20 November 2025, Volume 48 Issue 6

    

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  How AI Reshapes Human Morality: Behavioral, Psychological, and Neural Perspectives

  Guo Xueli, Su Song, Liu Chao, Tang Honghong

  Journal of Psychological Science. 2025, 48(6): 1282-1293. https://doi.org/10.16719/j.cnki.1671-6981.20250601

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  The rapid development and widespread use of Artificial Intelligence (AI) have given rise to significant ethical concerns, with a key question being how AI influences human morality. This paper reviews relevant literature to examine the multifaceted impacts of AI on human morality.
  First, we propose potential underlying mechanisms to illustrate how AI applications influence human morality. Studies show that people typically attribute less “mind” to AI than to humans. This perception fundamentally influences humans’ cognitive judgments and emotional responses during human-AI interactions compared to human-human interactions, resulting in distinct patterns of moral cognition, moral emotions, and social evaluations. The most notable influence on moral cognition may be how humans change their attribution patterns and judgments of responsibility in morally relevant behaviors within Human-AI interactions. According to previous studies, when AI systems cause harm, people tend to blame the AI more and attribute a higher degree of responsibility for the harm to the AI than to human agents. People consider AI as a “scapegoat” for immoral behaviors in many situations, facilitating their immoral behaviors. Meanwhile, Human-AI interactions could enhance humans’ tendency for utilitarianism in moral judgment, which may reduce prosocial behavior. Furthermore, human-AI interactions typically mitigate humans’ emotional responses toward stimuli or behaviors related to morality. This tendency may contribute to increasing immoral behavior and decreasing prosocial behavior. Specifically, when AI initiates prosocial actions instead of humans, people’s moral emotional response is diminished, which reduces their prosocial tendencies. Additionally, human-AI interactions could undermine people’s concerns for social image. When interacting with AI, individuals tend to adhere less to moral principles and become less sensitive to social evaluation. This reduced social awareness may subsequently increase unethical behavior or decrease prosocial behavior.
  Then, we discuss the neural mechanisms underlying how human-AI interactions influence human morality. When people interact with AI rather than humans, their brain regions involved in social perception, emotion, and cognition show significant differences. Three primary brain networks demonstrate such differences: the interoception and human stereotype network, the social cognition network, and the valuation network. The interoception and human stereotype network primarily includes insula and MCC. These brain regions are less active during interactions with AI than with humans. The social cognition network involves the TPJ, STS, TP, SPL, PCC, and MTG. Activities in these regions are also decreased when people interact with AI. The valuation network primarily involves the MPFC, including the VMPFC and DMPFC. During human-AI interactions, compared to human-human interactions, both the ventromedial prefrontal cortex (VMPFC) and the dorsomedial prefrontal cortex (DMPFC) exhibit reduced activity during decision-making. This suggests diminished processing and evaluation of decision-related information.
  Finally, we discuss the positive and negative effects of AI on human morality across four different forms of human-AI interaction: AI delegator, AI advisor, AI collaborator, and AI moral model. When AI plays as a delegator, people may use AI as a proxy for executing immoral actions, or promote cooperation and fairness to benefit themselves. When AI serves as an advisor, its immoral suggestions can promote deceptive behavior, yet its moral advice does not necessarily encourage honesty. When AI functions as a collaborator, it has a mixed impact on human moral behaviors, revealing both positive and negative effects. When AI acts as a moral role model, it shows the potential to positively shape moral cognition and behavior. Its impact on children’s moral development is particularly pronounced, surpassing that observed in adults.
  Future research should focus more on exploring how different AI applications impact human morality, particularly their long-term effects. These efforts will provide valuable theoretical support and guidance for the design, development, and application of AI.
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  Who is AI? The Impact of Identity Labels in Multi-Round Trust Games

  Liu Jiahui, Gu Ruolei, Wu Tingting, Luo Yi

  Journal of Psychological Science. 2025, 48(6): 1294-1313. https://doi.org/10.16719/j.cnki.1671-6981.20250602

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  With the rapid development of artificial intelligence technology, intelligent agents are becoming increasingly prevalent in human life. Therefore, it is crucial to understand how identity labels influence human trust-based decision-making. Previous studies have revealed that there is a certain label effect on human trust. That is, even if all the participants are playing games with robots, when they are told that their partner is a robot, individuals cooperate less than when they are told that their partner is human(Ishowo-Oloko et al., 2019). In addition, the labels assigned to the participants themselves may also be one of the important factors influencing their trust.
  The present study investigated how identity labels (human vs. AI) of investors and trustees influence human trust in a multi-round trust game (MRT) with computational modeling. This experiment was a behavioral study involving 84 investors (25 males, mean age 21.5 ± 2.5 years old) and 84 trustees (23 males, mean age 21.0 ± 2.0 years old). It employed a mixed design with 2 factors: 2 (Investor Label: Human vs. AI) × 2 (Trustee Label: Human vs. AI). But both investor and trustee are fixed human partners and they played 40 rounds MRT in 2 periods labeled as different labels, respectively. In each round of MRT, the investor (played by the real participant) received ¥20 and decided how much of it to send to the trustee (played by another real participant). The amount (a, ranging from 0 to 20) the investor sent was tripled and delivered to the trustee, who returned b (ranging from 0 to 3*a) back to the investor. The investor ended up with 20-a+b; the trustee with 3*a-b.
  The results indicated that the investor’s investment amount increases as the trustee's return increases in the last round. As the interaction proceeds, the investor’s investment amount gradually increases. In addition, AI-labeled investors invested less to trustee, particularly interacting with human-labeled trustees. This suggests that the AI label reduces sensitivity to fairness norms and weakens impression management motives, as reflected in lower social preference parameters including envy and guilt in Fehr-Schmidt inequality aversion model (FS model). While investors generally invested more when they received higher returns from trustees in the last round, AI-labeled investors showed more random decision-making patterns (evidenced by elevated inverse temperature parameters in the Rescorla-Wagner model) specifically when interacting with human-labeled trustees. And learning rates did not differ across identity labels, indicating that the differences stem from decision-making strategies rather than learning efficiency. These results suggest that the effect of AI label on investors’ trust occurs through dual paths. One path is the weakening of motivation for impression management and sensitivity to static social norms and the reduction of the active pursuit of fair results. The other path is the decline in the utilization rate of dynamic learning strategies. When facing human, AI label reduce the investor’s adjustments based on historical feedback (the application of dynamic learning strategies decreases), and the behavior becomes more random.
  For trustees, the repayment increases as the investor's investment and decreases as the interaction proceeds. Trustee returned more money to the human-labeled investor than to the AI-labeled investor. The AI-labeled trustee exhibited higher repayment and stronger guilt, suggesting they internalized a "service obligation" stereotype. Then interacting with AI-labeled investors, this trend becomes stronger indicating that human-labeled trustee showed less trust expectation for AI-labeled investor and less concern on social evaluation from the AI labeled investor. Meanwhile, the dynamic behavior of trustees primarily exhibits a dominant influence pattern driven by current investment amounts, with minimal impact from cumulative experience. Furthermore, this influence pattern becomes more pronounced for trustees with an AI-label, suggesting that the AI label further reinforces such myopic and immediate decision-making patterns.
  These findings suggest that identity labels have a significant impact on individuals' trust decisions and are role-specific and context-specific. AI labels change individuals' decision-making patterns by weakening social norms (investors) or strengthening service obligations (trustees) and this indicates that AI label influence trust through dual paths of impression management mechanisms (altering social supervision expectations) and stereotype activation (internalizing behavioral characteristics of AI). This mechanism reveals the dynamic construction process of identity cognition, breaks through the traditional linear understanding of AI labels, and demonstrates its complex cognitive mechanism in social interaction - not only a passive trust intermediary, but also a dynamic cognitive switch that can actively reshape the boundaries of trust and cooperation, providing a new theoretical perspective for understanding the embedding process of artificial intelligence in social systems.
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  Using Agent-Based Modeling to Study the Adaptation and Evolution of Human Emotions and Cognition^*

