Advances in Psychological Science

Neurophysiological mechanisms and interventions of subthreshold depression by integrating machine learning techniques

LIU Yongjin, YANG Xue, DU Xinxin, JI Wenqi, ZANG Yinyin, GUAN Ruiyuan, SONG Sen, QIAN Mingyi, MU Wenting

2025, 33 (6): 887-904. doi: 10.3724/SP.J.1042.2025.0887

Abstract ( 62 )

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Major Depressive Disorder (MDD) is a significant global public health threat for both national mental health, causing significant societal burden for both individuals and families. Subthreshold depression (StD) is an ultra-high-risk phase for MDD. Understanding its neurophysiological mechanisms and dynamic development patterns can help predict MDD onset and inform the development of novel and effective preventive interventions. To overcome the limitation of regarding depression as a static and single diagnosis, this study uses dynamical systems theory and machine learning techniques to explore the StD’s neurophysiological mechanisms. By combining resting-state, task-state evaluations, and clinical interviews, it constructs a neurodynamic network model for StD with multi-modal data and machine learning, followed by cross-population and cross-individual validation.
Firstly, using multi-modal data, Study 1 explores the differences in symptoms and neurophysiological mechanisms among individuals with StD, healthy, and MDD individuals from a population perspective, and then, identifies critical predictive neurophysiological indicators of StD. By combining resting-state and task-state evaluations, as well as clinical interviews, it explores the distinctiveness of symptoms and multi-modal data in StD individuals (versus healthy and MDD). And the neurodynamic network model for StD will be constructed using multi-modal data combined with machine learning techniques to analyze depressive symptoms and neurophysiological characteristics, followed by cross-population and cross-individual validation. at the individual level, Study 2 use ecological momentary assessment combined with follow-up tracking to investigate the relationship between the attractor state of subthreshold depression and subsequent transition to MDD, along with accompanying neurophysiological changes. A dynamic development prediction model will be constructed. This study utilizes longitudinal tracking of multi-modal data combined with machine learning techniques to measure the attractor of StD and explore its predictive ability for subsequent depressive states and neurophysiological characteristic changes. Finally, to overcome the limitations of evaluating the preventive intervention effects for StD mainly relied on clinical diagnostic results, this study explores the effectiveness of cognitive behavioral therapy (CBT) in preventing the progression of StD from a commonality perspective, with the depressive symptoms and neurophysiological characteristics as objective evaluation indicators of intervention effectiveness. Furthermore, it elucidates the predictive role of the individual's attractor state in the future transformation of subthreshold depression. Study 3 uses a randomized controlled trial to investigate the preventive intervention efficacy of CBT group (versus waiting list group) on subthreshold depression and the mediating role of the attractor state. Based on the above study, this study proposes a predictive hypothesis regarding the preventive effect of cognitive behavioral therapy (CBT) on subthreshold depression based on the dynamical systems theory. It is hypothesized that CBT can reduce depressive symptoms and the transition rate by enhancing the stability of the attractor state, and this effect can be measured by neurophysiology characteristics.
In conclusion, this study offers a dynamic-systems-theory-driven approach, integrating multi-modal machine learning techniques to analyze the neurophysiological uniqueness of StD. This study helps develop more precise models for predicting StD and evaluating preventive intervention effects based on neurophysiological characteristics obtained from various sensors. These results can be used for daily physiological assessments, early detection of emotional or mental states earlier deterioration, and development of more precise early diagnosis and prevention strategies for MDD.

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Promoting or inhibiting? The double-edged sword effect of acceptance of generative AI advice on creativity

ZONG Shuwei, YANG Fu, LONG Lirong, HAN Yi

2025, 33 (6): 905-915. doi: 10.3724/SP.J.1042.2025.0905

Abstract ( 97 )

