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The micro-dynamic neural processing model of insight problem-solving CHEN Yan, LI Ying, LIU Guanxiong, YU Quanlei, LIANG Zheng, CHEN Shi, ZHAO Qingbai Acta Psychologica Sinica    2026, 58 (4): 634-650.   DOI: 10.3724/SP.J.1041.2026.0634 Abstract （865）   HTML （16）    PDF （718KB）（112）       Knowledge map    Table and Figures | Reference | Related Articles | Metrics

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The Framing Effect of Cross-Period Temporal Choice in the Loss Domain Will Influence the Preference for Debt-Swapping Decisions MA Jia-Tao, LI Shu, HE Guibing Acta Psychologica Sinica    2026, 58 (4): 651-666.   DOI: 10.3724/SP.J.1041.2026.0651 Abstract （764）   HTML （6）    PDF （1625KB）（115）       Knowledge map    Framing effects, which violate the axioms of rational decision-making, represent a robust psychological phenomenon. This study investigates whether framing effects exist in cross-period temporal choice in the loss domain and explores their implications for debt swap policies. The findings reveal two key insights: (1) For a single debt program with a fixed total amount and maturity date, presentation frames significantly influenced debtors’ acceptance level of the debt repayment program. Specifically, low-frequency frames (e.g., annual payments) yielded a significantly higher acceptance level of the debt repayment program compared to high-frequency frames (e.g., weekly payments); (2) For binary-choice debt programs with constant total amounts but varying maturity dates, framing effects again proved significant. Compared to high-frequency frames/conventional timelines, low-frequency frames/compressed timelines made debtors more inclined to accept the initial debt program with higher interest rates and shorter terms. The observed preferences were consistent with predictions of the Graph-Edited Equate-to-Differentiate Model. This study contributes to a deeper understanding of cross-period temporal choice in the loss domain, enriches the “temporal nudge toolbox” with novel interventions, and provides crucial psychological evidence to inform the evaluation of debt swap policies and the optimization of debt management strategies. Table and Figures | Reference | Related Articles | Metrics

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Self-help AI psychological counseling system based on large language models and its effectiveness evaluation HUANG Feng, DING Huimin, LI Sijia, HAN Nuo, DI Yazheng, LIU Xiaoqian, ZHAO Nan, LI Linyan, ZHU Tingshao Acta Psychologica Sinica    2025, 57 (11): 2022-2042.   DOI: 10.3724/SP.J.1041.2025.2022 Abstract （755）   HTML （33）    PDF （1396KB）（3401）       Knowledge map    This study aimed to explore the technical feasibility of constructing a self-help AI psychological counseling system based on large language models without relying on real case data, and to evaluate its effectiveness in improving mental health outcomes in general populations. The research was conducted in two phases: First, we developed a self-help AI psychological counseling chatbot system using zero-shot learning and chain-of-thought prompting strategies; Subsequently, we evaluated the system's practical effectiveness through a two-week randomized controlled trial with 202 participants. Results from Experiment 1 demonstrated that the GPT-4o model, after prompt engineering optimization, showed significant improvements in Compliance, Professionalism, Emotional Understanding and Empathy, as well as Consistency and Coherence. Experiment 2 revealed that compared to the control group, participants using the self-help AI psychological counseling chatbot experienced significant short-term improvements in depression, anxiety, and loneliness. Notably, anthropomorphized AI counselors demonstrated significant advantages in alleviating loneliness, while non-anthropomorphized designs were more effective in reducing stress. Additionally, improvements in anxiety symptoms persisted at one-week follow-up, while improvements in other indicators did not sustain. This study preliminarily explores the positive impact of LLM-based self-help AI psychological counseling on mental health, revealing differential effects of various AI designs on specific psychological issues, and provides valuable insights for future research and practice. Table and Figures | Reference | Related Articles | Metrics