  Duan Qin, Luo Siyang

  Journal of Psychological Science. 2025, 48(6): 1314-1332. https://doi.org/10.16719/j.cnki.1671-6981.20250603

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  This review explores the use of agent-based modeling (ABM) within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions, adaptive changes, and evolutionary processes. By representing agents and their defined environments with probabilistic interactions, ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level. The review looks into the applications of ABM in portraying some of the key components of emotions and cognition—empathy, cooperation, decision making, and emotional transmission—and analyzes the problems including scalability, empirical validation, and description of sensitive emotional states. The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence (AI) techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli. This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology, neuroscience, and engineering. This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking, healthcare, and education.
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  Collaborator or Creator? The role of AI in AI-Human Collaborative Creativity

  Zhou Qianzhu, Zhang Jiyu, Ji Cancan, Su Jiahao, Wang Yurou, Li Yadan

  Journal of Psychological Science. 2025, 48(6): 1333-1345. https://doi.org/10.16719/j.cnki.1671-6981.20250604

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  Creativity is an inexhaustible driving force for individual and social development. With the advent of artificial intelligence (AI), AI-human collaborative creativity has become a reality. At present, studies on AI-human collaborative creativity are scattered across various fields and lack a systematic analysis of their internal mechanisms. This gap hinders the full utilization of AI in creative activities.
  AI-human collaborative creativity is defined as the process in which AI assists humans, AI and humans enhance each other, to collaborate effectively and engage in close cooperation to achieve creative outcomes. Research on AI-human collaborative creativity primarily focuses on four domains of AI: robotics, virtual AI, embedded AI, and intelligent devices in extended reality. Different AI products exhibit varying levels of creativity and collaborative creativity capabilities. These AI products play different roles in the AI-human collaborative creativity process.
  To conduct an in-depth analysis of the role of AI in AI-human collaborative creativity, we propose a cognitive framework of "support-promotion-cooperation-generation ". In the domain of AI-supported human creativity, AI does not engage in creative activities and merely executes mechanical and repetitive tasks. As an auxiliary technology, AI frees people from heavy workloads to focus on creative work. In the domain of AI-enhanced human creativity, AI participates in the creative process, but its contribution is less than that of human. AI enhances individual daily creativity through bottom-up process (e.g., creative scaffolding, multisensory stimulation, and knowledge sharing) and top-down process (e.g., creative self-efficacy and self-regulated learning). In addition, AI contributes to prediction, identification, and selection in scientific creativity, while it enhances inspiration in artistic creativity. Besides, AI expands associations in divergent thinking and supports evaluation and selection in convergent thinking. In the domain of AI-human cooperative creativity, AI serves as an agent of creativity, with its contribution equal to that of human in the creative process. AI and human cooperate as independent entities to create, which can be divided into two forms: simultaneous creation (e.g., Shimon/LuminAI collaborate with individuals to create music/dance simultaneously) and alternating creation (e.g., AI and individuals take turns composing poetry). AI relies on data consistency and excels at exploratory creativity, while humans are better at transformational creativity, with the ability to shift across different dimensions. Due to their distinct characteristics, the cooperation can lead to complementary advantages in creativity. In the domain of AI-generated creativity, AI serves as an agent of creativity, contributing more than humans during the creative process. The originality performance of ChatGPT4 can surpass that of average individuals and other generative artificial intelligence (GAI) (e.g., ChatGPT3 and copy.ai). GAI is also capable of independently engaging in scientific and artistic creation. However, since AI cannot construct interpretations based on sensory experience and its writing tends to be homogenized, its performance in tasks such as Figural Interpretation Quest and creative writing is generally inferior to that of individuals.
  There are some influencing factors of AI-human collaborative creativity. First, individual factors include AI perception, personal abilities, individual creativity, and individuals’ views on GAI. Second, the AI aspects include temperature parameters, out-of-distribution parameters, robot traits, and sample learning. Lastly, organizational factors include organizational preparation and cohesion.
  Building on a comprehensive review of existing literature, future research can delve into the following aspects. First of all, the impact of AI-influenced emotional and cognitive factors on creativity still requires further investigation. AI may negatively impact individual creativity through cognitive interference, the emergence of STARA awareness, the creation of information cocoons, and the use of similar elements in text and artistic creation. Therefore, it is crucial to examine the potential negative impact of AI on creativity. Furthermore, future research should focus on exploring the behavioral and cognitive neural mechanisms through which AI promotes human creativity. Additionally, it's essential to focus on the ethical principles of AI-human collaborative creativity.
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  AI-Driven Information Reconstruction: Bridging Visual Perception and Memory

  Zhang Guoxin, Wang Wenqiang, Li Feng, Wang Benchi

  Journal of Psychological Science. 2025, 48(6): 1346-1358. https://doi.org/10.16719/j.cnki.1671-6981.20250605

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  This systematic review traces the evolution of neural decoding methodologies, from early visual feature extraction to the cutting-edge frontier of reconstructing memory and mental states. Advances in computational neuroscience and artificial intelligence have driven this progress, with sophisticated generative models redefining how neural signals are interpreted and translated.
  Early pioneering studies in visual perception laid the groundwork for neural decoding by demonstrating the reconstruction of basic visual elements—such as oriented edges and simple geometric shapes—from functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. These foundational efforts, while groundbreaking, were limited by simple stimulus designs and reliance on handcrafted feature representations. The advent of deep learning marked a transformative shift, enabling more flexible and nuanced interpretations of neural activity. Initial applications of variational autoencoders and generative adversarial networks established frameworks for mapping neural signals to latent representations, though challenges in reconstruction accuracy and model stability persisted.
  Recent advances in diffusion models have ushered in a new era of precision, significantly enhancing the translation of neural signals into detailed visual outputs. Modern frameworks now leverage biologically inspired, hierarchical architectures that separately process low-level visual features and high-level semantic content, closely mirroring the organization of the human visual system. These developments have enabled the reconstruction of complex naturalistic scenes, human faces, and dynamic visual experiences, bringing neural signal interpretation closer to practical applications.
  Venturing further, the field now confronts the more ambitious challenge of reconstructing memory and mental imagery—a task far more complex than visual perception decoding. Unlike perception studies that rely on controlled external stimuli, memory decoding must grapple with internally generated, highly subjective, and context-dependent neural signals. Preliminary research reveals intriguing parallels between neural patterns associated with recalled and perceived experiences, yet significant hurdles remain. These include the noisy and variable nature of memory-related signals, individual differences in neural encoding, and the dynamic, context-sensitive character of memory retrieval.
  Technical challenges in memory reconstruction remain formidable. Current approaches struggle to generalize across diverse memory types and individual neural encoding variations, limiting their applicability. Non-invasive neuroimaging techniques suffer from insufficient temporal resolution and signal clarity, obscuring the fine-grained dynamics of memory processes. Furthermore, existing neural signal encoders—typically optimized for simpler, stimulus-driven tasks like visual perception—often fail to capture the complex, distributed brain activity underlying memory. New theoretical frameworks are needed to model the reconstructive, dynamic, and context-dependent nature of human memory, which fundamentally differs from the linear stimulus-response dynamics characteristic of visual perception.
  Addressing these challenges demands several critical research priorities. First, developing advanced neuroimaging technologies with improved spatiotemporal resolution is essential to capture intricate, dynamic patterns of memory encoding. Second, designing more sophisticated neural signal encoders will better model the distributed and context-sensitive nature of memory processes. Third, creating large-scale, standardized neuroimaging datasets is vital for training robust, generalizable models that account for individual variability and diverse memory types. These efforts collectively aim to bridge current limitations and achieve accurate, generalizable memory reconstruction.
  Successful reconstruction of memory and mental states could fundamentally transform our understanding of human cognition, enable novel treatments for memory-related disorders, and revolutionize human-computer interfaces. However, these advancements also raise profound philosophical questions regarding the nature of memory, personal identity, and consciousness. As neural decoding approaches this pivotal juncture, researchers must carefully balance its transformative potential against technical, ethical, and societal challenges. The journey from visual reconstruction to comprehensive mental state decoding represents a groundbreaking endeavor at the intersection of neuroscience and artificial intelligence, with the potential to reshape our understanding of the human mind.
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  Discussion Topics, Sentiments, and Usage Intentions: A Three-Dimensional Perspective of Public Attitudes Toward Large Language Models