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Generative artificial intelligence (GAI), as an emerging artificial intelligence technology, has showcased remarkable creativity across diverse domains. However, members of an organization are the main carriers of corporate innovation and creativity. A pivotal question persists in academic circles: Will the integration of GAI technology into the workplace and the promotion of its work suggestions or solutions among organizational members stimulate or stifle their creativity? This question remains largely unexplored, particularly regarding the phenomenon of advice taking from GAI within the realm of organizational management. This study aims to address this significant gap. Initially, grounded in the context of organizational management, we elucidate the connotation and dimensions of advice taking from GAI, addressing the fundamental inquiry: “what constitutes advice taking from GAI?”. Subsequently, we investigate the double-edged sword effects of advice taking from GAI on creativity, examining its effects at both the employee and team levels through the lenses of social cognition and goal orientation. We endeavor to clarify the issue, “what is the relationship between advice taking from GAI and creativity?”. Finally, we synthesize an exploration of the boundary conditions for the effect of advice taking from GAI, seeking to answer: “under what circumstances does advice taking from GAI yield positive outcomes?”
This study makes three contributions. First, we explore the dimensions and measurement of the construct of advice taking from GAI, paving the way for new research directions in advice taking. In the digital era, AI has become a novel source for organizations to acquire unique information and generate personalized recommendations. Conducting in-depth academic research in this area contributes to a better understanding of the effectiveness of advice taking from GAI. Unfortunately, existing studies primarily focus on the immediate effects of GAI on idea generation, neglecting that the application of GAI in organizations is a multi-stage process, rather than a simple tool usage. In response, study 1 introduces a refined framework comprising three stages: advice solicitation, advice evaluation, and advice adoption, highlighting the iterative interaction process between organizational members and GAI. By investigating how organizational members gradually adopt GAI advice and examining the impact of this process on employee creativity, this study broadens the scope of research on the relationship between AI techniques and human creativity. It also underscores the importance of focusing on the iterative processes of screening, evaluating, adjusting, and applying AI advice and the subsequent outcomes.
Second, from the approach of social cognitive and goal orientation, this study investigates the double-edged sword effect of individual/team advice taking from GAI on individual/team creativity and its underlying mechanisms, expanding the research perspective on the advice taking from GAI. A review of the literature reveals that most existing studies on AI adoption focus on its influencing factors and formation mechanisms, with limited discussion on the effects of AI adoption. In the few studies related to GAI and organizational creativity, the primary focus is on the individual-level use of GAI and its related outcomes, without systematically, comprehensively, or multi-levelly exploring the mechanisms through which advice taking from GAI influences creativity. To address this gap, study 2 and study 3, respectively, examine how the differentiated impacts of advice taking from GAI on individuals’ cognitive states and teams’ goal orientations drive divergent outcomes for organizational creativity. Specifically, the double-edged sword effect of advice taking from GAI on creativity is explored. This reveals the complex effect mechanism of GAI in different contexts, particularly the dynamic balance between individuals’ sense of efficacy and dependence on AI, as well as the competing orientations of teams toward learning goals and performance goals.
Third, this study systematically identifies corresponding intervention strategies from multiple organizational levels to enhance the positive effects of advice taking from GAI on creativity while mitigating its negative effects. Existing research on the boundary conditions of AI usage effects primarily focuses on individual-level characteristics, with limited attention to the intervention effects of organizational-level factors. As both the practical bearer of AI application outcomes in the workplace and a critical component of AI governance frameworks, organizations hold significant governance responsibilities. In this context, this study proposes intervention strategies from two perspectives: resource support and feedback mechanisms, to examine the boundary conditions under which advice taking from GAI exerts positive or negative effects on creativity. These findings help clarify the organizational levels at which interventions are needed to influence members’ advice taking from GAI, providing decision-makers with valuable references.

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The bidirectional trust in the context of new human-machine relationships

XIE Yubin, ZHOU Ronggang

2025, 33 (6): 916-932. doi: 10.3724/SP.J.1042.2025.0916

Abstract ( 66 )

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In the context of the rapid development of artificial intelligence, the relationship between humans and machines is shifting from the traditional “auxiliary-subordinate” model to a “equal collaboration” or even “symbiotic coevolution” model. Most of research has mainly focused on one-directional trust from humans to machines, but as intelligent agent gains greater autonomy and decision-making capabilities, mutual trust is becoming a central issue in human-machine collaboration. This paper examines the mechanisms for building mutual trust between humans and machines. It also explores measurement methods, and the practical challenges involved. The goal is to provide theoretical support for the design and optimization of future intelligent systems.
Building on existing human-machine trust frameworks, this paper proposes a dynamic model of mutual trust. It introduces a three-stage model: “dispositional trust-perceived trust-behavioral trust,” which covers both human-to-machine and machine-to-human trust. The model emphasizes perceived trust as the key bridge between dispositional trust and behavioral trust, highlighting its role in the transfer of trust between AI, intelligent agents, and humans. Dispositional trust: The initial stage of trust, which is based on an individual’s inherent traits and is independent of specific contexts. It lays the foundation for the subsequent development of trust. Perceived trust: Gradually formed during interaction, this stage reflects the dynamic perception of the other party’s behavior, attitude, and trustworthiness. It is the core of emotional trust transfer and dynamic adjustment. Behavioral trust: The final manifestation of trust, expressed through concrete behaviors such as dependence, cooperation, and action. It is post-action trust based on behavioral feedback, reflecting the ultimate outcome of the trust relationship.
The advantages of this model are reflected in several key aspects. First, its dynamic evolutionary characteristics allow the model to fully capture the development of trust from dispositional trust to perceived trust and finally to behavioral trust, effectively accommodating the complexity and variability of trust relationships in human-machine interactions. Second, the model emphasizes bidirectional trust transfer, focusing on the interaction between humans and intelligent agents. It highlights the role of perceived trust as the crucial bridge between dispositional trust and behavioral trust, offering in-depth insights into its significance in emotional trust transfer and dynamic adjustment, thus providing unique guidance for optimizing human-machine interaction. Third, the model introduces an expanded perspective on dispositional trust by incorporating algorithmic trust, exploring the sources of initial trust in algorithms and the impact of individual algorithm aversion, thereby offering a new theoretical foundation for algorithmic trust research. Lastly, the model provides an in-depth analysis of behavioral trust, emphasizing the impact of machine behavior on human-machine trust, such as the negative effect on “perceived trustworthiness” when a machine denies a human request, and revealing the emotional and behavioral consequences of trust misalignment.
The purpose of this theoretical model is to develop methods for measuring and modeling human-machine mutual trust based on the characteristics of different scenarios. Building on a review of existing measurement methods and drawing from interpersonal trust measurement experience, this paper introduces a framework and methods for mutual trust between humans and machines. The study focuses on several key areas: developing stage-specific measurement tools for dispositional trust, perceived trust, and behavioral trust; exploring multidimensional, multilevel methods that combine subjective reports, physiological signals, and behavioral data to create a dynamic monitoring and calibration system; and adapting interpersonal trust quantification methods to design trust modeling tools suited to human-machine interactions. Ultimately, this research aims to provide a systematic, operable framework for measuring and modeling mutual trust, laying the foundation for dynamic evaluation and intelligent adjustment.
In terms of application, this paper examines the practical value of mutual trust through case studies in autonomous driving and aviation. It also discusses current challenges, such as individual differences that hinder trust development, the lack of standardized tools for measuring machine trust, and the unclear long-term psychological effects of mutual trust on users. The paper calls for further research to refine trust measurement tools, address issues of “over-trust” or “mistrust” in human-machine trust alignment and define the boundaries of machine trust behavior within ethical and legal frameworks. By integrating theoretical and methodological innovations, this paper offers new directions for research on trust mechanisms in human-machine collaboration and provides valuable guidance for the development of efficient and safe intelligent systems.

