between organizational members and GAI. By investigating how organizational members gradually adopt GAI advice and examining the impact of this process on employee creativity, this study broadens the scope of research on the relationship between AI techniques and human creativity. It also underscores the importance of focusing on the iterative processes of screening, evaluating, adjusting, and applying AI advice and the subsequent outcomes.

    Second, from the approach of social cognitive and goal orientation, this study investigates the double-edged sword effect of individual/team advice taking from GAI on individual/team creativity and its underlying mechanisms, expanding the research perspective on the advice taking from GAI. A review of the literature reveals that most existing studies on AI adoption focus on its influencing factors and formation mechanisms, with limited discussion on the effects of AI adoption. In the few studies related to GAI and organizational creativity, the primary focus is on the individual-level use of GAI and its related outcomes, without systematically, comprehensively, or multi-levelly exploring the mechanisms through which advice taking from GAI influences creativity. To address this gap, study 2 and study 3, respectively, examine how the differentiated impacts of advice taking from GAI on individuals' cognitive states and teams' goal orientations drive divergent outcomes for organizational creativity. Specifically, the double-edged sword effect of advice taking from GAI on creativity is explored. This reveals the complex effect mechanism of GAI in different contexts, particularly the dynamic balance between individuals' sense of efficacy and dependence on AI, as well as the competing orientations of teams toward learning goals and performance goals.

    Third, this study systematically identifies corresponding intervention strategies from multiple organizational levels to enhance the positive effects of advice taking from GAI on creativity while mitigating its negative effects. Existing research on the boundary conditions of AI usage effects primarily focuses on individual-level characteristics, with limited attention to the intervention effects of organizational-level factors. As both the practical bearer of AI application outcomes in the workplace and a critical component of AI governance frameworks, organizations hold significant governance responsibilities. In this context, this study proposes intervention strategies from two perspectives: resource support and feedback mechanisms, to examine the boundary conditions under which advice taking from GAI exerts positive or negative effects on creativity. These findings help clarify the organizational levels at which interventions are needed to influence members' advice taking from GAI, providing decision-makers with valuable references.

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    Application of machine learning to improve the predictive performance of non-suicidal self-injury: A systematic review
    GAO Baixue, XIE Yunlong, LUO Junlong, HE Wen
    Advances in Psychological Science    2025, 33 (3): 506-519.   DOI: 10.3724/SP.J.1042.2025.0506
    Abstract1823)   HTML291)    PDF (949KB)(5110)      

    Non-suicidal self-injury (NSSI) is a significant public health problem characterised by widespread stigma, high complexity and heterogeneity. Traditional NSSI research measure and analysis methods are limited, resulting in very low predictive power of the identified factors. In recent years, machine learning has gradually been applied to the analysis and modelling of NSSI. Through simplified questionnaire models and complex multimodal data models, the importance of predictive factors can be visualised and more accurate NSSI classification can be achieved, thus improving the overall predictive performance to a moderate level. In the future, it is necessary to combine traditional NSSI theories and methods to make the screening criteria more stringent, and combine unsupervised learning with transfer learning to increase the reproducibility and comparability of the models. Furthermore, combining non-questionnaire NSSI data with deep learning meanwhile is helpful to improve the predictive performance.

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    Effects of employee mindfulness on job performance and the mediating mechanisms: A meta-analysis
    ZHANG Jing, MENG Yu, ZHANG Meng, CHEN Huiya
    Advances in Psychological Science    2025, 33 (4): 647-672.   DOI: 10.3724/SP.J.1042.2025.0647
    Abstract1705)   HTML155)    PDF (1243KB)(8215)      

    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.

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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
    Advances in Psychological Science    2025, 33 (8): 1267-1274.   DOI: 10.3724/SP.J.1042.2025.1267
    Abstract1651)   HTML129)    PDF (1160KB)(1728)      

    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.

