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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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    Conceptual Framework
    Voice and voice endorsement in the digital intelligence era: A media synchronicity perspective
    JIA Rongwen, FAN Wei, DUAN Minhui, LIU Sunyu, TANG Yipeng
    2025, 33 (3):  381-401.  doi: 10.3724/SP.J.1042.2025.0381
    Abstract ( 112 )   PDF (722KB) ( 185 )   Peer Review Comments
    With the acceleration of global integration, organizations face rapidly changing external environments and intense competition. The significance of employees' voices within organizations has increased. However, existing research predominantly focuses on face-to-face communication, neglecting virtual interactions. In the intelligent digital era, employees are progressively turning to digital media, such as WeChat and video calls, to express their voices. However, the impact of these media on voice expression remains largely unexplored. To address this gap, this study applies media synchronicity theory to enhance the understanding provided by traditional voice behavior theory. It thoroughly explores the influence mechanism of digital media on voice generation and endorsement, and constructs a corresponding theoretical analysis framework.
    First, this study merges media synchronicity theory with the voice behavior process by incorporating the media selection process into voice research. This integration constructs a theoretical framework linking voice intention to media choice. The core premise of media selection suggests that individuals match media characteristics with task objectives, making decisions based on a cost-benefit analysis. Based on media synchronicity theory, this study evaluates media characteristics in terms of synchronicity, considering factors such as transmission velocity, parallelism, and symbol sets. It distinguishes voice tasks into two primary processes: conveyance and convergence. The study posits that low-synchronicity media are preferable when conveyance needs are high, while high-synchronicity media are more effective when convergence needs are paramount. Additionally, it assesses contextual factors (e.g., interpersonal and topic familiarity) and individual traits (e.g., impulsive personality) that influence the relative importance of conveyance and convergence needs, thereby affecting media choice in both rational and non-rational manners.
    Second, this study explores the effects of media selection, along with subjective and objective factors, on voice endorsement, focusing on the conveyance and convergence processes within the voice behavior framework. Drawing on media synchronicity theory, it suggests that the impact of voice media on voice endorsement is mediated by these two processes. In the conveyance process, low-synchronicity media provide recipients with ample time to process the voice content, reducing discomfort and enhancing the likelihood of adoption. In the convergence process, high-synchronicity media facilitate deeper interaction and negotiation, thereby increasing adoption probability. Additionally, the complexity and contextual adaptability of the voice process indicate that the impact of media choice on voice endorsement is influenced by both subjective and objective factors. Objectively, the familiarity between the parties or with the voice issue determines the relative importance of conveyance and convergence, with suitable media choices in specific contexts enhancing voice endorsement. Subjectively, individuals with strong self-enhancement motivation might adopt avoidance strategies, which could undermine the benefits of conveyance and convergence processes, negatively impacting voice endorsement.
    Third, adopting a dynamic perspective, this study conceptualizes voice as a long-term event and examines how repeated mentions of voice across different times, members, and media influence voice endorsement. Initially, the study suggests that multiple media, compared to single media, are more conducive to voice endorsement. It highlights the strengths and weaknesses of low- and high-synchronicity media during conveyance and convergence processes, emphasizing that the integration of multiple media better addresses these needs throughout the voice process. Subsequently, this study examines the boundary conditions influencing the effectiveness of multiple media, focusing on the roles of synchronicity differences and timing in enhancing voice endorsement. It posits that the benefits of multiple media are not static but vary according to the alignment of media synchronicity, timing, and the development of the voice event. For instance, in the early stages of voice, media with low synchronicity afford recipients more time and space to contemplate voice content. In contrast, during the middle stages, as familiarity with the voice context grows, media with high synchronicity are more effective in facilitating convergence and further negotiation. Thus, for voice agents, the selection of suitable media and the strategic timing of their use are crucial for maximizing voice endorsement.
    This study introduces a comprehensive theoretical framework to analyze the impact of digital media on voice generation and endorsement. It extends the scope of research on media selection and voice behavior, offering guidance for enterprises to refine voice activities and enhance the rate of voice endorsement based on innovative management concepts.
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    The formation mechanism of spatial stigma from the mobility perspective
    LI Yaoqi, ZENG Xinying
    2025, 33 (3):  402-410.  doi: 10.3724/SP.J.1042.2025.0402
    Abstract ( 94 )   PDF (529KB) ( 147 )   Peer Review Comments
    Spatial stigma is a negative narrative tied to a place and related to its reputation, reflecting the negative stereotypes that society holds about it. It is not uncommon for places to be stigmatized, which seriously damages the image of the place as well as the identity and psychological health of the relevant groups. Existing studies usually view spatial stigma as an established social fact and local meaning, emphasizing only the existence of spatial stigma. This research orientation, which focuses on the result but not the process, has led the studies to concentrate on the sociocultural impacts of spatial stigma and tend to develop explanations from some macro-narrative logic. Although it helps to stimulate scholars' attention to this topic to some extent, it is difficult to classify the potential types of spatial stigma and explain the reasons for its formation, so the formation mechanism of spatial stigma remains unknown.