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The impact of marriage on life satisfaction trajectories during emerging adulthood: A ten-year longitudinal study based on China Family Panel Studies PENG Wang, YAN Ping, ZHOU Yaping, Xiang Yanhui Acta Psychologica Sinica    2025, 57 (12): 2149-2164.   DOI: 10.3724/SP.J.1041.2025.2149 Abstract （734）   HTML （27）    PDF （2672KB）（463）       Knowledge map    Marriage is widely debated regarding its impact on individuals' developmental trajectories of life satisfaction, a question of profound significance for both social well-being and personal quality of life. In collectivist societies such as China, marriage may involve distinct mechanisms related to familial responsibilities and social support. However, longitudinal empirical research examining marriage events and their trajectories of life satisfaction in the Chinese context remains scarce. Drawing on hedonic adaptation theory and the buffering model of social support, this study systematically investigated how marriage impacts life satisfaction trajectories across different age stages in emerging adulthood and examined the dual mediation mechanisms of positive affect and negative symptoms, utilizing a decade of large-scale longitudinal data from the China Family Panel Studies (CFPS, 2010-2020). Data from CFPS included a longitudinal sample of 1, 603 married participants and 5 778 matched unmarried controls aged 18~29 years. Propensity score matching (PSM) was conducted to form comparable married and unmarried groups based on demographic characteristics (gender, age, education, income, health status, and employment) and temporal factors (survey participation years and measurement frequency). Based on the matched sample, we fitted several distinct multilevel linear models, each using a different time indicator, to evaluate life satisfaction trajectories surrounding marriage events. Furthermore, we examined the mediating effects of positive affect and negative symptoms in two post-marriage time windows (0?2 years and more than 2 years after marriage). Results showed that marriage, on average, significantly and durably improved life satisfaction, with marked age heterogeneity: the effect was non-significant among individuals aged 18~21; those aged 22~25 exhibited a “rise-then-decline” pattern after marriage; and those aged 26~29 showed an anticipatory increase before marriage that remained stable thereafter. Mediation analyses further indicated a dual mechanism. Overall, sustained gains were jointly accounted for by increased positive affect and the buffering of negative symptoms; however, the mediating paths also varied by age: for ages 18~21, neither path was significant; for ages 22~25, mediation operated through elevated positive affect during the first 0~2 years post-marriage and shifted to buffering of negative symptoms beyond 2 years; and for ages 26~29, the effect was predominantly mediated by buffering of negative symptoms. Sex differences indicated similar overall gains for women and men, with men displaying a more pronounced anticipatory effect prior to marriage. The study systematically revealed how marriage influenced life satisfaction trajectories among emerging adults in China and suggested the potential dual-stage, dual-pathway mediation mechanisms behind this relationship, highlighting notable age-related heterogeneity. Theoretically, these findings challenged the traditional hedonic adaptation perspective of short-lived marital effects, expanding a dynamic understanding of marital happiness mechanisms within collectivist cultures. Practically, this research provides age- and mechanism-specific insights for marriage education and family policy interventions to enhance the well-being of young adults. Table and Figures | Reference | Related Articles | Metrics

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When design meets AI: The impact of AI design products on consumers’ response patterns LI Bin, RUI Jianxi, YU Weinan, LI Aimei, YE Maolin Acta Psychologica Sinica    2025, 57 (11): 1914-1932.   DOI: 10.3724/SP.J.1041.2025.1914 Abstract （697）   HTML （35）    PDF （3308KB）（6826）       Knowledge map    With the rapid development of Artificial Intelligence (AI) technology, utilizing AI to design products and innovate is a major trend in the future. Based on the stereotype content model, this article explored the effects, mechanisms, and boundary conditions of design source (human vs. AI) and product type (nostalgic vs. innovative) on consumer response patterns (appreciation vs. aversion) through six progressive Studies (N = 1418). The results showed that for nostalgic products, consumers preferred human design, showing AI aversion; for innovative products, consumers preferred AI design, showing AI appreciation, which produced a matching effect of “human design-nostalgic products” and “AI design-innovative products”. Further analysis revealed that processing fluency played a mediating role in this matching effect process; warmth perception and competence perception were key factors that led to processing fluency. In addition, the AI-human collaborative design mode, AI anthropomorphic features, and consumer self-construction types all played a moderating role. This article not only revealed the response patterns and deep mechanisms of consumers' appreciation or aversion towards different types of products designed by AI but also provided references for strategic planning and marketing strategies of AI+ design in the new era of artificial intelligence. Table and Figures | Reference | Related Articles | Metrics