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    A new perspective on social communication in autism: The double empathy problem
    CAO Shoujing, WANG Xin
    Advances in Psychological Science    2025, 33 (4): 539-547.   DOI: 10.3724/SP.J.1042.2025.0539
    Abstract1607)   HTML226)    PDF (514KB)(2770)      

    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.

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    Dynamic processing mechanisms of cognitive maps in navigation in visually cue-restricted environments
    HUANG Lei, ZHANG Junheng, JI Ming
    Advances in Psychological Science    2025, 33 (4): 673-679.   DOI: 10.3724/SP.J.1042.2025.0673
    Abstract1483)   HTML387)    PDF (3728KB)(4140)      

    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.

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    Workplace artificial intelligence role classification: Impacts on employee psychology and behavior and coping strategies
    TAN Meili, YIN Xiangzhou, ZHANG Guanglei, XIONG Puzhen
    Advances in Psychological Science    2025, 33 (6): 933-947.   DOI: 10.3724/SP.J.1042.2025.0933
    Abstract1477)   HTML81)    PDF (753KB)(3102)      

    Artificial intelligence (AI) has become an integral part of modern workplaces. However, understanding its impact on employees remains a complex and dynamic area of research. Through a comprehensive review of relevant literature and taking into account the practical applications of AI, this study innovatively approaches the topic. It classifies AI's roles in the workplace from the employees' perspective into four distinct types: “opponent”, “assistant”, “colleague”, and “leader”. This sets our study apart from previous research, as we place greater emphasis on the employee-centered perspective.

    Based on a review of the literature, this classification is based on the “substitution-aid-augmentation-management” framework. From this perspective, the study systematically examines how these different AI roles influence employees' psychological states and behaviors. When AI functions as an “opponent”, it triggers job-related anxiety among employees. The fear of replacement can lead to negative behaviors such as service disruptions and knowledge hoarding. Conversely, if employees perceive this threat as a challenge, it can stimulate creativity. As an “assistant”, AI enhances work performance by reducing employees' cognitive load. Nevertheless, over-reliance on it may weaken employees' moral judgment and limit their access to diverse information. In the role of a “colleague”, AI collaborates with employees on complex tasks, but it also gives rise to issues like ambiguous job responsibilities and decreased interpersonal communication. When AI assumes the “leader” role, although its precise management has certain advantages, it also raises ethical concerns. Its impersonal communication style and potential privacy invasions can cause stress among employees, and algorithm-based management may undermine employees' creativity and job satisfaction.

    To address these impacts, the study proposes practical strategies for both organizations and individuals. At the organizational level, companies should provide AI-related training to help employees acquire the necessary skills and adapt to changes. Fostering a positive AI-friendly work culture can guide employees to utilize AI effectively and mitigate its negative effects. Additionally, establishing clear AI usage rules and ethical standards is essential for safeguarding employees' rights. At the individual level, employees need to actively develop skills that are difficult for AI to replicate, such as creativity and social skills, and continuously enhance their AI literacy. They should also shift their mindset towards AI, viewing it as an opportunity rather than a threat, to better integrate into the new workplace environment.

    Combining the current research status and the role-by-role analysis presented earlier, we also point out several directions for future research.

    First, we can further explore and refine the classification of AI in the Chinese context. Based on China's unique cultural, economic, and social environment, a more precise classification will provide a better understanding of how AI functions in Chinese workplaces. Next, we can explore the boundary conditions of such impacts. These boundary conditions include factors like organizational culture, employee characteristics, and technological maturity. Third, conducting comparative studies can reveal commonalities and differences in how AI affects employees in different contexts. This can be valuable for organizations operating globally or seeking to learn from different practices. Fourth, analyzing the differential impacts of AI across various industries and job functions helps understand its unique influences in different work settings. Different industries and job roles interact with AI in distinct ways, and this research can offer targeted insights. Fifth, conducting comparative studies can reveal commonalities and differences in how AI affects employees in different contexts. This can be valuable for organizations operating globally or seeking to learn from different practices. Finally, strengthening interdisciplinary research, which integrates knowledge from multiple fields like psychology, computer science, and management, offers a more comprehensive understanding.