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Workplace artificial intelligence role classification: Impacts on employee psychology and behavior and coping strategies

TAN Meili, YIN Xiangzhou, ZHANG Guanglei, XIONG Puzhen

2025, 33 (6): 933-947. doi: 10.3724/SP.J.1042.2025.0933

Abstract ( 83 )

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Artificial intelligence (AI) has become an integral part of modern workplaces. However, understanding its impact on employees remains a complex and dynamic area of research. Through a comprehensive review of relevant literature and taking into account the practical applications of AI, this study innovatively approaches the topic. It classifies AI's roles in the workplace from the employees' perspective into four distinct types: “opponent”, “assistant”, “colleague”, and “leader”. This sets our study apart from previous research, as we place greater emphasis on the employee-centered perspective.
Based on a review of the literature, this classification is based on the “substitution-aid-augmentation-management” framework. From this perspective, the study systematically examines how these different AI roles influence employees' psychological states and behaviors. When AI functions as an “opponent”, it triggers job-related anxiety among employees. The fear of replacement can lead to negative behaviors such as service disruptions and knowledge hoarding. Conversely, if employees perceive this threat as a challenge, it can stimulate creativity. As an “assistant”, AI enhances work performance by reducing employees' cognitive load. Nevertheless, over-reliance on it may weaken employees' moral judgment and limit their access to diverse information. In the role of a “colleague”, AI collaborates with employees on complex tasks, but it also gives rise to issues like ambiguous job responsibilities and decreased interpersonal communication. When AI assumes the “leader” role, although its precise management has certain advantages, it also raises ethical concerns. Its impersonal communication style and potential privacy invasions can cause stress among employees, and algorithm-based management may undermine employees' creativity and job satisfaction.
To address these impacts, the study proposes practical strategies for both organizations and individuals. At the organizational level, companies should provide AI-related training to help employees acquire the necessary skills and adapt to changes. Fostering a positive AI-friendly work culture can guide employees to utilize AI effectively and mitigate its negative effects. Additionally, establishing clear AI usage rules and ethical standards is essential for safeguarding employees' rights. At the individual level, employees need to actively develop skills that are difficult for AI to replicate, such as creativity and social skills, and continuously enhance their AI literacy. They should also shift their mindset towards AI, viewing it as an opportunity rather than a threat, to better integrate into the new workplace environment.
Combining the current research status and the role-by-role analysis presented earlier, we also point out several directions for future research.
First, we can further explore and refine the classification of AI in the Chinese context. Based on China's unique cultural, economic, and social environment, a more precise classification will provide a better understanding of how AI functions in Chinese workplaces. Next, we can explore the boundary conditions of such impacts. These boundary conditions include factors like organizational culture, employee characteristics, and technological maturity. Third, conducting comparative studies can reveal commonalities and differences in how AI affects employees in different contexts. This can be valuable for organizations operating globally or seeking to learn from different practices. Fourth, analyzing the differential impacts of AI across various industries and job functions helps understand its unique influences in different work settings. Different industries and job roles interact with AI in distinct ways, and this research can offer targeted insights. Fifth, conducting comparative studies can reveal commonalities and differences in how AI affects employees in different contexts. This can be valuable for organizations operating globally or seeking to learn from different practices. Finally, strengthening interdisciplinary research, which integrates knowledge from multiple fields like psychology, computer science, and management, offers a more comprehensive understanding.
This study contributes to the existing literature by offering a comprehensive classification of AI roles in the workplace and elucidating their differential impacts on employees. By meticulously examining the four roles of AI in the workplace from the employees' perspective, it fills gaps in existing literature and offers a more comprehensive view of their interactions. It also provides practical insights for organizations and individuals to better adapt to the evolving role of AI in the workplace. Future research should further explore the boundary conditions of these impacts, investigate cross-cultural differences in employee responses to AI, and examine the long-term effects of AI on career development and work-life balance.