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Human advantages and psychological transformations in the era of artificial intelligence WU Michael Shengtao, PENG Kaiping Acta Psychologica Sinica    2025, 57 (11): 1879-1884.   DOI: 10.3724/SP.J.1041.2025.1879 Abstract （618）            Knowledge map    In the era of artificial intelligence (AI), the boundaries between humans and machines have become blurred, and re-understanding and developing humanity's unique advantages are increasingly prominent and urgent. Meanwhile, with the rapid development of technology and scientific paradigm, a broad psychology encompassing the minds and behaviors of humans, animals, and machines is emerging. Recent researchers have conducted a series of studies on the psychology and governance of AI, from the perspectives of impacts of AI, new human-machine relationships, AI methods, and interdisciplinary empowerment. Future psychology researchers should focus on human society and future development, and reflect on the status of humanity and human dignity under the impact of AI, especially the unique advantages derived from human evolution as well as the expansions of human nature and identity; truly master and utilize AI technologies to empower the development of psychology, making mind research on the black box of human consciousness and complex social behavior more precise and efficient, and promoting AI-based mind computation and intervention across time and space scales and personalized interventions. More important, they must consider how psychology (with strengths in studying human nature, social relations, and ethical values) could empower the development of AI, by exploring AI cognition and its comparison with humans and animals, which is critical for promoting the AI application and governance in a human-machine symbiotic society. Reference | Related Articles | Metrics

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Human-AI cooperation makes individuals more risk seeking: The mediating role of perceived agentic responsibility GENG Xiaowei, LIU Chao, SU Li, HAN Bingxue, ZHANG Qiaoming, WU Mingzheng Acta Psychologica Sinica    2025, 57 (11): 1885-1900.   DOI: 10.3724/SP.J.1041.2025.1885 Abstract （552）   HTML （32）    PDF （935KB）（1834）       Knowledge map    With advancements in artificial intelligence (AI), artificial intelligence is increasingly becoming a “helper” for humans. In the process of human-AI cooperation risk decision-making, it is urgent to clarify whether artificial intelligence will encourage human risk-taking behavior and how perceived agentic responsibility can play a role. In order to investigate the impact and mechanism of human-AI cooperation on individual risk decision-making, four experiments were conducted. The results showed that: (1) Participants in the control group (i.e., without cooperation) exhibited the highest risk-taking behavior, while those engaged in human-AI cooperation took greater risks than those in human-human cooperation. (2) Individual agentic responsibility partially mediated the effect of human-AI cooperation on individuals’ risk decision-making. Specifically, participants reported a higher sense of agentic responsibility in human-AI cooperation compared to human-human cooperation, which contributed to increased risk-taking. (3) Outcome feedback significantly moderates the mediating role of individual agentic responsibility regarding the influence of human-AI cooperation (versus human-human cooperation) on individuals’ risk decision-making. Notably, under success conditions, participants attributed greater responsibility to themselves in human-AI collaboration compared to human-human collaboration. Conversely, under failure conditions, there was no significant difference in responsibility attribution between the two types of collaboration. Table and Figures | Reference | Related Articles | Metrics

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Moral deficiency in AI decision-making: Underlying mechanisms and mitigation strategies HU Xiaoyong, LI Mufeng, LI Yue, LI Kai, YU Feng Acta Psychologica Sinica    2026, 58 (1): 74-95.   DOI: 10.3724/SP.J.1041.2026.0074 Abstract （551）   HTML （40）    PDF （372KB）（360）       Knowledge map    As artificial intelligence (AI) assumes an increasingly prominent role in high-stakes decision-making, the ethical challenges it raises have become a pressing concern. This study systematically investigates the moral deficit effect in AI decision making by integrating mind perception theory with moral dualism. Through this framework, we identify a dual-path psychological mechanism and propose targeted intervention strategies. The findings show that individuals display significantly weaker moral reactions to unethical decisions made by AI than to those made by humans. This moral deficit effect arises because AI is perceived as having lower agency and experientiality compared to human decision-makers. To address this gap, the study proposes an integrated intervention strategy: enhancing moral responses through anthropomorphizing AI while simultaneously adjusting human expectations. Unlike disciplines that primarily focus on designing fair algorithms at the technical level, this study takes a psychological perspective. By highlighting differences in people’s moral judgments of AI versus human decisions, it provides new insights into mitigating the social consequences of algorithmic bias and contributes to the broader development of “algorithmic ethics.” Table and Figures | Reference | Related Articles | Metrics