    This study contributes to the existing literature by offering a comprehensive classification of AI roles in the workplace and elucidating their differential impacts on employees. By meticulously examining the four roles of AI in the workplace from the employees' perspective, it fills gaps in existing literature and offers a more comprehensive view of their interactions. It also provides practical insights for organizations and individuals to better adapt to the evolving role of AI in the workplace. Future research should further explore the boundary conditions of these impacts, investigate cross-cultural differences in employee responses to AI, and examine the long-term effects of AI on career development and work-life balance.

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    Understanding approach-avoidance conflict dysregulation in anxiety: Cognitive processes and neural mechanisms
    XIA Yi, ZHANG Jie, ZHANG Huoyin, LEI Yi, DOU Haoran
    Advances in Psychological Science    2025, 33 (3): 477-493.   DOI: 10.3724/SP.J.1042.2025.0477
    Abstract1464)   HTML165)    PDF (2575KB)(1409)      

    Effectively resolving approach-avoidance conflicts is crucial in everyday life. However, anxious individuals exhibit behavioral manifestations of dysregulated approach-avoidance conflict. This dysregulation is characterized by abandoning positive outcomes to avoid stimuli that are unrelated to actual threats or less threatening. Traditional motivational theories divide individuals’ coping with approach-avoidance conflict into information input and behavioral output processes. However, these are insufficient to fully explain the specific mechanisms underlying approach-avoidance conflict dysregulation. In this review, we propose a three-stage model comprising conflict perception, conflict processing, and feedback learning. This model emphasizes that approach-avoidance conflict dysregulation in anxious individuals may manifest as heightened threat perception, imbalanced motivation-expected value comparison, and abnormal feedback learning. Future research can further validate the relative independence of these three stages in the model, parameterize the model through hierarchical and modular methods, and explore the mechanisms underlying approach-avoidance conflict dysregulation in anxious individuals through a developmental perspective.

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    Trends and dynamics in longitudinal research: Model development, integration, and differentiation
    LIU Yuan, YAO Zhichen
    Advances in Psychological Science    2025, 33 (7): 1181-1198.   DOI: 10.3724/SP.J.1042.2025.1181
    Abstract1400)   HTML30)    PDF (1033KB)(4794)      

    “Trends” and “dynamics” represent two significant themes in longitudinal research. To address the challenges associated with trends and dynamics, researchers frequently utilize panel data or intensive longitudinal data to gather information, allowing for the development of various statistical models.

    The trends study analyzes systematic changes and usually operates at the between-person level. It illustrates the general developmental trajectory while acknowledging individual differences. To effectively capture these general trends, researchers often use panel data, as it involves wide intervals and prolonged periods of data collection. A commonly employed model for this purpose is the latent growth model (LGM) combined with a multilevel model (MLM). In contrast, the dynamic study focuses on temporal changes within individuals, typically at the within-person level. It examines autoregressive and cross-lagged relationships from earlier time points to later ones. Both panel data and intensive longitudinal data could address dynamic issues because they allow for the measurement wave over short time intervals and necessitate a large number of measurements. Cross-lagged panel models (CLPM) are frequently used to analyze dynamics for panel data, while time series analysis and dynamic structural equation models (DSEM) are commonly applied to intensive longitudinal data.

    As research questions become more intricate, we rely on models that integrate trends and dynamics, yielding numerous integrated models. For panel data, this includes the random intercept cross-lagged model (RI-CLPM), autoregressive latent trajectory model (ALT), and latent curve model with structural residuals (LCM-SR), among others, to provide a comprehensive approach to addressing the interplay of trends and dynamics. In the context of intensive longitudinal data, where stationary is a preassumption for time series modeling, models incorporating the detrending process have been developed, such as the dynamic structural equation model (DSEM) and residual dynamic equation model (RDSEM), etc.