  Lin Yuan, Sun Ruitong, Zhang Jun, Xu Kan, Yu Shuo

  Journal of Psychological Science. 2025, 48(6): 1359-1369. https://doi.org/10.16719/j.cnki.1671-6981.20250606

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  Advances in research and technological innovation have demonstrated significant potential and application value of Large Language Models (LLMs) in fields such as healthcare, education, and journalism. However, their widespread adoption has also highlighted the “double-edged sword” nature of this technology. Challenges including accountability dilemmas caused by algorithmic black boxes, societal fairness concerns stemming from occupational displacement anxiety, and privacy risks associated with massive data processing have sparked widespread public debates about their value and potential risks. Consequently, understanding public attitudes and addressing concerns have become pivotal to balancing technological innovation with social acceptance. The proliferation of internet access and social media platforms has opened new paradigms and observational avenues for public attitude research. Existing studies on public perceptions of LLMs often focus on individual and external factors. This research leverages rich social media comment data and employs the ABC attitude model to identify key discussion topics about LLMs, analyzes the distribution of emotional and behavioral tendencies across these topics, and investigates the interaction mechanisms between public concerns, emotional expressions, and usage intentions.
  The study is conducted in two phases. In the first stage, data are collected from public comments on LLMs on the Douyin platform. Using Few-shot Prompting and human collaboration, it leverages the semantic understanding and generation capabilities of large language models to automatically categorize text into topics, emotions, and usage intentions. During the combination of large language models and manual clustering, the study references the Unified Theory of Acceptance and Use of Technology (UTAUT2) and the Value-Based Adoption Model to assist in thematic clustering. In the second stage, the study employs Multinomial Logistic Regression model to explore the impact mechanisms of topics on emotions and usage intentions of large language models. The independent variable is public discussion topics, sentiment serves as the dependent variable in Model 1, while usage intentions are the dependent variables in Models 2 and 3. Control variables account for regional differences and time effects.
  Key findings reveal: (1) Overall, negative sentiment and no tendency dominated the comments, but there were significant differences in public sentiment and usage tendency across topics. (2) Public usage intentions toward LLMs are jointly shaped by discussion topics and emotional factors. Specifically, performance expectancy, effort expectancy, and hedonic motivation discussion topics demonstrated positive effects on both emotions and usage intentions. Discussions about performance and effort expectancies significantly reduced negative emotions while enhancing usage intentions. Discussions about hedonic motivation not only fostered positive emotions but also mitigated behavioral resistance and increased adoption willingness. Conversely, discussions about price value negatively impacted emotions, significantly decreasing the likelihood of positive emotional expressions. Notably, emotional factors played a particularly crucial role, simultaneously reducing resistance to LLM usage and strengthening adoption intentions. Public sentiment and usage intentions toward LLMs have not shown significant regional divides. However, over time, the public’s positive sentiment toward large language models has gradually become more rational, while behavioral resistance to them has gradually diminished.
  This research contributes to psychological studies of public attitudes by introducing a novel analytical paradigm that integrates social media data with LLM methodologies, while adding a citizen-centric perspective into AI governance. First, the collaborative framework combining LLM processing with human-guided topic clustering effectively leverages LLMs’ superior text comprehension capabilities, overcoming the technical complexity and interpretational rigidity of traditional topic modeling approaches. The integration of manual validation and theoretical frameworks significantly enhances analytical accuracy and theoretical relevance. Second, by systematically mapping core public discussion topics and their associated emotional/behavioral patterns, and elucidating the mechanisms through which topics and emotions shape usage intentions, this study deepens the psychological understanding of attitude structures and public perception dynamics toward emerging AI technologies. Future research should expand multi-platform data collection, extend the research cycle, and explore commonalities and differences in public attitudes in cross-cultural contexts.
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  Do Large Language Models Grasp Poetic Prosody?A Human-Machine Comparison of Phonological Prediction in Classical Chinese Poetry

  Wei Tingxin, Li Jiabin, Zhao Ying, Wu Zhou, Chen Qingrong

  Journal of Psychological Science. 2025, 48(6): 1370-1383. https://doi.org/10.16719/j.cnki.1671-6981.20250607

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  Prediction is a core cognitive mechanism in human language processing, essential for understanding and producing language during listening, reading, and conversation. Recent advances in large pre-trained language models (LLMs) have shown striking success in mimicking human-like predictive behavior, sparking ongoing debate over whether such models exhibit "brain-like" mechanisms of prediction. Classical Chinese poetry, with its layered constraints of semantics, structure, and prosody, offers an ideal paradigm to probe multi-level linguistic prediction, particularly in phonological domains such as tonal and rhyming structures. This study presents a last-character prediction task that incorporates tonal class, rhyme category, and semantic consistency, using regulated verse as experimental material. We systematically compare the performance of human participants with various LLMs across three input conditions to explore similarities and differences in their predictive mechanisms.
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  A Multi-Factor Decision-Making Process Model for the Evolution of Fair Distribution

  Lu Chunlei, Li Xing, Zheng Hui, Wang Min, Zhao Xiaozheng, Gao Ziyuan, Li Jingjing, Zhou Xiaolin

  Journal of Psychological Science. 2025, 48(6): 1384-1393. https://doi.org/10.16719/j.cnki.1671-6981.20250608