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LLMs Amplify Gendered Empathy Stereotypes and Influence Major and Career Recommendations* DAI Yiqing, MA Xinming, WU Zhen Acta Psychologica Sinica    2026, 58 (3): 399-415.   DOI: 10.3724/SP.J.1041.2026.0399 Abstract （522）   HTML （21）    PDF （1257KB）（277）       Knowledge map    Large language models (LLMs) are increasingly deployed in highly sensitive domains such as education and career guidance, raising concerns about their potential to reproduce and amplify social biases. The present research examined whether LLMs exhibit gendered empathy stereotypes—specifically, the belief that “women are more empathetic than men”—and whether such stereotypes influence downstream recommendations. Three studies were conducted. Study 1 compared LLMs with human participants and found that across six leading LLMs, gendered empathy stereotypes were significantly stronger than those observed in humans across three facets of empathy: emotional empathy, attention to others’ feelings, and behavioral empathy. Study 2 manipulated input language (Chinese vs. English) and gender-identity priming (male vs. female), demonstrating that English prompts and female priming elicited stronger gendered empathy stereotypes. Study 3 focused on major and career recommendation tasks and revealed that LLMs systematically recommended high-empathy majors and professions to women, while directing men toward low-empathy fields. Together, these findings indicate that LLMs exhibit pronounced gendered empathy stereotypes, that these biases vary across input context, and that they can transfer into real-world recommendation scenarios. This research offers theoretical insights into bias formation in LLMs and provides practical implications for improving fairness in AI systems used in educational and career guidance. Table and Figures | Reference | Related Articles | Metrics

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Stimulus similarity modulated sensory dominance effects in cross-modal conflicts WANG Aijun, HUANG Jie, ZHAO Danna, LI Xin, ZHANG Ming Acta Psychologica Sinica    2026, 58 (4): 571-589.   DOI: 10.3724/SP.J.1041.2026.0571 Abstract （521）   HTML （45）    PDF （1188KB）（240）       Knowledge map    The levels-of-processing framework posits that cross-modal conflicts demonstrate modality-specific dominance patterns, with visual dominance effect occurring at pre-response stages and auditory dominance effect emerging at response stages. However, little studies systematically examined how the representational modalities of stimuli during cognitive processing modulate these sensory dominance effects. The present study used a 2-1 mapping paradigm to investigate how stimulus similarity influences sensory dominance effects at both the pre-response and response levels. Experiment 1 revealed that visual dominance effect emerged during pre-response cross-modal conflicts, whereas auditory dominance effect manifested at the response level. Moreover, visual similarity significantly reduced visual dominance effect at the pre-response level and auditory dominance effect at the response level. In contrast, auditory similarity markedly enhanced visual dominance effect at the pre-response level. Experiment 2 used transcranial direct current stimulation to modulate neural activity in the left fusiform gyrus (Experiment 2a) and the left inferior parietal lobule (Experiment 2b), thereby causally testing how stimulus similarity influences sensory dominance effects in cross-modal conflict. Experiment 2a indicated that anodal stimulation reduced the visual dominance effect at the pre-response level, whereas Experiment 2b showed that anodal stimulation increased the visual dominance effect at the same processing stage. Overall, the present study demonstrates that stimulus similarity modulates sensory dominance effect in cross-modal conflicts, with visual and auditory similarity differentially modulating sensory dominance effects at the pre-response level. These findings provide novel insights into the mechanisms of cross-modal conflict across distinct cognitive processing stages and advance the understanding of sensory dominance effects in multisensory contexts. Table and Figures | Reference | Related Articles | Metrics

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Humans perceive warmth and competence in large language models WU Yueting, WANG Bo, BAO Han Wu Shuang, LI Ruonan, WU Yi, WANG Jiaqi, CHENG Cheng, YANG Li Acta Psychologica Sinica    2025, 57 (11): 2043-2059.   DOI: 10.3724/SP.J.1041.2025.2043 Abstract （512）   HTML （19）    PDF （769KB）（5966）       Knowledge map    With the continuous advancement of technical capabilities and the extensive penetration of application scenarios of Large Language Models (LLMs), the structure of social interaction is transitioning from the traditionally single interpersonal interaction to a multi-level system integrating interpersonal interaction, human-machine interaction, and machine-machine interaction. In this context, understanding how humans perceive and evaluate LLMs has become an important issue. This research systematically examines the perception patterns of LLMs by humans through three studies. Study 1 found that, consistent with how humans perceive other humans, humans primarily perceive LLMs through two dimensions: warmth and competence. However, in general contexts, unlike the warmth priority in human perception, humans prioritize competence when perceiving LLMs. Study 2 explored the priority effect of warmth and competence in predicting different attitudes. The results show that both warmth and competence positively predict humans’ willingness to continue using LLMs and their liking of LLMs, with competence having a stronger predictive effect on willingness to continue using, and warmth having a stronger predictive effect on liking. Study 3 further explored the differences in human perception of LLMs and others. The results show that humans’ warmth evaluations of LLMs do not differ significantly from those of humans, but their competence evaluations of LLMs are significantly higher than those of humans. This study provides a theoretical basis for understanding human perception of LLMs and offers a new perspective for the design optimization of artificial intelligence and the study of human-machine collaboration mechanisms. Table and Figures | Reference | Related Articles | Metrics