    We utilized empirical data from the 2013 Health and Retirement Study (HRS) to demonstrate the practical application of various longitudinal models. Our findings revealed that the integrated models, such as ALT and LCM-SR, exhibited superior model fit. This suggests that there are developmental trends that must be accounted for. The ALT model displayed significant autoregressive and cross-lagged relationships among the target variables, whereas the LCM-SR models did not.

    In conclusion, we compared various longitudinal models and provided practical recommendations. First, researchers should determine the appropriate data collection paradigm to employ. When the number of measurement waves is ten or fewer and the time intervals are large, panel data is suitable; otherwise, an alternative approach should be considered. The long format is also suggested for intensive longitudinal data. Second, since the stationary is crucial in dynamic research, it is essential to assess trends. Panel data can be analyzed for trends using LGM or MLM with time covariates, while intensive longitudinal data (through time series analysis) should employ stationary tests. Descriptive statistics can also provide valuable insights. If trends are present, panel data should utilize an integrated model that encompasses both trends and dynamics, whereas intensive longitudinal data should adopt detrending models. In the absence of trends, direct dynamic modeling can be applied. Specifically, if the goal is to distinguish between trends and dynamics, researchers should consider residual models such as RI-CLPM, LCM-SR, and RDSEM. Conversely, for research emphasizing dynamics, cumulative models like ALT and DSEM should be applied.

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    Exploring the impact of focus back effort training on mind wandering and its mechanisms
    HE Hong, ZHANG Xinyue, SHI Jinghong, LIU Qiang
    Advances in Psychological Science    2025, 33 (7): 1077-1090.   DOI: 10.3724/SP.J.1042.2025.1077
    Abstract1399)   HTML73)    PDF (1791KB)(1609)      

    Humans spend a significant amount of time engaged in mind wandering. While it is a common cognitive state, it is often seen as a hindrance to productivity and daily functioning. Minimizing its frequency is crucial for maintaining focus and efficiency in academic, professional, and personal life. This study is grounded in the resource control theory of mind wandering and the dynamic framework model, proposing that focus back effort reflects executive control and the intentional constraint of thought. Specifically, resource control theory emphasizes the role of executive control in reducing mind wandering. Focus back effort training enhances this process by lowering the depletion of executive control resources, thereby minimizing the extent to which mind wandering consumes task-related resources. The dynamic framework model, on the other hand, highlights both automatic and intentional constraints on thought. Focus back effort represents intentional constraint, and training reduces the reliance on such deliberate control, making thoughts more likely to automatically return to the task at hand, ultimately decreasing mind wandering. The method of using focus back effort training to reduce the frequency of mind wandering is beneficial to make up for the resource consumption caused by the de-automation of mindfulness training. From the perspective of cognitive intervention, combined with brain imaging methods, this paper intends to conduct research in the following three aspects. Firstly, behavioral experiments and task-state magnetic resonance imaging data are used in the laboratory to explore the effectiveness of the intervention of focus back effort training on mind wandering and task performance and to reveal its cognitive neural mechanism. Secondly, the laboratory research was extended to life situations to investigate the effects of focus back effort training in life situation. Finally, the research explores the effect of focus back effort training on classroom mind wandering and academic performance of primary and middle school students.

    Resource control theory focuses on the irrelevance of task-unrelated content, while the dynamic framework model emphasizes the fluid transition between different mental contents. This study proposes that focus back effort embodies key elements of both theories: executive control from resource control theory and thought constraint from the dynamic framework model. Both executive control and thought constraint are considered higher-order structures of mind wandering. Investigating the effects of focus back effort training on mind wandering can help validate the hierarchical nature of these two theories. Furthermore, by employing multimodal research methods to effectively reduce the frequency of mind wandering across different contexts, this study not only provides robust empirical support for and extends these theoretical frameworks but also facilitates the integration of static and dynamic perspectives. Ultimately, this contributes to a more comprehensive understanding of the mechanisms underlying mind wandering.