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  The fair distribution of jointly acquired resources is a fundamental prerequisite for maintaining cooperative relationships across human societies. Although numerous studies have used agent-based simulations (ABS) to investigate how humans evolved preferences for fair resource allocation, they often examine individual factors in isolation, failing to provide a comprehensive understanding of the underlying evolutionary mechanisms. The biological market theory, which posits that interactions between organisms resemble trading behaviors observed in economic markets, offers a promising framework for integrating these disparate elements into a coherent whole.
  Building on this theory, we identify four critical factors in the evolution of fair distribution, including reputation, partner choice, outside options, and cognitive empathy. Reputation refers to the awareness of other individuals' decision-making preferences. Partner choice involves selecting whom to cooperate with. Outside options denote the expected benefits from cooperating with alternative agents. Cognitive empathy encompasses understanding and considering the cognitive decision-making processes of others. Extensive ABS research has shown that reputation is fundamental to the evolution of fair distribution. Partner choice allows agents to select partners, driven by the presence of better outside options. Cognitive empathy often evolves concurrently with fair distribution and can further promote its development.
  We subsequently integrate these four interconnected elements to propose a comprehensive decision-making process model that captures the evolutionary dynamics of fair distribution. This model formally describes the cognitive calculus through which two agents evaluate whether to maintain their current cooperative relationship or seek alternative partnerships. Specifically, the decision-making process involves three sequential but interdependent considerations: (1) Whether there are other potential partners who could provide significantly greater cooperative benefits; (2) Whether the anticipated incremental benefits of cooperating with alternative partners sufficiently outweigh the costs associated with switching partners; and (3) Whether these considerations would be deemed valid and compelling from the perspective of the current partner, thereby creating reciprocal expectations. This sophisticated decision-making process model inherently incorporates all four identified factors, demonstrating their functional interdependence. Simulation results demonstrate that this model effectively captures the emergence of fair distribution preference and behavior. Further simulation analysis verifies the model's explanatory power and highlights the adaptive functions of cognitive empathy and the consideration of multiple outside options within the framework of the biological market theory.
  Looking ahead, we identify two key directions for future research. First, we suggest moving beyond simple equality-based norms to explore the evolution of more nuanced fairness forms, such as proportional distribution based on contribution or need, and various forms of distributive justice, including liberal egalitarianism. This will help us better capture the complexity of fairness in real-world contexts. Second, while our current simulation effectively models cognitive decision-making processes in the evolution of fair distribution, future work should integrate evolutionary simulations with psychological experiments, computational modeling, and brain imaging studies. This integration will elucidate how our evolved biology shapes fair-sharing behavior and provide a more complete link between evolutionary theory, cognitive mechanisms, and observable actions.
  In this review, we explored the evolution of fair distribution using ABS technology. Grounded in biological market theory, we highlighted the key roles of reputation, partner selection, outside options, and cognitive empathy in driving fair distribution. We also integrated these elements into a model that captures the cognitive decision-making process of agents and demonstrated how biological market theory can unify multiple factors in the evolution of fair distribution. We believe this model offers a comprehensive understanding of the evolutionary mechanisms underlying fair distribution and may inform the development and implementation of fair distribution policies.
General Psychology, Experimental Psychology & Ergonomics
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  The Impact of Imitation on Deceptive Behavior and the Mediating Mechanism

  Fang Jinru, Geng Xiaowei

  Journal of Psychological Science. 2025, 48(6): 1394-1407. https://doi.org/10.16719/j.cnki.1671-6981.20250609

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  Imitation serves as a crucial mechanism for the transmission of human culture and is central to individual moral development. Conventional imitation refers to the more accurate imitation by which individuals learn the normative behaviors of the group through imitation. Instrumental learning refers to the less accurate imitation by which individuals learn the technical behavior of the group through imitation. These two forms of imitation exert different effects on cognitive strategies and behavioral outcomes. The purpose of this study is to investigate the effects of conventional imitation and instrumental imitation on deceptive behavior, with particular attention to the mediating role of moral idealism. The hypotheses are as follows: individuals exhibit significantly lower levels of deceptive behavior under conventional imitation conditions compared to instrumental imitation(H1); conventional imitation significantly enhances individual moral idealism relative to instrumental imitation(H2a); and moral idealism plays a mediating role in conventional imitation reducing deceptive behavior(H2b).
  We conducted three experiments to test these hypotheses. In Experiment 1, a total of 129 participants were randomly assigned to either conventional or instrumental imitation conditions. Participants were primed with instructional language and videos of making a necklace. In conventional imitation, the instructional language emphasized "we have always done it this way", with synchronized and consistent demonstrator actions. In contrast, instrumental imitation highlighted "see how he/she does it", with the demonstrators' actions not synchronized or consistent. Deceptive behavior was subsequently measured using a mental math task. An independent-samples t-test revealed that participants in the conventional imitation condition exhibited lower rates of cheating and plagiarism.
  In Experiment 2a, a total of 168 undergraduate students were randomly assigned to three different conditions (i.e., conventional imitation, instrumental imitation, and control condition). Participants in the conventional imitation and instrumental imitation conditions were primed similarly to Experiment 1, while those in the control condition only made necklaces without additional priming. Moral idealism was assessed using the Idealism Scale from the Ethics Position Questionnaire, and deceptive behavior was measured using a Fault Finding Task. One-way ANOVA results showed that participants in the conventional imitation condition demonstrated significantly lower levels of deceptive behavior and higher levels of moral idealism compared to the other conditions. There was no significant difference between the instrumental imitation condition and the control condition. A three-step regression analysis confirmed that moral idealism partially mediated the effect of conventional imitation on reducing deception.
  In Experiment 2b, a total of 131 undergraduates were randomly assigned to two different conditions, moral idealism and control condition. Participants in the moral idealism condition were primed by unscrambling sentences (e.g., the scrambled sentence “Moral standards” “ judging oneself” “green plants” “and others” “apply to” may be recomposed as “Moral standards apply to judging oneself and others”). In control conditions, participants completed common sense-based sentences (e.g., “1. The sun, 2. High-rise buildings, 3. All revolve around, 4. The nine major planets, 5. Motion”). It can be recomposed as, “The nine major planets all revolve around the sun motion.”They then completed different finding tasks. Independent-samples t-tests showed that participants in the moral idealism condition exhibited less cheating behavior than those in the control condition, suggesting that moral idealism can effectively reduce cheating behavior.
  In summary, the research found that conventional imitation reduces deceptive behavior, moral idealism played a mediating role, whereas instrumental imitation does not affect deceptive behavior. These findings contribute to our understanding of imitation behavior and moral decision making, and also provide some guidance for moral education.
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  The Online Processing of Semantic and Pragmatic Constraints in Chinese Focus Adverb Sentences

  Wang Yuxia, Chen Li

  Journal of Psychological Science. 2025, 48(6): 1408-1417. https://doi.org/10.16719/j.cnki.1671-6981.20250610

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  The current study examined the online processing of semantic and pragmatic information in Mandarin exclusive focus adverb sentences, such as "Only middle school students can solve such difficult math problems; college students should also can/cannot." The focus particle "zhi" (only) requires subsequent sentences to conform to exclusive semantic constraints (i.e., college students cannot), while world knowledge requires them to satisfy event plausibility pragmatic constraints (i.e., college students can). Since subsequent sentences must satisfy both exclusive semantic constraints and event plausibility pragmatic constraints, this provides an ideal window for examining the real-time processing mechanisms of semantic and pragmatic information at the propositional level.
  To investigate these dynamics, the study employed three experimental methods: offline questionnaire, self-paced reading, and eye tracking. Using a 2 X 2 experimental design, this study manipulated semantic constraints (consistent versus inconsistent) and pragmatic plausibility (plausible versus implausible). The materials included 40 target items and 64 fillers, counterbalanced using a Latin square design, with 40 participants in each task. For the online experiments, the analyses focused on the critical words “can/cannot” and the subsequent two-word spillover region.
  The offline questionnaire showed that pragmatic constraints were the key factor determining sentence acceptability, with semantic constraints further enhancing acceptability when pragmatics were plausible. Online experiments demonstrated that semantic constraints were processed with priority: when sentences conformed to exclusive semantic constraints, processing difficulty was low; when they violated such semantic constraints, processing difficulty was high. Meanwhile, pragmatic constraints played a role during integration, which was modulated by semantic constraints.
  In the self-paced reading task, pragmatic information only had an effect when semantic constraints were met. When semantic constraints were met, pragmatically implausible sentences incurred the greatest processing cost in the study. In contrast, when semantics were consistent, the pragmatic factor had no effect, indicating that semantic conflict dominated processing. We propose that the segmented presentation of self-paced reading creates distinct processing patterns. When semantics are inconsistent, resolving semantic conflicts consumes available cognitive resources, leaving none for pragmatic processing. However, when semantics are consistent, readers can fully construct semantic representations, allowing pragmatic factors to influence processing—with pragmatically implausible content increasing cognitive load.
  In contrast, the eye-tracking stud showed that pragmatic information played a role when semantic constraints were violated: pragmatic plausibility did not play a role when semantic constraints were satisfied; pragmatic plausible sentences significantly alleviated processing difficulties when semantics were inconsistent where readers relied on pragmatic information to resolve semantic conflicts. We termed this the “pragmatic attraction effect”, a form of “acceptability illusion”. For semantic inconsistent sentences, pragmatic plausibility provided readers with a sense of rationality, making the sentence appear more reasonable and speeding up processing. Unlike the self-paced reading study where processing was broken into segments, the pragmatic attraction effect might be tied to the continuous, parallel processing enabled by natural eye movement.
  These findings collectively suggest that offline processing supports top-down pragmatic strategies, while online comprehension aligns with a "semantic-first, pragmatics-delayed" processing model. This highlights the critical influence of semantic constraints in interpreting focus adverb sentences, with pragmatic constraints modulating semantic conflicts under specific conditions. The methodological dissociation between self-paced and eye-tracking paradigms further highlights how task demands shape the integration of semantic and pragmatic information during sentence comprehension, revealing different mechanisms at play during online language processing.
Developmental & Educational Psychology
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  Why Do Parents of Children with Autism Spectrum Disorder Have Lower Well-Being Than Typical Parents? An Analysis Based on Social Support, Psychological Resilience, and Parenting Stress