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Personality-based alignment of large language models and its impact on moral judgment LI Chang-Jin, JIAO Liying, CHEN Zhen, XU Hengbin, WU Michael Shengtao, XU Yan Acta Psychologica Sinica    2026, 58 (7): 1237-1253.   DOI: 10.3724/SP.J.1041.2026.1237 Abstract （434）   HTML （39）    PDF （626KB）（137）       Knowledge map    With the advent of the era of human−machine symbiosis, the ethical dilemmas and algorithmic biases of large language models (LLMs) have sparked widespread societal concern. Consequently, guiding artificial intelligence technology toward beneficial development has emerged as a urgent and challenging imperative. This research explores the impact of personality-based alignment grounded in the HEXACO personality model on the moral judgment of LLMs. Specifically, Study 1 examined and verified that LLMs can effectively manifest HEXACO personality traits by adhering to prompts. Study 2 explored the influence of personality-based alignment on the utilitarian tendencies of LLMs, as well as the similarities and differences compared to human participants. The results indicate that personality prompts characterized by high Honesty−Humility, Agreeableness, and Conscientiousness significantly reduce the propensity of GPT-3.5, GPT-4, and ERNIE 3.5 to make utilitarian choices. Accordingly, we propose an LLM personality-based alignment framework grounded in both the HEXACO personality model and the personality metatrait, highlighting the moral salience effect of personality dimensions within the Stability metatrait—namely, Honesty−Humility, Agreeableness, and Conscientiousness—in the personality-based alignment of LLMs. This research provides a psychological foundation for the theoretical construction and technological pathways of LLM personality-based alignment. Table and Figures | Reference | Related Articles | Metrics

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Category and semantic distance modulate the impact of prediction on memory DAI Jiaojian, SUN Mingze, WANG Dongfang, MAO Xinrui, GUO Chunyan Acta Psychologica Sinica    2026, 58 (1): 1-14.   DOI: 10.3724/SP.J.1041.2026.0001 Abstract （431）   HTML （34）    PDF （942KB）（231）       Knowledge map    Table and Figures | Reference | Related Articles | Metrics

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Emotional capabilities evaluation of multimodal large language model in dynamic social interaction scenarios ZHOU Zisen, HUANG Qi, TAN Zehong, LIU Rui, CAO Ziheng, MU Fangman, FAN Yachun, QIN Shaozheng Acta Psychologica Sinica    2025, 57 (11): 1988-2000.   DOI: 10.3724/SP.J.1041.2025.1988 Abstract （428）   HTML （13）    PDF （5320KB）（395）       Knowledge map    Multimodal Large Language Models (MLLMs) can process and integrate multimodal data, such as images and text, providing a powerful tool for understanding human psychology and behavior. Combining classic emotional behavior experimental paradigms, this study compares the emotion recognition and prediction abilities of human participants and two mainstream MLLMs in dynamic social interaction contexts, aiming to disentangle the distinct roles of visual features of conversational characters (images) and conversational content (text) in emotion recognition and prediction. The results indicate that the emotion recognition and prediction performance of MLLMs, based on character images and conversational content, exhibits moderate or lower correlations with human participants. Despite a notable gap, MLLMs have begun to demonstrate preliminary capabilities in emotion recognition and prediction similar to human participants in dyadic interactions. Using human performance as a benchmark, the study further compares MLLMs under different conditions: integrating both character images and conversational content, using only character images, or relying solely on conversational content. The results suggest that visual features of character interactions somewhat constrain MLLMs’ basic emotion recognition but effectively facilitate the recognition of complex emotions, while having no significant impact on emotion prediction. Additionally, by comparing the emotion recognition and prediction performance of two mainstream MLLMs and different versions of GPT-4, the study finds that, rather than merely increasing the scale of training data, innovations in the underlying technical framework play a more crucial role in enhancing MLLMs’ emotional capabilities in dynamic social interaction contexts. Overall, this study deepens the understanding of the interaction between human visual features and conversational content, fosters interdisciplinary integration between psychology and artificial intelligence, and provides valuable theoretical and practical insights for developing explainable affective computing models and general artificial intelligence. Table and Figures | Reference | Related Articles | Metrics