    Mobility is an important context for the formation of spatial stigma and a catalyst for exacerbating the extent and impact of spatial stigma, providing a dynamic, processual, and relational research perspective for discussing the formation mechanism of spatial stigma. From the perspective of mobility, local conflictual events trigger moral inferences and evaluations from people to places, which is the source of spatial stigma; the dissemination and dramatization of conflictual events by the media promotes the flow of negative information, which is the reason for the exacerbation of spatial stigma. Of course, not all local events lead to the same spatial stigma, and not all places tied to conflictual events are assigned the same degree of spatial stigma. Therefore, this paper categorizes local conflictual events into three types, namely, social moral conflict, social security conflict, and social environmental conflict, and proposes a research framework for the formation mechanism of spatial stigma under the mobility perspective. Specifically, this paper introduces the mediating mechanism of local stereotypes and the boundary conditions of local characteristics (e.g., local economic level, local diversity) and media reports (e.g., media type, narrative style), and argues that local conflictual events will further lead to spatial stigma by affecting stereotypes of places, while local characteristics and media reports will weaken or strengthen the effect.
    Migration flows information about local conflictual events to various places, triggering the public imagination of the place and its related elements, and labeling the place with relevant negative stereotypes. These negative local stereotypes affect people's perceptions, emotions, and behaviors, and can easily develop into stigmas if not effectively intervened. In addition, mobility exacerbates regional differences in levels of economic development and cultural diversity, which can also become entrenched in social norms that affect public acceptance of places and local conflicts. People are more tolerant of regions with higher levels of economic development and greater diversity, because they consider conflicts to be understandable in these regions. Finally, the media use multiple channels to facilitate the flow and dissemination of information, but the public has differentiated trust in different types of media and different preferences for different forms of reporting. We suggest that the official media is more capable of calming negative public emotions than the self-media due to the credibility of the government; while objective reports can make objective judgments based on facts, and are more capable of resolving social emotions and social conflicts than story reports.
    This study breaks the previous research orientation of spatial stigma, which emphasizes the results rather than the process, by classifying the specific types of place-bound conflict events and focusing on the mechanisms and boundary conditions of these local conflictual events that lead to spatial stigma. This study provides a new theoretical perspective to analyze the causes of spatial stigma and points out the direction for empirical research on spatial stigma.
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    Meta-Analysis
    A meta-analysis of the influence of work connectivity behavior after-hours on employees: The mediating role of work autonomy and psychological detachment
    LI Hu, LI Jianguo, ZENG Lei, SU Lingjie
    2025, 33 (3):  411-424.  doi: 10.3724/SP.J.1042.2025.0411
    Abstract ( 106 )   PDF (670KB) ( 169 )   Peer Review Comments
    With the development of modern communication technology, Work Connectivity Behavior After-hours (WCBA) has become increasingly common, which has led to complex impacts. This paper examines the comprehensive effects of WCBA from a work-family perspective, based on its cross-domain conceptual attributes. From the perspective of the Conservation of Resources theory, the study explores the differential impacts of WCBA on the work and family domains. Work autonomy (representing resource gain) and psychological detachment (representing resource loss) are introduced as mediating variables, with the relative strengths of these mediating effects compared across the work and family domains. The aim is to determine whether WCBA results in greater "resource gain" or "resource loss" in different areas. Given that previous research has shown work-family balance and task performance to be the best indicators of employee performance in the family and work domains, respectively, this paper adopts these metrics as the core dependent variables to assess the impact of WCBA in each context.
    Specifically, this study includes 71 documents, with a total of 283 effect sizes, after searching, screening, manual supplementation, and exclusion for meta-analysis. The findings reveal the following: (1) WCBA exerts a positive impact on work-family balance and task performance through work autonomy, and a negative impact through psychological detachment; (2) The positive mediating effect of work autonomy on the relationship between WCBA and task performance is stronger than the negative mediating effect of psychological detachment; compared to the positive mediating role of work autonomy, the negative mediating role of psychological detachment between WCBA and work-family balance is stronger.
    The findings of this paper contribute significantly to understanding the differential impacts of WCBA in the work and family domains. First, this study updates and enriches the research on work-family boundaries, filling the gap in meta-analytic research in this area. Secondly, this study clarifies the mediating mechanisms through which WCBA exerts its effects, elucidating the differential roles of different mediating variables in explaining the effects of WCBA. It is particularly worth noting that no studies have yet explored the mediating mechanism of work autonomy in the relationship between WCBA and work-family balance. This study tests the validity of this pathway through meta-analysis, thereby revealing a potential positive impact mechanism between WCBA and work-family balance. Finally, the study compares the differential impacts of WCBA across these domains through meta-analysis, demonstrating that WCBA generates greater "net gain" and a positive impact in the work domain, while causing a "net loss" and a negative impact in the family domain. These findings not only expand the theoretical framework of WCBA but also provide references and guidance for the study of similar cross-domain variables.
    In the context of increasing digitalization and boundarylessness of the workplace, it is impractical to completely eliminate WCBA. Therefore, organizations and leaders leverage its positive effects to the positive effects and mitigate potential harm. For organizations and managers, understanding the mechanisms through which WCBA exerts its influence can inform the development of effective management strategies to enhance employee job satisfaction and organizational performance. Such strategies may include: (1) defining the objectives and expectations of WCBA; (2) preventing overwork and information overload; (3) enhancing employees' sense of work autonomy; (4) establishing clear boundaries for work hours; (5) encouraging employees to disengage from work during non-working hours; (6) preventing the encroachment of work on personal life; and (7) supporting employees in psychological detachment. For policymakers, the findings of this study provide theoretical support for the improvement of work-hour regulations. Policymakers can guide enterprises to reasonably arrange employees' working hours and protect their legal rights and interests. These efforts contribute to a positive employment environment, as well as social harmony and stability.