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The influence of algorithmic human resource management on employee algorithmic coping behavior and job performance

XI Meng, LIU Yue-Yue, LI Xin, LI Jia-Xin, SHI Jia-Zhen

2025, 33 (6): 948-964. doi: 10.3724/SP.J.1042.2025.0948

Abstract ( 56 )

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Algorithmic human resource management (HRM) is an emerging research field that combines artificial intelligence (AI) with HRM, representing a transformative shift in the field of strategic HRM and emphasizing the use of data-driven algorithms to enhance decision-making processes and optimize workforce management. While its operational benefits are widely recognized, its deeper implications for employee job performance remain underexplored, particularly in the context of employees’ perceptions, trust, and behavioral adaptations to algorithmic systems. This study addresses these gaps by offering a nuanced theoretical framework that investigates the mechanisms through which algorithmic HRM influences employee job performance by examining the mediating role of employees’ cognitive and emotional responses, as well as their algorithmic coping behaviors.
This research builds on structuration theory to explore the duality of technology and human agency in algorithmic HRM. Specifically, it positions employees not merely as passive recipients of algorithm-driven decisions but as active agents who interpret, adapt, or resist these technologies. By integrating structuration theory’s emphasis on the interplay between structural constraints and human agency, this study highlights how employees’ perceptions of algorithmic transparency, fairness, and trust shape their cognitive, emotional, and behavioral responses. Furthermore, it underscores the importance of balancing algorithmic efficiency with ethical considerations to sustain employee engagement and organizational legitimacy.
The innovative contributions of this study include a differentiation between the impacts of algorithmic HRM on in-role performance and extra-role performance. The study theorizes that while algorithmic precision and real-time feedback enhance task performance by providing clear metrics and actionable insights, perceptions of fairness and transparency are critical for fostering trust and encouraging extra-role behaviors. This dual focus on performance dimensions provides a more holistic understanding of algorithmic HRM’s effects, addressing prior research limitations that predominantly focus on operational efficiency.
The study proposes several mechanisms through which algorithmic HRM influences employee performance. First, employees’ perceptions of fairness and trust in algorithmic decision-making processes act as critical mediators. Transparent algorithms enhance trust, reduce resistance, and encourage engagement, while opaque or biased algorithms can elicit skepticism and hinder performance. Second, algorithmic HRM directly improves in-role performance by providing precise, data-driven guidance and individualized feedback. In contrast, extra-role performance, such as helping behaviors, relies heavily on employees’ perceptions of algorithmic fairness and the degree to which algorithms respect individual circumstances. Third, the study categorizes employees’ behavioral adaptations into three types: adaptation, resistance, and manipulation. Employees who adapt to algorithmic systems are more likely to achieve high in-role performance, while those who resist may experience diminished productivity. Manipulative behaviors, such as exploiting algorithmic vulnerabilities, may yield short-term gains but often undermine long-term performance and organizational trust.
The study identifies several avenues for future research to expand the understanding of algorithmic HRM. First, future research could explore the sustained impacts of algorithmic HRM on employee performance, examining how trust and engagement evolve over time and under varying organizational contexts. Second, comparative analyses of different algorithmic HRM systems (e.g., predictive vs. evaluative algorithms) could reveal their unique effects on employee cognition, emotions, and behaviors, offering insights into their strengths and limitations for in-role and extra-role performance. Investigating the moderating effects of individual characteristics (e.g., personality traits, openness to change) and cultural contexts could deepen our understanding of how employees from diverse backgrounds interact with algorithmic systems and how these differences influence the effectiveness of algorithmic HRM. Finally, future studies should examine strategies to enhance the ethical and transparent use of algorithmic HRM, including employee involvement in algorithm design and periodic reviews to mitigate bias. Such research could bridge the gap between operational efficiency and ethical governance, ensuring that algorithmic HRM aligns with organizational values and employee expectations.
By linking algorithmic HRM to employee performance through the mediating effects of cognition, emotion, and behavior, this study advances theoretical and practical understandings of algorithmic HRM’s role in the digital workplace. It provides a robust framework for examining the interplay between technology and human agency, highlighting the importance of fairness, trust, and adaptability in leveraging algorithmic systems for sustainable performance gains. The findings underscore the need for a balanced approach that integrates operational efficiency with ethical and human-centered practices, offering a comprehensive roadmap for organizations navigating the complexities of algorithmic HRM.