  Zheng Jianhong, Huang Jingmin, He Chenglin

  Journal of Psychological Science. 2025, 48(6): 1418-1428. https://doi.org/10.16719/j.cnki.1671-6981.20250611

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  This study aims to explore how social support, psychological resilience, and parenting stress influence the well-being of parents of children with autism spectrum disorder (ASD) and to conduct a comparative analysis with parents of typically developing children. The prevalence of ASD is rising globally, particularly in China, where the number of newly diagnosed cases is increasing significantly each year. This surge not only imposes substantial pressure on affected families but also challenges social and public health systems. Due to the long-term mental and financial burdens of caregiving, the well-being of parents of children with ASD is significantly lower than that of parents of typically developing children, and they are more susceptible to severe mental health issues. Although several national policies have been introduced to support the education and rehabilitation of children with ASD, the mental health of these parents has not received adequate attention. Therefore, this study focuses on analyzing the mechanisms through which social support, psychological resilience, and parenting stress influence the well-being of parents of children with ASD.
  This research employed a questionnaire survey method, targeting 1,004 parents of children with ASD and 1,305 parents of typically developing children from 21 provinces, municipalities, and autonomous regions in China. The study utilized several instruments, including the General Well-Being Schedule (GWB), the Social Support Rating Scale (SSRS), the Parenting Stress Index-Short Form (PSI-SF), the Connor-Davidson Resilience Scale (CD-RISC), and the Psychological Resilience Questionnaire for Parents of Children with Special Needs. For data analysis, SPSS 22.0 was employed to conduct descriptive statistics, correlation analyses, and t-tests. Additionally, Mplus 8.3 was utilized to test chain mediation effects and between-group differences.
  The results indicate that parents of children with ASD have significantly lower levels of social support compared to parents of typically developing children (t(2307) = 16.49, p < .001), and experience significantly higher levels of parenting stress (t(2307) = -13.21, p < .001). However, their psychological resilience is significantly higher than parents of typically developing children (t(2307) = -35.54, p < .001). Parents of children with ASD reported significantly lower levels of well-being compared to parents of typically developing children (t(2307) = 12.91, p < .001). Correlation analysis revealed that the well-being of parents of children with ASD is significantly and positively correlated with social support and psychological resilience (p < .01), while it is significantly and negatively correlated with parenting stress (p < .01). Further chain mediation analysis showed that social support indirectly enhances the well-being of parents of children with ASD by increasing psychological resilience and reducing parenting stress. The mediating effect of psychological resilience accounted for 74.1% of the total indirect effect, while the mediating effect of parenting stress accounted for only 6.7%. In contrast, for the group of parents of typically developing children, the mediating effect of psychological resilience accounted for 42.1% and parenting stress accounted for 47.6%. The results of the multiple-group structural equation modeling comparison revealed that the serial mediating effect of "psychological resilience → parenting stress" was significantly stronger in parents of children with ASD than in parents of typically developing children. This indicates that, compared to parents of typically developing children, parents of children with ASD rely more on psychological resilience as a pathway through which social support enhances their well-being.
  The findings suggest that psychological resilience can effectively mitigate the negative effects of high parenting stress and serves as an essential internal protective mechanism for enhancing the well-being of parents of children with ASD. Social support plays a central role in the relationship between psychological resilience and well-being, indicating that strengthening the social support and psychological resilience of parents of children with ASD can effectively reduce their parenting stress and enhance their overall well-being. Therefore, social service providers and policymakers should focus on building a robust social support system for these parents and developing intervention programs aimed at enhancing their psychological resilience to better help them cope with the challenges of parenting and improve their quality of life.
Social, Personality & Organizational Psychology
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  Love and Indifference Can be Transmitted: The Acquisition of Feelings of Helpfulness and Helplessness and Their Effects on the Tendency to Help Others

  Zhang Qiyong, Gao Mei, Lu Jiamei

  Journal of Psychological Science. 2025, 48(6): 1429-1438. https://doi.org/10.16719/j.cnki.1671-6981.20250612

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  Learned helplessness has become a popular topic in motivation studies. However, previous research on learned helplessness has three major shortcomings. First, when animals or humans were used as experimental subjects, they were usually kept in isolation and lacked the experience of seeking help from their peers. In other words, research on learned helplessness only describes the psychological state of individuals after repeated failures of self-help, without considering actual help-seeking behavior. Second, prior studies failed to address the behavioral tendencies of humans after acquiring learned helplessness, ignoring the perspective of social psychology. Learned helplessness, as a motivation theory, has predominantly been confined to individual psychological analysis, with the explanatory unit being the “individual” rather than the “interpersonal.” Third, the helplessness observed in animals has been directly extended to explain human behavior after failed self-help attempts. However, humans experience more complex psychological processes before seeking help, involving various factors such as psychological distance and attributional style, which modulate learned helplessness in humans.
  In Experiment 1, we investigated how interpersonal psychological distance (familiarity vs. strangeness) and attributional style (optimism vs. pessimism) regulate learned helplessness. This experiment employed a two-factor experimental design, adopted an adapted version of the human Learned-Helplessness-Induced task paradigm, and collected participants’ physiological indices using a biofeedback instrument. Experiment 2 compared the differences in helping behavior among individuals who experienced learned helplessness (following repeated help-seeking failures), those who received help (following repeated help-seeking successes), those with no experience of help-seeking. The interaction effect of group and attributional style on helping tendencies was also examined. This experiment employed a 3×2 mixed design with three groups (learned helplessness, learned help, control group) and two attributional styles (optimistic vs. pessimistic), with helping others answer questions as an indicator of helping behavior. In Experiment 3, we constructed a psychological model of helping behavior based on learned helplessness, psychological distance, and attributional style.
  The findings revealed that: (1) Participants quickly developed a feeling of learned helplessness after multiple failures in seeking help, with a stronger sense of learned helplessness observed when failing to seek help from acquaintances compared to strangers. Individuals with a pessimistic attributional style exhibited greater learned helplessness than those with an optimistic style after repeated failures in seeking help. (2) Learned helplessness and help-receiving could change participants’ tendencies to help others. Those with an optimistic attributional style who received help displayed the highest level of willingness to help others, while individuals with a pessimistic attributional style who experienced learned helplessness exhibited the lowest level of helping behavior. (3) Learned helplessness reduced the helping tendencies of individuals with an optimistic attributional style but had minimal effects on those with a pessimistic attributional style. However, learned help enhanced the inclination to assist others, regardless of attributional style. (4) Learned helplessness and interpersonal psychological distance mediated the relationship between attributional style and helping behavior. While attributional style did not directly influence helping tendencies, learned helplessness and interpersonal psychological distance exerted a negative regulatory effect on the inclination to help others.
  In conclusion, the results suggest that learned helplessness emerges quickly after repeated failures in seeking help, with stronger effects observed when failing to seek help from acquaintances rather than strangers. Learned helplessness, psychological distance, and attributional style significantly influence individuals' helping tendencies. The sense of being helped transforms into love, whereas the sense of helplessness translates into indifference, both of which can be acquired and transmitted. Individuals with a pessimistic attributional style experience stronger learned helplessness compared to those with an optimistic style after repeated failures in seeking help. While attributional style does not directly influence helping tendencies, learned helplessness and interpersonal psychological distance negatively regulate such tendencies. The psychological model of helping behavior based on learned helplessness provides valuable insights into the mechanisms underlying helping behavior and highlights the importance of considering individual differences in attributional style and psychological distance when studying learned helplessness.
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  Mainstream or Niche Brands?The Impact of Need for Uniqueness and Goal-Pursuit Stage on Consumer Brand Choices