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    Regular Articles
    How does action influence metacognition? — An exploration based on cognitive models and neural mechanisms
    CHENG Xiaorong, QIU Shiming, DING Xianfeng, FAN Zhao
    2025, 33 (3):  425-438.  doi: 10.3724/SP.J.1042.2025.0425
    Abstract ( 132 )   PDF (586KB) ( 136 )   Peer Review Comments
    Action and metacognition are essential components of cognitive processing. Metacognition reflects an individual's ability to represent, monitor, and regulate cognitive processes, and can be measured through confidence ratings. Actions, on the other hand, serve as critical means for outputting internal cognitive processing, particularly decision-making information. Recent research has shown that various aspects of action—such as response speed, intensity, sequence, conflict, and observation—can influence metacognition. However, the mechanisms through which action influences metacognition remain unclear, especially in terms of the dependency, relationship, and organizational structure between the information used for metacognitive evaluation (i.e., metacognitive evidence) and that used for perceptual decision-making (i.e., perceptual evidence) during this process.
    From the perspective of cognitive models, post-decision models of metacognition—such as the hierarchical model, the metacognitive Bayesian model, and the Signal Detection Theory (SDT)-based generative model—offer a compelling framework for explaining the empirical findings regarding the influence of action on metacognition. These models propose that the sensory-motor information provided by action impacts metacognition after, rather than before, perceptual decision-making. Even after the perceptual decision has been made, metacognitive evidence continues to accumulate dynamically until the metacognitive evaluation is completed. Notably, while all post-decision models assert that metacognitive evidence differs from perceptual evidence, they vary in how the two types of evidence are related.
    First, the hierarchical model suggests that perceptual evidence and metacognitive evidence may originate from the same source but differ in quality. More specifically, the processing approach is hierarchical—different types of processing for the same evidence are organized hierarchically, with new processing introducing additional noise (e.g., action-related information), which alters the hierarchical structure of the evidence. Additionally, the processing approach is sequential—early processing results in an objective decision, while later processing inherits the evidence from early stage, evaluates it, and generates a subjective decision. Second, the metacognitive Bayesian model proposes that perceptual decision-making and metacognitive elevation have distinct yet coupled sources of evidence. According to this model, action responses reflect an individual's internal state, which, together with other confidence-related evidence, contributes to the formation of metacognitive elevation. Third, within the framework of the SDT-based generative model, metacognitive elevation is influenced by a trade-off between confidence noise and confidence boost. Confidence noise represents inefficiencies in the metacognitive elevation, which reduces confidence sensitivity. Confidence boost refers to post-perception information (e.g., action information) that is not used in the perceptual decision-making but is utilized in metacognitive elevation. The trade-off between these two factors determines the level of confidence: when the confidence boost is high and noise is low, confidence is high; conversely, when the boost is low and noise is high, confidence is low. From the perspective of neural mechanisms, the ventral stream and dorsal stream are responsible for visual perception (e.g., object feature recognition) and visually guided motor responses (e.g., object grasping), respectively. Notably, since both streams are connected to the prefrontal cortex, perceptual information from the ventral stream and action information from the dorsal stream may be integrated and processed there, forming metacognitive elevation. This integration provides a neural basis for the influence of action on metacognition. More specifically, perceptual and action information may be integrated through brain networks centered on the prefrontal cortex, relying on electrophysiological mechanisms such as β oscillations and α inhibition, with metacognition shaped by attentional regulation.
    While the cognitive models and neural mechanisms summarized above provide valuable insights into how action influences metacognition, further research could deepen our understanding of this influence by exploring the following directions. First, exploring the boundary conditions under which action influences metacognition. Some studies have shown that actions do not necessarily affect metacognition in all situations, suggesting that influence of action on metacognition may be subject to specific conditions that warrant further exploration. Second, clarifying the true psychological meaning reflected by confidence. One perspective suggests that perceptual confidence reflects the likelihood that a decision is correct, while another argues that confidence reflects the amount of evidence supporting a decision, with relatively less sensitivity to the evidence against it. Distinguishing between these two perspectives can help us better understand which aspect of metacognition is altered by action. Finally, exploring metacognitive performance in individuals with movement disorders, such as those with autism or paraplegic spinal cord injuries. Comparing metacognitive performance in the presence and absence of atypical actions or movement disorders can help identify the necessary conditions and specific mechanisms through which action impacts metacognition.
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    The mechanisms and functions of inter-brain synchronization
    SHU Xindi, LIU Hanyin, WANG Jin, LIU Zhiyuan, LIU Lanfang
    2025, 33 (3):  439-451.  doi: 10.3724/SP.J.1042.2025.0439
    Abstract ( 145 )   PDF (505KB) ( 148 )   Peer Review Comments
    Inter-brain synchronization (IBS) represents a neural phenomenon wherein the brain activities of individuals become correlated during social interactions. This paper aims to provide a comprehensive overview of IBS by examining its definition, measurement methods, driving mechanisms, influencing factors, and functional implications within social interactions.
    Definition and Measurement of IBS
    Inter-brain synchronization (IBS) refers to the correlated or synchronized neural activities between individuals during interaction. While some researchers propose a stricter definition, suggesting that only neural activities with direct causal behavioral impacts should be classified as IBS, the broader definition is more commonly adopted. Moreover, IBS can be measured using several methods, akin to those used for analyzing intra-brain connectivity. These methods are categorized based on the type of information they use (time-domain vs. frequency-domain) and the directionality of information flow (directed vs. undirected connectivity). We categorize these into five main types of metrics: inter-subject correlation (ISC), regression analysis, coherence analysis, phase synchrony, and causality analysis. These diverse methods enable researchers to understand the various dimensions of IBS, offering insights into its underlying mechanisms and functional significance in social interactions.