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The impact of brand-developing versus collaborative virtual influencer endorsement selection strategies on consumer engagement

XIANG Diandian, YIN Yule, GE Mengqi, WANG Zihan

2025, 33 (6): 965-983. doi: 10.3724/SP.J.1042.2025.0965

Abstract ( 36 )

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Virtual influencer endorsements, empowered by artificial intelligence technology, offer a new approach to brand communication and exert outstanding influence on social media platforms. However, research on the relationship between virtual influencer endorsements and consumer engagement remains in its early stages, not only limited in scope but also facing two primary research gaps. First, firms face a unique challenge in leveraging these endorsements to enhance consumer engagement. This challenge arises from a strategic tension in selecting either brand-developing proprietary virtual influencers or collaborating with third-party virtual influencers. However, there is a lack of exploration of the impact of virtual influencer endorsement selection strategies on consumer engagement. Second, existing literature has yet to investigate the underlying mechanisms and boundary conditions. From various theoretical perspectives, virtual influencer endorsement involves several key mechanisms, including the communication cycle, information processing, and relationship interaction. Therefore, understanding the effectiveness of these mechanisms is crucial for addressing the differing brand communication effects generated by different virtual influencer endorsement selection strategies.
In response to these research gaps, based on the communication time, accuracy, and relationships perspectives, this study proposes communication timeliness mechanisms (Study 1), information discrimination mechanisms (Study 2), and relationship-building mechanisms (Study 3) to comprehensively examine the effects and mechanisms of virtual influencer endorsement selection strategies (brand-developing proprietary versus collaborating with third-party) on consumer engagement, and explores key boundary conditions from artificial intelligence technology factors and virtual influencer/consumer factors perspectives, combined with brand communication-related theories and influencer marketing.
Specifically, first, drawing on the research insights on brand awareness in the process of brand communication in marketing, and based on the signal detection theory, this study aims to explore the communication time mechanism of long-term "brand awareness" and "virtual influencer awareness" induced by virtual influencer endorsements selection strategies, and further analyze the moderating effect of the artificial intelligence technology factor (i.e., authenticity level of virtual influencers) and influencer factor (i.e., brand-virtual influencer consistency) (Study 1). Second, grounded in the elaboration likelihood model (ELM), which is widely used in the advertising domain, this study aims to explore the double-edged sword information discrimination mechanism of advertising intrusiveness and information diagnostic induced by virtual influencer endorsements selection strategies selection and examine the moderating effect of artificial intelligence technology factor (i.e., authenticity level of virtual influencers) and consumer factor (i.e., need for cognition) (Study 2). By doing so, it comprehensively understands how consumers distinguish the endorsement information of virtual influencers. Third, following the widely used parasocial interaction theory in the field of social media marketing, this study explores the relationship-building mechanism of consumers in virtual influencer endorsement brand activities, that is, consumer-brand identification and consumer-virtual influencer identification, and examines the moderating effect of artificial intelligence technology factor (i.e., virtual influencer’s agency: fully artificial intelligence-driven versus manual intervention) the authenticity level of virtual influencers and influencer factor (i.e., brand-virtual influencer consistency) (Study 3).
In response to these research gaps, this work makes the following contributions. First, this article focuses on the uniqueness of virtual influencer endorsement selection strategies, thus expanding the scope of brand endorsement research. While existing studies primarily explore the differences between virtual and real influencers in brand communication, few studies examine the specific selection strategies for virtual influencer endorsements. By analyzing this selection strategy, this study enriches the theoretical research system of brand endorsement and provides new topics for further research in this field. Additionally, this study systematically investigates the multiple mediation models, comprehensively revealing the underlying mechanisms, which contributes to understanding the relationship between virtual influencer endorsements selection strategies and consumer engagement and advancing virtual influencer marketing research. Furthermore, this study reveals the boundary conditions related to artificial intelligence technology factors (i.e., authenticity level of virtual influencers, virtual influencer’s agency: fully artificial intelligence-driven versus manual intervention), virtual influencer factor (i.e., brand-virtual influencer consistency), and consumer factor (i.e., need for cognition), thereby facilitating interdisciplinary integration between the fields of influencer marketing, AI technology and psychology. Moreover, this study aims to offer effective guidance for firms in selecting virtual influencer spokespersons, managing interventions for virtual influencer applicability, and implementing artificial intelligence marketing, thereby providing crucial insights for virtual influencer practices.