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The personality structure and cultural conceptualization of humility in Chinese culture WANG Xin, WU Yanhong Acta Psychologica Sinica    2026, 58 (7): 1405-1427.   DOI: 10.3724/SP.J.1041.2026.1405 Abstract （427）   HTML （4）    PDF （1098KB）（337）       Knowledge map    Humility is a traditional virtue esteemed in Chinese culture, exerting positive effects on individuals, organizations, and society. Combining qualitative research, corpus compilation, and a review of previous scales, this study constructed the structure of the humility trait within Chinese culture and developed the Chinese Humility Scale. The scale comprises five dimensions: Accurate Self-Perception, Other-Enhancement, Dialectical Thinking, Self-Effacement, and Self-Improvement. Based on this five-dimensional structure, Latent Profile Analysis was utilized, revealing five distinct profiles of humility manifestations in Chinese culture: “Humble,” “Moderately Humble,” “Overly Humble,” “Unhumble,” and “Strategically Humble.” This research clarifies the conceptual connotations of humility within Chinese culture and provides a novel measurement tool for future empirical studies, as well as for the identification and selection of well-rounded talents possessing both competence and humility. Furthermore, it plays a positive role in inheriting and promoting the traditional virtue of humility while facilitating individual development and social harmony. Table and Figures | Reference | Related Articles | Metrics

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The impact of anthropomorphism on perceived warmth-competence of AI and human-AI cooperation intention CUI Xunxuan, QIAO Ziteng, LIU Ning Acta Psychologica Sinica    2026, 58 (7): 1254-1278.   DOI: 10.3724/SP.J.1041.2026.1254 Abstract （422）   HTML （13）    PDF （1060KB）（344）       Knowledge map    With the rapid development of artificial intelligence (AI), human-AI collaboration has garnered increasing attention. Although anthropomorphism is widely regarded as an important strategy to facilitate human-AI interaction, its effects are inconsistent and sometimes even counterproductive. Through six sequential studies, this research investigated the effect of anthropomorphism on human-AI collaboration intention, revealed the underlying psychological mechanism from a social cognitive perspective, and further examined the moderating roles of perceived threats (i.e., realistic threat and uniqueness threat). Studies 1a and 1b adopted a passive text-based manipulation and found that anthropomorphism indirectly promoted collaboration intention by enhancing perceived warmth. Study 2 used an active imagination manipulation and found that anthropomorphism simultaneously increased perceived warmth, perceived competence, and collaboration intention, with perceived warmth and competence serving as the mediating mechanism. Study 3a replicated the pathway through which anthropomorphism influences collaboration intention via perceived warmth and competence. Study 3b further found that even under high realistic threat, the mediating pathways of perceived warmth and competence remained significant, and realistic threat did not exert a moderating effect. Study 4 manipulated anthropomorphism using robotic appearance images and found that appearance anthropomorphism negatively affected human-AI collaboration intention, and this effect was moderated by perceived uniqueness threat. These findings help answer the critical questions of how and when to anthropomorphize in human-AI collaboration and provide theoretical references for optimizing the anthropomorphic design of AI and promoting human-AI collaboration. Table and Figures | Reference | Related Articles | Metrics