    Beyond these contributions, this study presents several key innovations:

    First, it pioneers the intervention of mind wandering through psychological factors, a novel approach proposed by our research team. While previous studies have primarily focused on correlational findings, this study advances the field by developing a focus back effort training paradigm and conducting longitudinal intervention research to establish causal relationships, thereby extending prior work. Second, it integrates behavioral experiments with functional magnetic resonance imaging studies, conducting research both in controlled laboratory settings and ecologically valid real-life contexts. This dual approach ensures that the findings are not only scientifically rigorous but also broadly applicable, offering new insights for future research in mind wandering and consciousness. Finally, as mind wandering is a pervasive phenomenon in daily life, exploring its cognitive and neural mechanisms in real-world contexts provides valuable strategies for mitigating its negative impact on work and daily functioning. Moreover, applying focus back effort training in classrooms to reduce mind wandering among primary and secondary school students and enhance academic performance has significant educational implications.

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    Suicide risk assessment: A diagnostic perspective
    HOU Xiangqing, YANG Ying, ZHANG Qianqian, YANG Li
    Advances in Psychological Science    2025, 33 (3): 494-505.   DOI: 10.3724/SP.J.1042.2025.0494
    Abstract1375)   HTML123)    PDF (490KB)(3537)      

    Suicide risk assessment is fundamental to effective intervention, but its standardization and accuracy have been a major challenge for the field of suicide. Recent research on the relationship between suicide and mental disorders has found that suicide is not limited to the diagnostic of some specific mental disorders, but is a transdiagnostic clinical syndrome. As a result, the field of suicide has begun to explore the possibility of setting a suicide-specific diagnosis to improve suicide risk assessment. Diagnostic models, such as suicidal behavior disorder, suicidal crisis syndrome and acute suicidal affective disorder, have shown promising advances in empirical research and clinical application. However, these diagnostic approaches remain underdeveloped and carry potential risks in practical application. Future efforts should focus on refining and validating these models by clarifying conceptual definitions, improving diagnostic differentiation, enhancing research design, and developing assessment tools.

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    Voice and voice endorsement in the digital intelligence era: A media synchronicity perspective
    JIA Rongwen, FAN Wei, DUAN Minhui, LIU Sunyu, TANG Yipeng
    Advances in Psychological Science    2025, 33 (3): 381-401.   DOI: 10.3724/SP.J.1042.2025.0381
    Abstract1330)   HTML170)    PDF (722KB)(1529)      

    With the acceleration of global integration, organizations are confronted with rapidly changing external environments and intense competition. The importance of employees’ voices within organizations have become increasingly important. However, existing research predominantly focuses on voices in face- to-face communication rather than in virtual settings. In the intelligent digital era, employees are more inclined to utilize digital media, such as WeChat and video calls, to express their voices. Nonetheless, the impact of these media on voice expression remains largely unexplored. In order to resolve this important research question, this study relies on media synchronicity theory to assist the insufficient explanatory logic of traditional voice behavior theory. Initially, this study investigates how voicers select voice media in both face-to-face and various digital media contexts. Subsequently, it examines the impact of media choice on voice endorsement by analyzing the conveyance and convergence processes in the voice expression sequence. Ultimately, this study adopts static and dynamic perspectives to explore how the selection of multiple voice media influences final voice endorsement. By integrating these perspectives with theories from organizational behavior, media psychology, and communication disciplines, this study constructs a theoretical framework that elucidates how digital media influence voice generation and endorsement. It expands the scope and boundaries of research on media selection and voice behavior, offering guidance to enterprises on optimizing voice activities and enhancing the adoption rate of voice behaviors through innovative management concepts.