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How can humans and machines collaborate? Investigating the value-creating mechanisms of intelligent data analysis for multiple parties in sales contexts

REN Xingyao, WU Huichao, CHEN Feiyan, XU Huanyu, Zhang Wenjing

2025, 33 (6): 984-1005. doi: 10.3724/SP.J.1042.2025.0984

Abstract ( 22 )

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This study addresses the demand for high-quality economic development and is at the forefront of advancements in digitization and intelligent technology. It aims to uncover how intelligent data analysis dashboards (machines) and sellers (salespeople) collaborate to create value for multiple parties (sellers, buyers, and platform firms) in the context of human-machine collaborative selling. The study categorizes intelligent data analysis into descriptive (what happened), diagnostic (why it happened), predictive (what will happen next), and prescriptive (what should be done about it) types. It explores the following three research questions: (1) Under an exchange relationship orientation, what types of intelligent data analysis, when aligned with specific seller capabilities, can drive changes in seller behavior and enhance short-term transactional outcomes between sellers and buyers? (2) Under a communal relationship orientation, what types of intelligent data analysis, when integrated with specific seller capabilities, can improve buyer experiences and improve the long-term relationship quality between sellers and buyers? (3) How can platform operators, considering differences in market environments, determine the appropriate relationship (exchange relationship vs. communal relationship) orientation of intelligent data analysis to effectively balance short-term transactional outcomes and long-term relationship quality, thereby creating value for themselves?
The main innovations of this research are threefold. First, building on the human-machine collaboration research, this study is the first to explore how intelligent data analysis and seller capabilities can collaborate to create value for sellers, buyers, and platform operators. Specifically: (1) In terms of the focus on human-machine collaboration, existing research primarily compares the effects of humans, machines, and human-machine collaboration, demonstrating the necessity of the collaboration. In contrast, this study explores how humans and machines can collaborate, focusing specifically on augmented collaboration (i.e., how machines can augment human decision-making). It addresses the important question of what kind of seller capabilities are needed to align with different types of intelligent data analysis to create value. (2) In terms of research context, existing empirical studies mainly focus on customer service scenarios and sales-related contexts (e.g., sales training and recruitment). This study, however, examines a new context—how human-machine collaboration can drive sales conversion—offering fresh insights into human-machine collaboration research.
Secondly, this study systematically examines the value of four types of intelligent data analysis: descriptive, diagnostic, predictive, and prescriptive. It provides new theoretical insights into data analysis research: (1) In terms of data analysis, only two studies have focused on descriptive data analysis. This study not only considers descriptive analysis but also explores more intelligent forms of analysis, including diagnostic, predictive, and prescriptive analytics. (2) Regarding the impact of data analysis tools on stakeholders, existing research has primarily examined their effects on tool users (e.g., sellers), while this study expands the scope to include other stakeholders (i.e., buyers and platform operators). (3) In terms of the focus on the effectiveness of data analysis tools, previous research has primarily concentrated on the standalone effects of data analysis, with little attention given to the collaborative interaction between data analysis and human capabilities. This study addresses this gap by introducing seller capability characteristics and exploring how intelligent data analysis tools can work synergistically with human capabilities.
Finally, from the platform operator’s perspective, this study innovatively focuses on intelligent data analysis as a decision-enhancing tool for sellers, and identifies its role in facilitating seller-buyer interactions within the value creation process of transactional platform operators. Existing research has focused on four aspects: attracting both supply and demand sides, facilitating seller-buyer interactions, achieving effective matching between the two, and platform governance. Regarding facilitating seller-buyer interactions, while scholars have examined the value of transaction mechanism design and various information communication tools, no research has yet focused on the role of intelligent data analysis tools for sellers in the value creation process. This study distinguishes itself from previous research by: (1) focusing on decision-enhancing tools for sellers and introducing relationship orientations (exchange vs. communal relationships) into research on platform value creation paths, thereby overcoming the prior limitation of confining relationship orientations to studies of single-sided markets;(2) addressing the diversity within platform ecosystems by exploring how to align intelligent data analysis orientations with different market environment characteristics (e.g., category development stage, competitive intensity, seller’s price positioning) to balance the dual goals of short-term transaction growth and long-term supply-demand relationship quality, while creating value for multiple parties.
Overall, this study will offer new theoretical insights into research on human-machine collaboration, data analysis, and the value creation paths of platform operators. It will help platform firms and sellers understand and leverage intelligent data analysis to create value for sellers, buyers, and platform firms, promote innovation in human-machine collaborative selling practices, and enhance the efficiency and effectiveness of supply-demand matching.