  Lei Shuyu, Du Jiangang, Yang Defeng

  Journal of Psychological Science. 2025, 48(6): 1439-1449. https://doi.org/10.16719/j.cnki.1671-6981.20250613

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  In contemporary society, consumers frequently pursue identity goals, which are ideal self-concepts that they aspire to embody. Examples include becoming a fitness expert or a corporate elite. These pursuits significantly shape consumer behavior, as individuals often emulate the brand preferences of aspirational groups, defined as external groups that consumers wish to join, reflecting their ideal self-image. While prior research has extensively examined specific, tangible goals (e.g., losing weight or passing an exam) and their effects on consumer behavior, the role of abstract identity goals in driving brand choices remains underexplored. This is a critical gap, given that aspirational groups exhibit diverse brand preferences, ranging from widely adopted mainstream brands to less common niche brands. This study investigates how consumers’ need for uniqueness and the stage of their identity goal pursuit jointly influence their preference for mainstream versus niche brands associated with aspirational groups. This study addresses the following questions: Do consumers with different levels of need for uniqueness exhibit varying brand preferences between mainstream and niche brands across goal pursuit stages? If so, what psychological mechanisms underlie these shifts?
  Grounded in goal pursuit theory, individuals’ reference points evolve across goal pursuit stages. In the initial stage, individuals use their initial state as a reference point, focusing on the gap between their current state (membership group) and their ideal state (aspirational group), fostering a desire to associate with the latter’s image. In the advanced stage, the reference point shifts to the ideal state, emphasizing alignment with their goal. Consequently, this research hypothesizes that in the initial stage, consumers, regardless of their need for uniqueness, favor mainstream brands due to a higher desire to associate with the aspirational group. In the advanced stage, low-uniqueness-need consumers, sensitive to interpersonal influence, prefer mainstream brands to secure group acceptance, while high-uniqueness-need consumers, less influenced by others, opt for niche brands to express individuality.
  With 152 participants, Experiment 1 employed a 2 (goal pursuit stage: initial vs. advanced) × 2 (need for uniqueness: high vs. low) between-subjects design and examined the interaction between the need for uniqueness and goal pursuit stage on brand preference. The results showed that in the initial stage, both high- and low-uniqueness consumers preferred mainstream brands. However, in the advanced stage, low-uniqueness consumers continued to prefer mainstream brands, whereas high-uniqueness consumers shifted toward niche brands. This experiment also confirmed that sensitivity to interpersonal influence mediated the effect in the advanced stage, but the need for belonging did not serve as an alternative explanation. Experiment 2 replicated these findings with 164 participants and further explored why the need for uniqueness did not influence brand choice in the initial stage. It confirmed that consumers’ desire to be associated with the aspirational group significantly masked the effect of the need for uniqueness on brand choice in the initial stage.
  This research holds significant theoretical and practical implications. First, unlike previous studies that focus on individuals’ motivation and behavior in specific goal pursuit processes, this study examines identity goals’ impact on consumer brand choice, providing new insights into goal pursuit research. Second, while prior studies on goal pursuit stages have primarily focused on reference point shifts and regulatory focus, this study expands the application scope of goal pursuit theory by exploring changes in consumers’ psychological states and their preferences for aspirational group brands at different stages of identity goal pursuit. Furthermore, while existing literature generally posits that consumers with a high need for uniqueness tend to prefer niche brands, this study suggests that this preference does not apply in the initial stage of goal pursuit, challenging traditional uniqueness need theory. Lastly, this study deepens the understanding of reference group influence and sensitivity to interpersonal influence in consumer brand choice, providing insights for marketers seeking to target consumers at different goal pursuit stages. These findings can help businesses to develop more effective branding strategies that align with consumers’ evolving psychological needs.
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  The Effect of Control Deprivation on Mysterious Goods Preference and Its Mechanism

  Zhong Yingyan, Liang Ruixuan, Wang Yan

  Journal of Psychological Science. 2025, 48(6): 1450-1461. https://doi.org/10.16719/j.cnki.1671-6981.20250614

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  Mystery goods are products whose exact nature is unknown to consumers before they commit to buying them. Previous studies have mainly analyzed the impact of mysterious goods from an economic perspective, based on supply or sales perspective. The psychological reasons for consumers’ preference for mystery goods have not been adequately studied. Therefore, this study focuses on the antecedent variables of consumers’ preferences for mystery goods and their psychological mechanisms from a consumer perspective. Based on the theory of compensatory consumption, the present research examines the effects of control deprivation on preferences for mystery goods and their psychological mechanisms. The purposes of this research are to examine the effects and mechanisms of control deprivation on mystery good preferences, to test the basic hypothesis that control deprivation increases mystery good preferences, and to explore whether consumers’ probability estimates of obtaining a desirable good are the psychological mechanisms at play, as well as how consumers’ own internal and external locus of control may moderate this effect.
  This research consisted of a pre-experiment and three formal experiments. First, the mystery goods materials were selected in the pre-experiment. Next, Experiment 1 examined whether control deprivation would increase preferences for mystery goods. Then, the mediating role of likelihood judgment in this effect and the moderating role of locus of control were examined in Experiment 2. Finally, the psychological mechanism of likelihood judgment was tested by manipulating the likelihood judgment in Experiment 3. In Experiments 2 and 3, participants had the opportunity to obtain the goods of their choice, which enhanced the ecological validity of the research. To establish the robustness of the proposed effect and the mechanism, control deprivation was manipulated through a recall task (Experiment 1) and a concept recognition task (Experiments 2 and 3). Mystery goods preference was measured using a simulated shopping paradigm (Experiment 1) and an incentive-compatible choice paradigm (Experiments 2 and 3). The psychological mechanism of likelihood judgment was tested by combining a measurement-of-mediation approach (Experiment 2) and a moderation-of-process approach (Experiment 3).
  Results indicate that control deprivation influences mystery goods preferences, and that probability estimation is the psychological mechanism. Specifically, during control deprivation, people overestimate the probability of obtaining a desirable item, which increases their preference for mystery goods. The findings are consistent with the control-defence mechanism and the compensatory consumption behavior model, which suggests that control deprivation prompts individuals to compensate for the sense of control through a variety of behaviors, including changes in consumer preferences and consumption behavior. Moreover, the phenomenon of overestimating the likelihood of obtaining a desirable good caused by control deprivation is consistent with the illusion of control driven by the desire for control. When people lack control, they subjectively increase the probability of winning a chance outcome, thereby increasing their sense of control over low-probability outcomes. Individuals under control deprivation in this research show likelihood overestimation and further manifested this tendency in their consumption behavior by purchasing mystery goods. In addition, an individual’s locus of control moderated the above effects, as evidenced by the fact that individuals with an internal locus of control are more likely to experience likelihood overestimation during control deprivation. Previous research has shown that the internal locus of control amplifies the effects of the desire for control on gambling behavior. This is because the internal locus of control increases an individual’s perceived ability to pick winning numbers, and the combination of perceived ability and control motivation exacerbates betting tendencies. It can be seen that an individual’s locus of control tendency moderates the extent to which people are influenced by probabilistic sales strategies when the perception of control decreases or the desire for control increases. The findings support and extend the theory of compensatory consumption and provide practical insights for marketers and consumers.
Research on Social Psychological Service in the New Era
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  The Relationship between Family Environment and Children's Mental Health: A Three-Level Meta-Analysis