    Mechanisms of IBS
    IBS can occur even in the absence of direct information exchange between individuals, driven by shared sensory stimuli, joint motor activities, or collective attention and arousal. Conversely, when direct information exchange is present, IBS is often explained by two theoretical frameworks: co-representation and mutual prediction. Co-representation suggests that individuals create shared mental representations of stimuli and intentions during interactions. Mutual prediction extends predictive coding to social contexts, where individuals continuously predict each other's behaviors, resulting in neural synchronization when predictions align. Together, these mechanisms enable adaptive social interactions through synchronized neural activities.
    The occurrence of IBS involves several brain regions, including the prefrontal cortex, parietal lobes, and temporal regions. These regions can be categorized into three systems based on their functions: the mirror neuron system, the mentalizing system, and the mutual attention-synchronization-reward loop. The mirror neuron system is engaged during action observation and execution, facilitating motor imitation and coordination. The mentalizing system is responsible for inferring others' intentions and involves areas like the medial prefrontal cortex, temporo-parietal junction (TPJ), and precuneus. This system is particularly active when individuals engage in higher-level social cognition. The mutual attention-synchronization-reward loop hypothesizes that IBS promotes social alignment by enhancing attention and triggering the reward system. This system may help explain why individuals are more likely to synchronize with those they have a stronger social connection with.
    Factors Influencing IBS
    IBS is influenced by multiple factors, including interaction type, task context, interpersonal relationships, and individual characteristics. Different interaction types induce IBS in various brain regions and to varying degrees. The intensity of interaction also plays a role; face-to-face communication tends to produce stronger IBS than indirect interactions. Contexts like cooperation and competition further impact IBS. Interpersonal relationships significantly affect IBS, possibly due to emotional bonds and shared experiences. In-group members also tend to exhibit stronger IBS than out-group members. Individual traits, including personality, empathy, gender, and emotional states, modulate IBS as well.
    Potential Functional Significance of IBS
    IBS is not merely a byproduct of individuals receiving similar external stimuli; it also plays a functional role in social interactions. One of the primary functions of IBS is facilitating interpersonal motor coordination. Studies using transcranial electrical stimulation (tES) to enhance IBS have shown that increased neural synchronization in the prefrontal cortex and right inferior frontal gyrus correlates with improved cooperative motor behavior. This finding provides causal evidence that enhancing IBS can promote more effective motor coordination between individuals.
    Another key function of IBS is enhancing verbal communication. Successful language exchange relies on the alignment of representations between interlocutors, including phonetic, semantic, and syntactic features. IBS observed during both one-way and two-way verbal exchanges indicates that neural coupling in language-processing regions correlates with better mutual understanding.
    Finally, IBS may play a critical role in fostering social bonds and promoting prosocial behavior. Neurofeedback training aimed at increasing IBS has been shown to strengthen prosocial tendencies, indicating that neural synchronization contributes to social bonding processes. The mutual attention-synchronization-reward loop suggests that IBS activates the reward system, reinforcing positive social interactions and motivating future social engagement.
    Conclusion
    IBS is a multifaceted phenomenon that plays a crucial role in social interactions by promoting motor coordination, enhancing communication, and fostering social bonds. Future research should aim to standardize measurement methods, explore the relationship between co-representation and mutual prediction, investigate the dynamics of inter-brain desynchronization, and further develop the concept of inter-brain plasticity. Understanding these aspects will not only provide deeper insights into the neural basis of social interactions but also pave the way for practical applications in areas such as relationship assessment, communication training, and interventions for social dysfunction.
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    Brain-to-brain synchronyduring teacher-student interactions and its regulatory factors in teaching interaction
    GONG Fangying, SUN Yifan, HE Qin, SHI Ke, LIU Wei, CHEN Ning
    2025, 33 (3):  452-464.  doi: 10.3724/SP.J.1042.2025.0452
    Abstract ( 93 )   PDF (504KB) ( 122 )   Peer Review Comments
    Teaching interaction is a core psychosocial process in classroom teaching and learning. Given the importance of teaching interaction in teaching and development, issues related to the quality, strategies and functions of teaching interaction have received considerable attention from researchers. In recent years, with the development of cognitive neuroscience and the advancement of neuroimaging technology, it has become possible to study the neural mechanisms of teaching interaction from an interbrain neuroscience perspective. This paper focuses on three specific forms of teaching interaction: verbal, non-verbal and mixed interactions. It is found that the synergy of metacognitive processing, theory of mind, working memory, language processing and emotion and other related brain areas form the neural basis of teaching interaction.Interpersonal Brain Synchronization (IBS) is significant as a neural marker in identifying effective teaching interactions and is moderated by multiple factors such as teaching ability, teaching strategies, knowledge and experience, and emotions.