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    Unconscious fear and its neural mechanisms
    YU Lingfeng, ZHANG Jie, MING Xianchao, LEI Yi
    Advances in Psychological Science    2025, 33 (7): 1234-1245.   DOI: 10.3724/SP.J.1042.2025.1234
    Abstract1325)   HTML43)    PDF (726KB)(1733)      

    Unconscious fear refers to fear responses that occur without conscious awareness of fear-inducing stimuli. Traditional views suggest that unconscious fear processing primarily relies on the superior colliculus-pulvinar-amygdala pathway, with the amygdala serving as the central hub. However, recent studies on cortical and subcortical structures have significantly expanded this understanding. Research has revealed that beyond the amygdala, structures including the visual cortex, lateral geniculate nucleus (LGN), superior colliculus, and pulvinar can rapidly detect and specifically respond to unconscious fear stimuli, revealing a more complex neural processing network. While numerous studies have explored the neural basis of unconscious fear processing, a systematic integration and analysis of the roles and interactions of various brain regions during early visual processing stages remains lacking. Therefore, this paper focuses on examining the functions and interactions of visual cortical and subcortical regions (LGN, superior colliculus, pulvinar) during unconscious fear processing, aiming to construct a more comprehensive neural network model.

    The LGN's function extends far beyond its traditional role as a simple visual relay station. Research indicates that K cells in the LGN project to the visual cortex, forming the neural basis for blindsight. More importantly, the extensive connections between the LGN, thalamic reticular nucleus, and visual cortex enable selective filtering of visual information before conscious processing. Under threat conditions, signal transmission rates from the retina to LGN significantly increase, while the ventral LGN participates in modulating the duration of defensive responses to visual threats. Furthermore, studies on blindsight patients further confirm that the LGN can process threat signals bypassing V1, with functional connections between the LGN and visual cortex providing an alternative pathway for rapid behavioral responses, highlighting the LGN's importance as a key node in unconscious fear processing.

    The pulvinar's role in unconscious fear processing is more sophisticated than traditionally understood. While conventional views suggest that the pulvinar merely relays information directly to the amygdala, research has revealed more refined functional divisions, where the inferior pulvinar primarily connects with extrastriate visual areas and superior colliculus, while the medial pulvinar maintains bidirectional connections with the amygdala and frontoparietal regions. Notably, the medial pulvinar's approximately 200ms response latency suggests that it may participate in coordinating cortical assessment of stimulus significance before information reaches the amygdala, rather than simply relaying signals. This complex connectivity pattern establishes the pulvinar as a crucial coordinator in evaluating stimulus biological significance.

    The primary visual cortex demonstrates unique capabilities in unconscious threat processing. Through feedforward sweep mechanisms, V1 can rapidly process fear signals independently of feedback from higher visual areas. More significantly, V1 undergoes plasticity through fear learning, forming threat-related memory representations that facilitate rapid and precise processing under unconscious conditions. This plasticity is influenced by multiple regulatory mechanisms, including enhanced amygdala theta oscillations, increased acetylcholine release from the basal forebrain, and enhanced signal transmission between the amygdala and sensory cortex.

    Future research should focus on three key directions: First, employing multimodal MEG/EEG-fMRI imaging techniques combined with dynamic causal modeling and Granger causality analysis to investigate temporal characteristics and dynamic interactions between cortical and subcortical structures; Second, developing large-scale neural network computational models to simulate dynamic interactions between key brain regions and predict new neural circuit interaction patterns; Finally, exploring clinical translational applications, particularly developing novel treatment approaches based on unconscious exposure therapy and neurofeedback training, providing new therapeutic strategies for emotional regulation disorders and mental illnesses.

    In conclusion, this paper establishes that alongside the amygdala, the LGN, pulvinar, and primary visual cortex constitute critical neural nodes in unconscious fear processing. This not only reflects the diversity of visual processing mechanisms but also exemplifies the distinctive neural patterns that characterize human threat response systems.