  Chen Ziwei, Jin Juanjuan, Yu Guoliang

  Journal of Psychological Science. 2025, 48(6): 1462-1480. https://doi.org/10.16719/j.cnki.1671-6981.20250615

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  Children's mental health serves as the foundation and assurance for their positive development. Unfortunately, the state of children's mental health is concerning at present. On the one hand, mental health problems such as depression and anxiety are increasingly prevalent among children. On the other hand, many children are experiencing impaired well-being due to the lack of economic and social resources that family provides. Consequently, it is imperative to prioritize the maintenance of children's mental health. Family is the central environment of children's positive development. It provides both long-term and immediate care and support, and affects children's emotion, cognition, and behavior, which is crucial for promoting children's mental health. However, a review of existing literature reveals significant variability in research findings regarding the relationship between family environment and children's mental health, largely attributable to ambiguous definitions of family environment and confounding variables. Furthermore, there is a lack of comparative analysis on comparison of the effects of different family environment factors on children's mental health, leaving their relative significance unclear. In this study, the family environment was distinctly delineated, encompassing both the physical and psychological dimensions, with family socioeconomic status and parenting style identified as indicators. Based on the dual-factor model, depression and anxiety were identified as negative indicators of mental health, while well-being was selected as a positive indicator. Three-level meta-analysis and relative weight analysis were used to analyze the influence and relative importance of physical and psychological environment on children's mental health.
  A three-level meta-analysis was employed to comprehensively assess the relationships between family environments (family socioeconomic status and parenting style) and children's mental health (e.g., depression, anxiety, and well-being) along with moderating factors influencing these relationships. Considering that several countries have introduced policies to improve the family environment to maintain children's mental health since 2010, the literature search period for this study was from January 2010 to December 2023. The literature search database included Web of Science, Psych INFO, Eric, Psych Articles, ProQuest Dissertations and Theses, CNKI, and Masters and Doctoral These Database. The search terms of family environment, mental health, and children were combined and searched in the abstract. Besides, relative weight analysis is utilized to ascertain the comparative importance of these two environmental dimensions of family concerning children's mental health outcomes. This study encompassed 316 articles published between 2010 and 2023, with 1,255 effect sizes across 339 independent samples and a total of 712,642 participants aged 0~18.
  The findings indicate that: (1) Family socioeconomic status was negatively correlated with depression and anxiety, and positively correlated with happiness. Positive parenting style was negatively correlated with depression and anxiety, and positively correlated with happiness; Conversely, negative parenting styles showed significant positive correlations with both depression/anxiety alongside significant negative correlations with happiness; (2) The relative importance ranking of parenting styles for children's mental health is higher than the family socioeconomic status; (3) Factors such as developmental stage, cultural background, publication year, parenting subject, and type of family socioeconomic status significantly moderated the relationship between family environments and children's mental health.
  The results underscore that favorable socioeconomic conditions coupled with positive parenting practices as protective factors for children's mental health whereas negative parenting approaches pose risks. These findings align with the social causation hypothesis, social selection hypothesis, attachment theory, and family systems theory. Besides, the importance of the family psychological environment is stronger than the physical environment. The research analyzed the relative importance of the physical and psychological aspects of family on children's mental health, and discussed the theoretical inconsistence between family socioeconomic status and children's mental health with empirical evidence. At the meantime, the research underscores the critical importance of emotional atmosphere in preserving children's mental health and offers scientific guidance for researchers to formulate objectives and content of family interventions, which holds both theoretical and practical significance.
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  The Relationship between Family Relationships and Children's Mental Health: A Three-Level Meta-Analysis

  Zhao Chengjia, Huang Xiaoxiao, Yu Guoliang

  Journal of Psychological Science. 2025, 48(6): 1481-1496. https://doi.org/10.16719/j.cnki.1671-6981.20250616

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  Family relationships play a crucial role in children’s mental health development, serving as the primary social context for psychological growth and adjustment. However, existing research demonstrates inconsistent findings regarding their relative importance and impacts on children’s mental health outcomes. While some studies emphasize the dominant role of parent-child relationships, others highlight the significance of marital relationships. Similarly, the role of sibling relationships remains debated, with mixed evidence on their unique contributions. Furthermore, theoretical perspectives offer contrasting predictions: the protective theory posits that positive family relationships promote mental health, the contagion risk theory cautions against potential negative effects even within positive relationships, and the multiple influence theory underscores the complex interplay of family subsystems. This three-level meta-analysis addresses these inconsistencies by (1) examining associations between three family relationship types (parent-child, marital, sibling) and children’s mental health (depression, anxiety, well-being), (2) quantifying their relative impacts via relative weight analysis, and (3) exploring moderating effects of gender ratio, developmental stage, cultural background, and family relationship informants.
  The analysis synthesized findings from 312 studies (2010~2023), comprising 320 independent samples (N = 427,585). Three-level meta-analytic models revealed significant associations between family relationships and children's mental health outcomes. Positive family relationships showed negative correlations with depression (parent-child: r = -.30, 95%CI = [-.35, -.28]; marital: r = -.18, 95%CI = [-.34, -.01]; sibling: r = -.15, 95%CI = [-.21, -.09]) and anxiety (parent-child: r = -.22, 95%CI = [-.29, -.17]; marital: r = -.21, 95%CI = [-.36, -.08]), while demonstrating positive correlations with well-being (parent-child: r = .41, 95%CI = [.38, .49]). Negative family relationships exhibited opposite patterns, showing positive correlations with depression (parent-child: r = .30, 95%CI = [.27, .34]; marital: r = .30, 95%CI = [.27, .35]; sibling: r = .26, 95%CI = [.13, .40]) and anxiety (parent-child: r = .33, 95%CI = [.27, .42]; marital: r = .27, 95%CI = [.18, .36]), and negative correlations with well-being (parent-child: r = -.19, 95%CI = [-.37, -.02]; marital: r = -.29, 95%CI = [-.37, -.22]). Relative weight analysis revealed that parent-child relationships contributed the most variance to depression (positive: 68.86%; negative: 35.32%) and anxiety (positive: 53.11%; negative: 63.04%), followed by marital relationships (depression-positive: 17.78%, depression-negative: 34.94%; anxiety-positive: 46.89%, anxiety-negative: 36.96%), and sibling relationships (depression-positive: 13.36%, depression-negative: 29.72%). However, for well-being, marital relationships demonstrated stronger effects than parent-child relationships, with negative marital relationships explaining 75.51% of the variance compared to 24.49% for negative parent-child relationships. Moderator analyses identified significant effects of cultural background, and relationship reporters. Associations were consistently stronger in collectivistic cultures than in individualistic cultures. Children-reported marital conflict showed stronger associations with depression compared to parental-reported. Gender ratio and developmental stage showed no significant moderating effects, suggesting the universality of these family relationship impacts across gender and age groups.
  This study provides robust support for protective theory by demonstrating that positive family relationships correlate with better mental health outcomes in children, whereas negative relationships show stronger associations with elevated risks of depression and anxiety. Relative weight analysis revealed differential correlational strengths across subsystems: parent-child relationships exhibited the strongest correlations with depression and anxiety, consistent with their proximal “developmental cornerstone” role, while negative marital relationships demonstrated a stronger inverse correlation with well-being compared to negative parent-child relationships, underscoring the unique salience of marital dynamics in emotional climate formation. Cultural context and family relationship informants significantly moderated these associations: collectivistic cultures showed stronger correlations overall, and child-reported marital conflict correlated more strongly with depression than parental reports. These patterns align with multiple influence theories, advocating for interventions that prioritize culturally attuned parent-child support programs and marital conflict resolution strategies to target subsystem-specific correlations.
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  The Relationship between Family Function and Children’s Mental Health: A Three-Level Meta-Analysis