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Desire for success but “lying flat”: The conflicting consequences of economic inequality and its mechanisms MAO Jia-Yan, YANG Shen-Long, TIAN Cai-Yu Acta Psychologica Sinica    2026, 58 (2): 247-263.   DOI: 10.3724/SP.J.1041.2026.0247 Abstract （420）   HTML （24）    PDF （543KB）（456）       Knowledge map    Previous research has shown that economic inequality increases individuals’ desire for wealth and status. However, it is unclear whether this desire translates into concrete, effortful action. Across six studies, the present research reveals a psychological ambivalence elicited by economic inequality: while it fosters a stronger desire for wealth and status, it simultaneously triggers a tendency toward “lying flat”. Studies 1 and 2 employed correlational methods and demonstrated that economic inequality predicts both outcomes. Specifically, status anxiety mediates the association between economic inequality and the desire for wealth and status, whereas perceived control mediates the relationship between economic inequality and lying flat tendency. Studies 3 and 4 (including 4a and 4b) utilized experimental designs, providing causal evidence for both effects and their underlying mechanisms. Given the adverse implications of lying flat tendency, Study 5 further examined potential moderating variables and found that perception of social mobility moderates the mediating pathway from economic inequality to lying flat tendency via perceived control. This research extends prior findings in the field and highlights the conflicting psychological consequences of economic inequality and their mechanisms. Moreover, it offers new insights for mitigating the negative outcomes of perceived inequality and sheds light on how to promote individual effort and facilitate upward social mobility. Table and Figures | Reference | Related Articles | Metrics

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Hearts correspond, love follows: The relationship between couples’ similarity and marital satisfaction SU Wei, FANG Xiaoyi, HOU Juan Acta Psychologica Sinica    2026, 58 (2): 350-361.   DOI: 10.3724/SP.J.1041.2026.0350 Abstract （416）   HTML （17）    PDF （460KB）（158）       Knowledge map    Table and Figures | Reference | Related Articles | Metrics

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Make your emotions heard: How emotion sharing boosts interpersonal emotion regulation across various interpersonal distances LI Sijin, TANG Yuyao, LI Qi, WANG Tingdong, ZHANG Dandan Acta Psychologica Sinica    2026, 58 (5): 840-852.   DOI: 10.3724/SP.J.1041.2026.0840 Abstract （400）   HTML （20）    PDF （914KB）（454）       Knowledge map    Emotional support from friends or strangers serves as a crucial resource for individuals coping with emergencies. However, assistance provided by strangers is often less effective than that offered by friends. How does interpersonal distance influence the regulator’s regulatory process? Can emotion sharing by the target mitigate the adverse effects of greater interpersonal distance? This study investigated the roles of interpersonal distance and emotion sharing in interpersonal emotion regulation (IER) through two experiments. Experiment 1 revealed that regulators selected more contextually adaptive strategies for friends than for strangers. Experiment 2 further measured changes in regulators’ pupil diameter as an index of cognitive resource engagement. The results showed that interpersonal distance influenced regulators across three key phases: emotion identification, strategy selection, and strategy output. Importantly, explicit emotion sharing by the target significantly reduced the negative effects of interpersonal distance, thereby optimizing IER processes between strangers. This study not only supports and extends the Social Regulatory Cycle Model but also provides practical guidance for optimizing IER processes and enhancing the quality of social interactions. Table and Figures | Reference | Related Articles | Metrics

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Perceived unsustainability decreases acceptance of artificial intelligence WEI Xinni, YU Feng, PENG Kaiping Acta Psychologica Sinica    2025, 57 (11): 1973-1987.   DOI: 10.3724/SP.J.1041.2025.1973 Abstract （392）   HTML （21）    PDF （629KB）（647）       Knowledge map    Artificial intelligence (AI) has the potential to facilitate ecological governance and promote sustainable development. However, it also rapidly consumes energy and generates significant carbon emissions, posing challenges to both the natural environment and human survival. Despite these concerns, little research has examined the environmental costs of AI and how people respond to them. This study investigates how perceptions of AI sustainability influence individuals’ willingness to adopt AI in human-machine- environment decision-making contexts, as well as the underlying mechanisms and boundary conditions of this effect. Using a survey and AI-generated attitude descriptors from ChatGPT, a pilot study revealed generally high usage intentions and positive attitudes toward environmentally friendly AI systems. Study 1, consisting of two sub-studies, manipulated perceived AI sustainability (low vs. control) and found that participants exposed to low-sustainability AI reported lower acceptance and reduced support for national AI research. Study 2, using an experiment manipulating the perception of sustainability (low vs. high), replicated these findings and identified moral judgment, rather than agency attribution, as the mediating mechanism. Study 3 explored potential boundary conditions, demonstrating that individuals’ pro-environmental attitudes moderated the observed effects. These findings provide psychological insights into the social governance of AI and offer new perspectives on the relationship between AI and sustainable development. Table and Figures | Reference | Related Articles | Metrics