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    A three-level meta-analysis of gender differences in spatial navigation ability
    XUE Xiaoran, CUI Wei, ZHANG Li
    Advances in Psychological Science    2025, 33 (5): 843-862.   DOI: 10.3724/SP.J.1042.2025.0843
    Abstract1316)   HTML33)    PDF (1536KB)(2677)      

    Spatial navigation refers to an individual's ability to update his or her position and orientation in space, learn the layout of new locations, and plan and follow routes to reach a destination. This ability is one of the fundamental abilities essential for the survival of both humans and animals. Gender is a significant factor contributing to individual differences in spatial navigation ability. Although numerous studies have explored gender differences in spatial navigation, findings regarding the existence and extent of these differences remain inconsistent. In these studies, gender often interacts with various factors, such as study design, collectively influencing spatial navigation ability. Therefore, it is essential to systematically investigate whether significant differences exist between men and women in spatial navigation ability and to analyze how moderating factors shape the relationship between gender and spatial navigation performance.

    The present study integrated 173 original papers involving 372 independent effect sizes and 26,604 subjects between 2007 and 2023 through a three-level meta-analysis to clarify gender differences in spatial navigation ability and their potential moderating variables. The results indicate that males outperform females in spatial navigation ability under most conditions and that this gender difference is significantly moderated by age, mode of representation, time constraints, task environment, test scenario, and assistive equipment. Specifically, age was a significant moderating variable: males significantly outperformed females in spatial navigation between the ages of 4 and 65 years, whereas there were no significant differences in infancy (0~4 years) and late adulthood (65 years and older). Representational modality in task design also played a role, with smaller gender differences in egocentric representational tasks and larger differences in allocentric representational tasks. Gender differences were more significant in time-constrained tasks and less so when there were no time constraints. Task environment and test conditions also significantly affected results, with smaller gender differences in indoor environments only, real-scene tests, or conditions using paper-and-pencil instruments and no assistive devices, and more significant differences in dual indoor-outdoor test conditions or conditions using assistive devices. In addition, the study found that geographic region did not influence gender differences in spatial navigation ability, as participants from various continents consistently exhibited significant gender differences. On the one hand, this may be due to the uneven focus on different regions in previous research, resulting in an imbalanced distribution of participants. On the other hand, economic conditions and living environments may serve as more proximal factors influencing gender differences, potentially moderating these differences by providing varying resources and challenges. Furthermore, gender differences in spatial navigation ability remained consistent across different task types and measurement metrics, indicating the reliability of these assessments. However, in real-life situations, spatial environmental cues are considerably more complex and multifaceted than what simplified tasks in laboratory settings can fully capture. Therefore, future research should continue to explore evaluation methods that are more closely aligned with real-world conditions.

    This study examined gender differences in spatial navigation ability and its moderators through a three-level meta-analysis. The findings not only confirmed the phenomenon of male superiority in spatial navigation ability but also identified several key variables that moderate these gender differences. Future research should involve more diverse and representative population samples and employ task designs and measurement methods that closely reflect real-world environments to further explore the relationship between gender and spatial navigation ability. Moreover, in practical terms, educators should prioritize the development of students' spatial navigation skills, particularly through targeted teaching and hands-on activities aimed at enhancing women's confidence and ability in handling navigation tasks. These efforts will not only contribute to narrowing the gender gap but also improve students' ability to tackle spatial challenges in real-world contexts, ultimately promoting educational equity and social progress.

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    Marketing effect of virtual influencers and its mechanisms in the context of AI technology
    LI Yan, CHEN Wei, WU Ruijuan
    Advances in Psychological Science    2025, 33 (8): 1425-1442.   DOI: 10.3724/SP.J.1042.2025.1425
    Abstract1293)   HTML75)    PDF (769KB)(925)      

    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.