    Through fMRI, ERP and other methods, researchers have found that interpersonal interaction behaviors elicit activation in brain regions including prefrontal, anterior cingulate gyrus, orbitofrontal cortex, and temporoparietal cortex. As a specific and complex interpersonal interaction behavior, does instructional interaction also trigger the activation of the above brain regions? Comparing the three specific forms of instructional interactions, verbal, nonverbal, and mixed interactions, the neural mechanisms common by instructional interactions are mainly concentrated in the frontal and temporoparietal common areas. This suggests that instructional interactions are based on joint attention and mutual anticipation, and that the brain's social-cognitive networks are jointly involved in understanding the intentions and affective states of others. Early input of information in instructional interactions activates lower-order visual-sensory areas, while higher-order brain areas play a crucial role in integrating new knowledge and understanding the purpose of others over time and with the input of information. In addition, based on the different forms and combinations of information transfer during teacher-learner interactions, we propose that teacher-student teaching interactions may be a “drive-interaction synchronization” process from the perspective of interpersonal brain synchronization. In the initial stage of teaching, the knowledge-oriented teaching steadily drives the learners' brain neurological processes, and eventually synchronizes them with the teacher's brain.In the in-depth stage of teaching, the learners actively participate in the construction of knowledge, and the teaching achieves consistency in the motor, affective, and cognitive dimensions, and the teacher's dominant identity is weakened by the coordination of behaviors and mutual understanding, so that the teacher's brain network and the learners' brain network interact with each other and interact with each other, and eventually reach a synchronous equilibrium state. The teacher's and learner's brain networks interact and influence each other, eventually reaching a state of synchronised equilibrium. The synchronization of teaching and learning interactions can either be maintained at the drive synchronization or transformed to the interaction synchronization. When teachers and students reach a synchronized and balanced state, the transfer of knowledge or skills will be more flexible and adaptable, and will be more conducive to equal dialogues and joint learning between teachers and students.
    Teaching interaction involves interpersonal brain-to-brain synchronization of lower-order and higher-order brain regions, as well as interpersonal brain-to-brain synchronization of homologous and heterologous brain regions between teachers and students. In order to further deepen the understanding of the process of instructional interaction and provide more evidence from cognitive-neural aspects for behavioral observation-based theories of instructional interaction, future research directions should focus on refining the common and specific neural mechanisms of the three types of teaching interactions: verbal, nonverbal, and mixed interactions, further examining other moderators of IBS, and highlighting the ecological validity of instructional research in terms of research design and methodological expansion in order to accurately reflect the realities of classroom practice and conduct a more in-depth exploration of teacher-student interactions in teaching scenarios.
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    Is BDNF an underlying biological mechanism in exercise-induced cognition? Evidence, challenges, and prospects
    GUO Yi, ZHANG Lian-cheng, TAO Ying-ying, ZHU Liang-hao, WANG Ting
    2025, 33 (3):  465-476.  doi: 10.3724/SP.J.1042.2025.0465
    Abstract ( 85 )   PDF (507KB) ( 97 )   Peer Review Comments
    Exercise increases the body’s levels of brain-derived neurotrophic factor (BDNF), which is strongly associated with cognitive performance. This raises the question: Is BDNF a biological mechanism through which exercise enhances cognition? Biological inferences from animal models and human studies suggest that exercise can directly and indirectly elevate BDNF levels in the brain through various mechanisms. In turn, BDNF is believed to improve higher cognitive functions, such as learning and memory, through mechanisms like enhancing neuroplasticity and inducing long-term potentiation. Therefore, it is reasonable to hypothesize that BDNF is a biological mechanism by which exercise promotes cognition. This hypothesis has been supported by some empirical studies, although other studies have yielded inconsistent results.
    Upon reviewing the existing literature, it was concluded that several conditions must be met to confirm the mediating role of BDNF in the process through which exercise enhances cognition. First, exercise must consistently elevate BDNF levels in the organism; second, the increase in BDNF levels following exercise should correspond to improvements in cognitive performance. However, these conditions have not always been met in human studies. In some cases, BDNF levels did not rise as expected after exercise; in others, BDNF increased without corresponding improvements in cognitive performance. These results challenge the assumed association between exercise, BDNF, and cognition, as well as the theoretical assumption that BDNF serves as a biological mechanism for exercise-induced cognitive enhancement. Potential explanations for these discrepancies include factors such as a sedentary lifestyle, stress, cognitive disorders, or age-related brain degeneration, which may reduce the impact of exercise on peripheral BDNF levels in certain populations. Additionally, some forms of exercise may not significantly stimulate BDNF release, and exercise interventions that do not meet a certain threshold may fail to elevate BDNF levels. Furthermore, the effect of exercise on BDNF levels may be unstable. These findings suggest that if other factors influence BDNF levels more strongly than exercise itself, the credibility of BDNF as a biological mechanism for exercise-enhanced cognition could be compromised. Moreover, due to the multitude of factors influencing BDNF levels in humans—such as the variety of exercise interventions, measures of BDNF levels, dimensions of cognitive function, and heterogeneous subject populations—the comparability and validity of results across studies are often limited. Current studies also face several limitations, including flaws in study design and insufficient control over additional variables (e.g., subject selection, exercise protocol design, cognitive function tests, and BDNF measurement methods). For instance, while the cognitive-enhancing effect of BDNF is triggered by intracerebral BDNF, current technology cannot directly measure brain BDNF levels in human subjects. Most studies rely on peripheral blood samples (e.g., from the fingertip, brachial artery, or jugular vein), and some have even used saliva or skeletal muscle tissue samples. However, BDNF levels measured in serum and plasma from the same blood collection site can differ significantly, leading to discrepancies between peripheral BDNF levels and actual brain BDNF content, further reducing the accuracy and comparability of findings. Notably, previous studies have indirectly inferred their conclusions without thoroughly investigating the time course of BDNF and cognitive performance changes in response to exercise. The process and quantitative relationship between BDNF levels and cognitive performance in response to exercise have not been well-explored, and the differential effects of exercise and BDNF on various dimensions of cognitive function remain unclear. These gaps contribute to ongoing controversy and hinder the development of theoretical models and clinical applications related to BDNF.