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When artificial intelligence faces human emotions: The impact mechanism of emotion expression in AI-empowered service robots on user experience

LUO Lijuan, WANG Kang, HU Jinmiao, XU Sihua

2025, 33 (6): 1006-1026. doi: 10.3724/SP.J.1042.2025.1006

Abstract ( 67 )

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The rapid advancement of next-generation AI technologies has fundamentally reshaped interaction patterns between users and service providers. Nowadays, users not only expect AI to effectively solve problems but also aspire to gain positive emotional experiences during the interaction process. However, current AI services still face challenges such as user resistance, low acceptance, and poor service experiences. Addressing how service robots can establish effective emotional communication with users to deliver personalized, intelligent, and empathetic service experiences has become a critical research frontier.
This study investigates the holistic process of user-service robot interaction through the lens of AI-empowered emotion connection, establishing an integrated framework of "Emotion recognition, Emotion understanding, Emotional connection." We propose the following research framework and systematically investigates three principal research dimensions:
(1) User emotion recognition and emotion matrix construction based on a multidimensional emotion computing model. A user emotion recognition computing model is developed using machine learning algorithms and decision-level weighted fusion to resolve inconsistencies in cross-dimensional emotional expressions. Building upon the established multidimensional emotion recognition model, the valence-arousal-dominance (VAD) model is adopted as the analytical framework. Through combined machine learning and qualitative analysis methods, we systematically characterize users' emotional responses across different service stages and contexts. This research concept helps build a bridge between emotion recognition and service interaction, laying the foundation for real-time emotional responses with service interactions.
(2) The impact mechanisms of AI-empowered emotional expression content on user experience from the perspective of service journey. Human-robot interaction processes can be categorized into three sequential stages: initial service encounter, service usage, and service feedback. The initial encounter stage prioritizes AI emotional expression to stimulate user interest and establish trust, while the usage stage focuses on delivering affective experiences to enhance satisfaction. The feedback stage aims to mitigate user dissatisfaction and attain forgiveness. Aligning with stage-specific objectives, we propose differentiated emotional expression strategies. Drawing on Trust Theory, Cognitive Appraisal Theory, and Basic Psychological Needs Theory, we hypothesize that service robots' implementation of stage-specific emotional expressions (positive emotion in initial encounters, empathy during service usage, and gratitude in service feedback) can systematically enhance user experience. This study delves into the underlying mechanisms of AI-empowered emotional expression content on user experience at each service stage. Moreover, we also propose three moderating factors—the anthropomorphic features of AI, time pressure, and the types of explanatory information provided—as boundary conditions in different stages. This hypothesis framework enables the systematic investigation of when and why differentiated emotional content across service journey stages impacts user experience. This research concept fosters a holistic and dynamic understanding of service journey stages, highlighting the significance of leveraging AI emotional intelligence to activate user experience throughout the journey.
(3) The impact mechanisms of AI-empowered emotional expression modalities on user experience from the perspective of service contexts. Service contexts are classified into hedonic-oriented and utilitarian-oriented scenarios, where user preferences diverge significantly. Hedonic contexts center on experiential values like enjoyment, pleasure, and emotional engagement, while utilitarian contexts emphasize functional benefits including practicality, efficiency, and utility. Through the theoretical lenses of Social Presence Theory, Psychological Distance Theory, and Emotions-as-Social-Information Theory, we hypothesize that service robots' implementation of embodied emotional expression modalities (mono-sensory vs. multisensory) in hedonic-oriented and utilitarian-oriented service contexts can significantly enhance user service experience. This study further examines the underlying mechanisms of AI-empowered embodied emotional expression modalities on user experience at each service context. Moreover, we also propose two moderating factors—relationship norm orientation and task complexity—as boundary conditions in different contexts. This hypothesis framework enables the systematic investigation of when and why emotional expression modalities across distinct service contexts impact user experience. This research concept fosters differentiated thinking on the modalities of AI's emotional expression in service contexts, shedding light on the importance of emotional modalities in both hedonic-oriented and utilitarian-oriented service contexts.
This study advances the understanding of emotional expression mechanisms in service robots and user experience enhancement strategies within intelligent services. It offers significant theoretical contributions and practical insights. In terms of theoretical significance, this research enriches human-AI interaction theory by proposing a comprehensive framework for service robots' emotional expression mechanisms. It empirically demonstrates how AI-driven affective expressions activate and influence user experience while clarifying underlying mechanisms, thereby advancing the theoretical foundation for emotionally intelligent interaction design. In terms of practical significance, this research provides a new direction for the integrated development of AI and service industry, enabling service providers to optimize touchpoints across the service journey. More importantly, it underscores the value of affective intelligence, providing robust support for the high-quality and sustainable development of the service robotics industry.