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    Meta-analysis of the relationship between social exclusion and creativity
    ZHANG Tingyu, LIN Jiajia, SUN Cuicui
    Advances in Psychological Science    2025, 33 (4): 632-646.   DOI: 10.3724/SP.J.1042.2025.0632
    Abstract1290)   HTML138)    PDF (637KB)(1732)      

    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.

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    Neurophysiological mechanisms and interventions of subthreshold depression by integrating machine learning techniques
    LIU Yongjin, YANG Xue, DU Xinxin, JI Wenqi, ZANG Yinyin, GUAN Ruiyuan, SONG Sen, QIAN Mingyi, MU Wenting
    Advances in Psychological Science    2025, 33 (6): 887-904.   DOI: 10.3724/SP.J.1042.2025.0887
    Abstract1284)   HTML65)    PDF (1298KB)(3347)      

    Major Depressive Disorder (MDD) is a significant global public health threat for both national mental health, causing significant societal burden for both individuals and families. Subthreshold depression (StD) is an ultra-high-risk phase for MDD. Understanding its neurophysiological mechanisms and dynamic development patterns can help predict MDD onset and inform the development of novel and effective preventive interventions. To overcome the limitation of regarding depression as a static and single diagnosis, this study uses dynamical systems theory and machine learning techniques to explore the StD's neurophysiological mechanisms. By combining resting-state, task-state evaluations, and clinical interviews, it constructs a neurodynamic network model for StD with multi-modal data and machine learning, followed by cross-population and cross-individual validation.

    Firstly, using multi-modal data, Study 1 explores the differences in symptoms and neurophysiological mechanisms among individuals with StD, healthy, and MDD individuals from a population perspective, and then, identifies critical predictive neurophysiological indicators of StD. By combining resting-state and task-state evaluations, as well as clinical interviews, it explores the distinctiveness of symptoms and multi-modal data in StD individuals (versus healthy and MDD). And the neurodynamic network model for StD will be constructed using multi-modal data combined with machine learning techniques to analyze depressive symptoms and neurophysiological characteristics, followed by cross-population and cross-individual validation. at the individual level, Study 2 use ecological momentary assessment combined with follow-up tracking to investigate the relationship between the attractor state of subthreshold depression and subsequent transition to MDD, along with accompanying neurophysiological changes. A dynamic development prediction model will be constructed. This study utilizes longitudinal tracking of multi-modal data combined with machine learning techniques to measure the attractor of StD and explore its predictive ability for subsequent depressive states and neurophysiological characteristic changes. Finally, to overcome the limitations of evaluating the preventive intervention effects for StD mainly relied on clinical diagnostic results, this study explores the effectiveness of cognitive behavioral therapy (CBT) in preventing the progression of StD from a commonality perspective, with the depressive symptoms and neurophysiological characteristics as objective evaluation indicators of intervention effectiveness. Furthermore, it elucidates the predictive role of the individual's attractor state in the future transformation of subthreshold depression. Study 3 uses a randomized controlled trial to investigate the preventive intervention efficacy of CBT group (versus waiting list group) on subthreshold depression and the mediating role of the attractor state. Based on the above study, this study proposes a predictive hypothesis regarding the preventive effect of cognitive behavioral therapy (CBT) on subthreshold depression based on the dynamical systems theory. It is hypothesized that CBT can reduce depressive symptoms and the transition rate by enhancing the stability of the attractor state, and this effect can be measured by neurophysiology characteristics.

    In conclusion, this study offers a dynamic-systems-theory-driven approach, integrating multi-modal machine learning techniques to analyze the neurophysiological uniqueness of StD. This study helps develop more precise models for predicting StD and evaluating preventive intervention effects based on neurophysiological characteristics obtained from various sensors. These results can be used for daily physiological assessments, early detection of emotional or mental states earlier deterioration, and development of more precise early diagnosis and prevention strategies for MDD.

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