  Qiao Yue, Huang Xiaoxiao, Yu Guoliang

  Journal of Psychological Science. 2025, 48(6): 1497-1515. https://doi.org/10.16719/j.cnki.1671-6981.20250617

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  As the fundamental building block of society, the integrity and soundness of family functions not only lay a solid foundation for the smooth operation of family production and daily life but also constitute a crucial determinant in children's mental health. However, the extant research on family functions is rather limited and one-sided. Disputes linger over the correlations between certain family function dimensions and children's mental health. Additionally, due to the intricate and multifaceted nature of family function concepts and dimensions, a comprehensive and systematic analysis of the relationship between family functions and children's mental health remains lacking.
  Consequently, this study conducted an extensive literature retrieval from six major databases, namely Web of Science Core Collection, PsychINFO, Eric, PsychArticles, ProQuest Dissertations & Theses, and China National Knowledge Infrastructure (CNKI). After a meticulous screening process, a total of 96 relevant studies were identified, published between 2010 and 2023. These encompassed 96 independent samples, 238 effect values, and involved 253,383 subjects. Based on mental health indicators, the studies were categorized into three sub-datasets. Notably, the average quality of the literature in each of the three subsets was 6, exceeding the theoretical average score, thereby indicating to the relatively high overall quality of the selected literature. This study then employed a three-level meta-analysis and a random effects model to rigorously investigate the relationship between children's mental health and family functions. Through a detailed relative weight analysis, it delved into the specific contributions of each dimension of family functions, namely family affectiveness, family communication, family norms, and family problem-solving, to children's mental health.
  The results manifested as follows: (1) Sound family functions were capable of mitigating the risks of children developing anxiety and depressive emotions and effectively enhancing their sense of well-being. (2) There existed significant disparities in the contributions of different family function dimensions. In the context of depression, the contributions in descending order were family emotion (40.49%), family communication (19.97%), family norms (19.94%), and family problem-solving (19.60%). For anxiety, the contributions in descending order were family emotion (53.14%), family communication (21.86%), family norms (14.86%), and family problem-solving (10.14%). With regard to the sense of well-being, the contributions of family communication (44.11%) and family problem-solving (27.19%) were more prominent than those of family emotion (17.39%) and family norms (11.31%). (3) Cultural background and school age served as moderating variables in the relationship between family functions and depression, while the publication time of literature, family function dimensions, and cultural background played moderating roles in the relationship between family functions and the sense of well-being.
  This study supports the theoretical propositions of process-oriented family functions, affirming the linear correlation between family functions and mental health. It systematically probed into the intrinsic relationship between family functions and children's mental health, thereby elucidating the pivotal position and unique role of its protective factors. It also unearthed the significant roles that family emotion and family communication play in children's mental health and discerned the distinctive influence of family problem-solving on the sense of well-being. The moderating effects in this study imply that the relationship between family functions and children's mental health harbors rich cultural diversity, pronounced stage characteristics, and dynamic temporal evolution.
  These discoveries can furnish valuable research directions for subsequent in-depth investigations, actively contribute to the precise definition and clarification of the concept of family functions. They can also offer a diverse range of practical bases for fields such as family education, family intervention, and family rehabilitation in the future, assist parents in more purposefully optimizing the family environment and implementing the concept of scientific parenting, and concurrently provide a focal point for family mental health education and related services.
Theories & History of Psychology
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  Intervention Effects of Automatic Emotion Regulation on Depressive Symptoms and Mechanisms

  Li Yaqin, Yuan Jiajin

  Journal of Psychological Science. 2025, 48(6): 1516-1526. https://doi.org/10.16719/j.cnki.1671-6981.20250618

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  One of the core cognitive features of depression is a lack of cognitive effort and difficulty in emotion regulation. Given that voluntary emotion regulation consumes significant cognitive resources, it may be less effective in downregulating depressive symptoms. Considering that automatic emotion regulation (AER) can reduce negative emotional experiences while consuming little or no cognitive resources, researchers have employed this approach as an intervention for depression. However, its effectiveness remains inconclusive. Therefore, this study systematically reviews previous research to explore the intervention effects of automatic emotion regulation and to examine its underlying mechanisms. Specifically, it first summarizes the intervention effects of automatic emotion regulation on depressive symptoms, explores the underlying mechanisms involved in these effects, and proposes new perspectives based on the findings.
  This study suggests that automatic emotion regulation is an effective intervention for depressive symptoms. Moreover, cognitive processes such as attention and memory, as well as neural activities in brain regions including the orbitofrontal cortex, amygdala, and anterior cingulate gyrus, might serve as key neural and psychological mechanisms.
  Future research should further investigate the roles of these regions in automatic emotion regulation for depressive symptoms. It is also important to consider individual differences that may affect intervention outcomes, particularly factors such as age, gender, and the severity of depressive symptoms. A substantial body of research suggests that these variables may influence intervention effectiveness. However, the specific ways in which age, gender, and the severity of depressive symptoms affect intervention outcomes remain unclear and require further exploration.
  In addition, future research should focus on the sustainability and generalization of automatic emotion regulation. The sustainability of AER refers to its capacity to exert lasting regulatory effects on negative emotions over time. The generalization effect of AER refers to the extension of regulation from specified to unspecified situations. Although the sustainability and generalization effects of automatic emotion regulation have been demonstrated in healthy populations, they have not been thoroughly examined in individuals with depression. Future studies should address this gap, including among patients with comorbid anxiety and depression.
  Nonetheless, numerous studies exploring automatic emotion regulation for depression have been conducted in laboratory settings, which limits their ecological validity. Thus, it is crucial to improve the ecological validity of automatic emotion regulation for depressive intervention. For instance, depressive individuals with impaired error monitoring have more difficulty coping with errors during uncertain situations, which potentially exacerbates their depressive symptoms. Therefore, understanding how depressive individuals cope with errors and regulate emotions in uncertain contexts is particularly important. However, no studies to date have examined error-processing characteristics of depressed individuals in uncertain situations and assessed intervention effectiveness. Future studies should address this gap. In addition, since automatic emotion regulation can serve as a potential intervention for depressive symptoms, it is important to examine its effects on depressive individuals in uncertain situations to enhance ecological validity. Furthermore, some studies have suggested that the integration of multiple psychotherapies may be more helpful for the intervention and treatment of depression. Thus, combining automatic emotion regulation with cognitive behavioral therapy or acceptance and commitment therapy may be a more effective intervention for depression.
  Finally, future studies should compare the intervention effects of various types of automatic emotion regulation strategies. Specifically, previous studies have primarily compared automatic emotion regulation with voluntary emotion regulation and control conditions, without examining differences among various AER strategies. In this regard, future studies should explore this, such as comparing strategies based on implementation intention with those based on goal priming. On the other hand, it is also necessary to compare the regulatory efficacy of different strategies in a specific form of automatic emotion regulation, such as comparing the effect of cognitive reappraisal and attentional distraction in the form of goal priming. These will contribute to a comprehensive understanding of automatic emotion regulation for depression interventions.