    To address these issues, future research should focus on clarifying key themes. First, rigorous mediation experiments should be designed to strictly control for additional variables, such as exercise protocols, subject populations, and methods of measurement. These experiments should aim to refine potential moderating effect tests, explore the quantitative relationship between exercise, BDNF, and cognition, and identify the mechanisms by which exercise influences different cognitive functions through BDNF. Second, meta-analyses examining the mediation effects of BDNF in the exercise-cognition relationship should be conducted to assess the moderating effects of various variables. By empirically testing and analyzing secondary data, the mediating role of BDNF in exercise-induced cognitive enhancement can be validated, thereby enriching our understanding of the biological mechanisms linking exercise and cognition. These findings would provide valuable theoretical guidance and practical implications for the study of exercise-induced cognitive benefits, offer new perspectives for exercise practice, and contribute to public health promotion.
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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
    2025, 33 (3):  477-493.  doi: 10.3724/SP.J.1042.2025.0477
    Abstract ( 143 )   PDF (2575KB) ( 169 )   Peer Review Comments
    The imbalance between approach and avoidance is evident in a range of psychiatric disorders, including anxiety. Experimental models of this imbalance are replacing traditional avoidance paradigms as valid tools for evaluating and predicting anxiety. Previous studies have mainly interpreted approach-avoidance conflict from the motivational perspective of seeking rewards and avoiding harm, simplifying individual responses to independent approach or avoidance motivations. However, anxious individuals tend to choose avoidance behaviors even when threats are weak and rewards are strong, making quick decisions with less information. This indicates that dysregulation of approach-avoidance conflict in anxious individuals may arise not only from increased avoidance motivation but also from cognitive processes such as expected value computation or habituation. Traditional theoretical frameworks are insufficient to explain these specific mechanisms. Therefore, we propose a three-stage model of “conflict perception, conflict processing, and feedback learning” to integrate anxious individuals' cognitive and neural changes during approach-avoidance conflicts and provide a more comprehensive explanatory framework for their abnormal behaviors.
    First, we summarize the theoretical foundations of the approach-avoidance conflict system, including the Reinforcement Sensitivity Theory focusing on approach and avoidance motivations, the avoidance learning theory based on Pavlovian conditioning, and the reinforcement learning theory emphasizing expected value and prediction error. These theories explain the dysregulation of approach-avoidance conflict in anxious individuals from different perspectives but lack an integrated view. Therefore, we propose a dynamic three-stage model that emphasizes expected value, motivation, and their interaction during conflict processing, and individuals can subsequently update or reinforce their behavioral strategies through feedback learning.
    Next, we summarize the behavioral and cognitive neuroscience evidence supporting this model, emphasizing the intrinsic links between cognitive processes and neural mechanisms. In the conflict processing stage, the hippocampus and prefrontal cortex integrate information, encode expectations, and compare approach-avoidance conflicts to make decisions. In the feedback learning stage, the goal-directed and habit systems within cortico-basal ganglia circuits compete, influencing individuals’ behavioral strategy updates and adaptability.
    Finally, we apply the three-stage model to explain the dysregulation of approach-avoidance conflict in anxious individuals. We point out that they exhibit heightened threat perception, imbalanced comparison of expected value and motivation, and abnormal feedback learning during conflict resolution. The brain regions involved in anxiety overlap with those used in resolving approach-avoidance conflicts, primarily exhibiting enhanced amygdala activity and impaired prefrontal and hippocampal functions. This leads to increased threat attention, expectation imbalance, and inflexibility in learning system transitions, which may be core reasons for dysregulated approach-avoidance conflicts.
    To further understand the specific mechanisms of approach-avoidance conflict dysregulation in anxious individuals, future research may consider: (a) Researchers should further validate the three-stage model by designing studies that avoid confounding different cognitive mechanisms. This will help demonstrate the relative independence of each stage and assess the causal relationship between conflict dysregulation and anxiety. (b)Integrating theories and methods from different fields can deepen cross-disciplinary exploration. Computational psychiatry methods can be employed to parameterize the three-stage approach-avoidance conflict model, providing quantitative explanations and predictions. (c) Adopting a developmental perspective is essential for understanding how approach-avoidance conflict dysregulation arises and evolves over time in anxious individuals. Combining multimodal and longitudinal tracking methods can provide valuable insights into the developmental trajectory of abnormal behaviors in anxious individuals and inform early identification and intervention strategies for anxiety.
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    Suicide risk assessment: A diagnostic perspective
    HOU Xiangqing, YANG Ying, ZHANG Qianqian, YANG Li
    2025, 33 (3):  494-505.  doi: 10.3724/SP.J.1042.2025.0494
    Abstract ( 93 )   PDF (490KB) ( 207 )   Peer Review Comments
    Suicide risk assessment is fundamental to effective intervention, but its standardization and accuracy have been a major challenge for the field of suicide. To address these issues, researchers have advocated for suicide-specific diagnosis within mental disorders classification systems. This paper provides a critical review of the suicide-specific diagnosis from four aspects: background, existing protocols and their research advances, current controversies, and future directions.
    First, the proposal for a suicide-specific diagnosis arises from two key issues. One is that the way suicide is conceptualized in the current psychiatric diagnostic framework affects the accuracy of suicide risk assessment. Traditionally, suicide has been treated as a symptom or consequence of other mental disorders, such as major depressive disorder, bipolar disorder, or borderline personality disorder. However, this approach overlooks individuals who exhibit suicidal behaviors but do not meet the diagnostic criteria for these conditions. Another is the growing evidence suggests that suicide is not merely a byproduct of other mental disorders but a transdiagnostic phenomenon. It is underpinned by independent psychological and physiological mechanisms. Research has highlighted the role of unique biological markers (e.g., serotonin dysregulation) and acute psychological states (e.g., cognitive rigidity, emotional dysregulation, and entrapment) that are specific to suicidal behaviors. These findings also suggest that suicide requires a distinct diagnostic framework.
    Second, this paper critically analyses existing suicide-specific diagnostic models (i.e., suicidal behavior disorder, suicide crisis syndrome, and acute suicidal affective disturbance) using the Feighner criteria, a standard for establishing mental disorder diagnoses. According to these criteria, a valid diagnosis must have clinical description, demonstrate distinctiveness from other disorders, and be supported by laboratory, follow-up and family studies. The analysis revealed that while the proposed models had made progress, none of them were fully meeting those criteria. Specifically, they are hindered by three key deficiencies: an unclear description of clinical features and the relationships between symptoms, inadequate accounting for the variability in symptom combinations, and ambiguous distinctions between suicide-specific symptoms and those of other mental disorders.
    Then, this paper highlights the main points of current debate about suicide-specific diagnosis, emphasizing its potential benefits and risks. One major concern is the problem of false-negative predictions, as suicide-specific diagnoses may fail to account for impulsive or rare suicide attempts. Critics argue that focusing narrowly on specific symptoms could lead to underestimating risk, particularly in individuals who do not fit diagnostic criteria. However, proponents suggest that by emphasizing acute and proximal risk factors—such as rapid increases in suicidal intent or emotional turmoil—these frameworks could significantly improve the prediction of imminent suicide risk. Another contentious issue is the potential for stigmatization and labeling. A suicide-specific diagnosis might reinforce societal stigma, creating barriers to help-seeking and reducing individuals’ willingness to disclose suicidal thoughts. Conversely, advocates argue that a clear suicide-specific diagnosis could normalize discussions around suicide, reducing misconceptions and encouraging individuals to seek timely treatment. By framing suicide as a clinical condition with identifiable features and targeted interventions, stigma might be alleviated rather than exacerbated. A further critique is that a suicide-specific diagnosis risks oversimplifying the multifaceted nature of suicide, which encompasses biological, psychological, social, and cultural dimensions. Treating suicide solely as a mental disorder could lead to excessive medicalization while neglecting broader sociocultural and economic determinants. However, supporters contend that identifying suicide as a distinct entity acknowledges its complexity and facilitates focused research and intervention efforts without dismissing the influence of external factors.
    Finally, this paper proposes four key directions for furthering suicide-specific diagnosis:(1) Refining diagnostic criteria. Diagnostic models should incorporate new evidence on proximal risk factors, such as sleep disorders, emotional reactions and recent psychiatric discharges, in order to improve the prediction of suicide risk; (2) Clarifying relationships with other disorders. Future studies should disentangle the overlap between suicide-specific symptoms and those of other mental disorders. Neuroimaging and biomarker research may help identify distinct mechanisms underlying suicide, aiding differential diagnosis; (3) Expanding research populations and study designs. Current research primarily focuses on clinical populations and cross-sectional designs, limiting generalizability. Longitudinal studies across diverse populations (e.g., adolescents, older adults) are essential for understanding the development and progression of suicidal symptoms; (4) Developing standardized assessment tools. Reliable measurement instruments are crucial for translating diagnostic frameworks into clinical practice. Tools like the suicide crisis inventory and acute suicidal affective disturbance inventory should be refined to capture symptom severity across populations.
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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
    2025, 33 (3):  506-519.  doi: 10.3724/SP.J.1042.2025.0506
    Abstract ( 155 )   PDF (949KB) ( 236 )   Peer Review Comments
    Non-suicidal self-injury (NSSI) is a considerable public health issue, characterized by high levels of stigma, complexity, and heterogeneity. These characteristics collectively pose significant challenges to measurement and analysis, resulting in limited predictive power in related research. The specific manifestations encompass: (1) A lack of understanding, prejudice, and discrimination towards NSSI may prompt patients to conceal their behaviors in surveys. Furthermore, the contagious nature of NSSI among adolescents raises alarms among parents and schools, rendering it difficult to advance surveys. (2) The intricate nature of NSSI is evident in its myriad influencing factors, necessitating the integration of various factors, including biological genetics, social, and psychological variables, for research. Additionally, the theoretical models proposed to explain the emergence and progression of NSSI lack uniformity. Therefore, it is imperative to consider as many factors as possible across various contexts and among individuals. Traditional analytical methods have limitations in variable selection, and the established predictive models often fail to align fully with real-life scenarios. (3) The high heterogeneity observed in NSSI research is attributed to its comorbidity with multiple mental disorders and to the existence of distinct subtypes of NSSI, which vary in frequency, methods, severity, and functionality. The lack of standardization in the definition and measurement methods of NSSI also exacerbates the heterogeneity in research.
    In recent years, machine learning has been increasingly applied to the analysis and modelling of NSSI, generally improving the predictive performance to a moderate level. This research includes a total of 24 studies and provides a comprehensive analysis of the machine learning process through the use of Sankey diagram visualization, from sample collection to algorithm selection, model training, and model evaluation. The main findings are as follows: (1) Machine learning has the potential to overcome the limitations of research in predicting NSSI outcomes due to the challenges associated with measuring NSSI. This can be achieved by simplifying questionnaires or enriching other effective research tools that can iteratively remove unimportant questionnaire items, effectively avoiding participant fatigue or dishonesty. In addition, machine learning can be integrated with ecological momentary assessment, wearable devices, or social media to develop more effective research tools. (2) Machine learning can increase the number and variety of predictors by integrating different types of data (e.g., demographic, behavioral, and physiological data) to improve the model performance of NSSI by increasing complexity. In addition, machine learning can rank the importance of NSSI predictors to identify more critical factors to improve the model performance of NSSI by increasing accuracy. (3) Machine learning can effectively distinguish between different categories and subtypes of NSSI. Machine learning can classify NSSI alongside other psychological or psychiatric disorders, or identify the presence or absence of NSSI, which is beneficial for uncovering more key predictive factors of NSSI, or establish models with better performance in the same subtypes of NSSI. In addition, machine learning improves the performance of NSSI models by identifying common predictors across different definitions, measures, populations and subtypes.
    In the future, (1) To better improve the performance of NSSI models, future research should incorporate a broader range of data types, including structured data such as scales and experiments, as well as unstructured data such as voice, text, and images. (2) Combining non-questionnaire NSSI data with deep learning can enhance the predictive performance, such as complex data and image-style data generated by fMRI, fNRIS, EEG, or eye-tracking, and integrating unsupervised learning and deep learning for data dimensionality reduction, key feature extraction, and analysis. (3) It is necessary to combine traditional NSSI theories and methods to make the screening criteria more stringent, and to combine unsupervised learning with transfer learning to increase the reproducibility and comparability of the models.
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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
    2025, 33 (3):  520-536.  doi: 10.3724/SP.J.1042.2025.0520
    Abstract ( 237 )   PDF (1489KB) ( 354 )   Peer Review Comments
    Procrastination is a cross-culturally pervasive problematic behavior that not only has a serious negative impact on people's learning, work, life, and emotions, but can even be detrimental to their physical and mental health. While existing literature has explored the causes and influencing factors of procrastination, these studies have largely been confined to single-level or single-modality analyses. As a result, the complex mechanisms underlying procrastinatory behavior remain insufficiently understood. In recent years, the emerging paradigm of multi-modal and multi-omics research has offered a more comprehensive and systematic approach to unraveling the mechanisms behind procrastination. This paradigm integrates insights from behavior, cognition, neuroscience, genetics, microbiomics, and metabolomics. Therefore, this paper aims to construct an integrated theoretical framework that combines cognitive, neural, genetic, microbial, and metabolomic perspectives to comprehensively and systematically explain the complex mechanisms driving procrastination, thereby addressing the scientific question: “Why do humans procrastinate?” Firstly, based on the temporal decision-making model of procrastination, this paper systematically elaborates on the cognitive susceptibility factors contributing to procrastination from the viewpoint of motivational trade-offs. These factors include self-control, emotion regulation, and future thinking. Secondly, this paper goes through the neural bases closely related to procrastination behavior from multiple brain imaging modalities, such as structural, resting and task states, and further integrates evidence at the cognitive level to propose a new integrated triple cognitive neural network model to explain procrastination behavior. Specifically, the neural basis of procrastination involves three key brain networks: the self-control network (with core nodes in regions such as the dorsolateral prefrontal cortex and anterior cingulate cortex), the emotion regulation network (with core nodes in areas such as the insula and orbital frontal cortex), and the future thinking network (with core nodes in regions such as the ventromedial prefrontal cortex, parahippocampal cortex, and ventral striatum). Importantly, this paper innovatively proposes a multi-modal and multi-omics theoretical framework for procrastination research. This framework aims to explore how specific genetic and metabolic/microbial factors influence the development and shaping of brain structures and functional networks related to procrastination, which in turn affect core cognitive abilities, ultimately leading to the onset and perpetuation of procrastination. For instance, specific genetic loci such as the COMT gene may influence prefrontal cortex function by regulating dopamine levels, thereby affecting self-control capacity and predisposing individuals to adopt procrastination as a coping strategy. Variations in neurotransmitter levels can impact the sensitivity of reward systems, potentially altering individuals’ responses to task rewards and emotional regulation, thus influencing procrastination decisions and behaviors. Additionally, the composition and diversity of gut microbiota may influence the coordination of functional brain connectivity, thereby affecting the occurrence of procrastination through the gut-brain axis. Undoubtedly, procrastination is influenced by the integrated effects of multiple brain networks and their interactions with peripheral biological systems. Through a multi-modal and multi-omics perspective, we can not only further verify the impact of these critical cognitive, neural, genetic, microbial, and metabolic factors on procrastination but also elucidate their mechanisms of action. This approach holds promise for providing a more robust theoretical foundation for the identification and intervention of procrastination. Furthermore, this paper highlights the need for future research to enrich molecular genetics, metabolic, and microbiomic studies of procrastination. It emphasizes the importance of integrating multi-modal and multi-omics research to explore the developmental mechanisms of procrastination from a developmental perspective, aiming for early identification, prevention, and precise intervention. In summary, by systematically reviewing the latest research findings and analyzing the mechanisms of procrastination from cognitive, neural, and genetic/metabolic perspectives, this paper constructs a multi-modal and multi-omics theoretical framework. It aims to offer a multidimensional explanation of the complexity of procrastination, thereby more comprehensively elucidating its nature, causes, and underlying mechanisms. This comprehensive approach not only advances our understanding of procrastination but also opens new avenues for developing effective interventions.
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    Reviewers list of APS in 2024
    2025, 33 (3):  537. 
    Abstract ( 122 )   PDF (178KB) ( 244 )  
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