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Neural mechanisms underlying the transformation between egocentric and allocentric spatial reference frames

LIU Jiali, ZHAO Haichao, HE Qinghua

2025, 33 (6): 1027-1035. doi: 10.3724/SP.J.1042.2025.1027

Abstract ( 29 )

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Organisms use two spatial reference frames to represent spatial information: the egocentric frame (i.e., self-centered, defined relative to the subject) and allocentric frames (i.e., a world-centered). The transformation between these two frames is essential for forming and applying cognitive maps. One widely recognized model, proposed by Bicanski and Burgess (BB model), suggests that the coordinated activity of the parietal cortex, retrosplenial cortex, and the medial temporal lobe networks underpins spatial reference frame transformation. Specifically, during spatial learning, the processed sensory information forms the egocentric representations in the parietal cortex. The retrosplenial cortex integrating this egocentric information and the head direction signal from the limbic regions (i.e., the anterior nucleus of the thalamus), which are then projected to the medial temporal lobe, where allocentric representations are formed. The reverse process facilitates the transformation from allocentric to egocentric frames, which is crucial for memory retrieval, imagination, and action execution.
Despite a substantial body of empirical research has been conducted surrounding this model, several unresolved issues remain: 1) the topological organization of egocentric and allocentric representations in the parietal-retrosplenial-temporal lobe loop is still debated; 2) it is unclear whether the human retrosplenial cortex supports joint representations of egocentric and allocentric information as seen in rodents, or if it is the sole brain region responsible for combining these spatial variables; 3) the hypothesized flow of information during spatial reference frame transformation still lacks experimental support. 4) there is a lack of causal evidence regarding the neural mechanisms underlying spatial reference frame transformation.
To address these questions, we propose propose 3 studies. In Studies 1 and 2, we will investigate the dynamic representation of egocentric, allocentric, and joint spatial information, as well as cross-brain communication within the parietal-retrosplenial-temporal lobe loop during spatial reference frame transformation, using high spatial-temporal resolution intracranial EEG. In Study 3, we will causally test the role of this neural loop in reference frame transformation and explore electrical stimulation protocols to enhance spatial reference frame transformation abilities using deep brain stimulation (DBS).
Studies 1 and 2 will recruit refractory epilepsy patients with electrodes implanted in the parietal cortex, retrosplenial cortex, and medial temporal lobe, who will perform a spatial memory task. Study 1 will focus on the direction learning phase (egocentric to allocentric transformation), where participants learn the directions of target buildings from a first-person perspective with different head direction, later identifying these directions on a global map. EEG time-frequency signals will be used to decode egocentric and allocentric target directions and head direction signals, identifying which brain regions within the parietal-retrosplenial-temporal lobe loop represent these spatial variables. Further, using Granger causality analysis and cross-frequency coupling analysis, we will investigate the information flow between brain areas which represent allocentric and egocentric information during time periods that can significantly decode these variables, and correlate the information flow with learning performance. Representational similarity analysis will be used to explore joint encoding of egocentric target directions and head directions by the retrosplenial cortex. Study 2 will focus on the direction testing phase (allocentric to egocentric transformation), where participants will convert memorized allocentric target directions into egocentric ones. The analysis will follow the same methodology as Study 1. In Study 3, we will investigate how electrical stimulation affects the conversion process from egocentric to allocentric reference frames. Refractory epilepsy patients with electrodes implanted in the retrosplenial cortex and medial temporal lobe will participate in a path integration task. Participants will use either an allocentric or an egocentric strategy during the task. Different stimulation protocols (50 Hz, theta-burst, or no stimulation) will be applied to the retrosplenial cortex to assess how these patterns affect behavioral performance with allocentric strategies.
This research combines computational models and multimodal neurotechnology to investigate the neural mechanisms of spatial reference frame transformation. It aims to uncover the topographical distribution of egocentric and allocentric representations in the brain, explore the role of low-frequency neural oscillations in cross-brain communication during reference frame transformation, and validate the causal role of the retrosplenial cortex in this process. These findings will not only provide evidence for existing computational models but also contribute to translating spatial navigation theory into clinical interventions.

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Third-party punishment under uncertainty: psychological and brain network mechanisms

LI Ting, WANG Li, LUO Yuejia, FENG Chunliang

2025, 33 (6): 1036-1046. doi: 10.3724/SP.J.1042.2025.1036

Abstract ( 40 )

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The American Psychological Association's (APA) 2019 report on Ten Trends in Contemporary Psychological Science identifies “Shining a spotlight on equity” as a central research imperative in modern psychology. Current investigations into third-party punishment (TPP), a crucial mechanism for enforcing social norms, including fairness, have largely overlooked the pervasive characteristic of uncertainty in real social contexts. Emerging evidence suggests that uncertainty often induces negative emotions, including anxiety and worry, which subsequently undermines prosocial behaviors such as cooperation and reciprocity. As a specialized form of prosocial conduct and linchpin of norm enforcement, TPP represents a critical societal safeguard. Its impairment could precipitate hierarchical breakdowns in normative constraints: from individual transgressions to collective norm erosion, ultimately threatening systemic social cooperation equilibrium. This necessitates empirical examination of whether and how uncertainty undermines TPP.
Two dimensions of fairness norm violations—outcome unfairness (i.e., observable unfair behaviors) and intentional unfairness (i.e., deliberate motives underlying unfair behaviors)—have been identified as pivotal drivers of TPP. Correspondingly, both outcome uncertainty and intent uncertainty may serve as critical factors influencing TPP. Recent behavioral studies have examined the impact of outcome uncertainty on TPP, yet the role of intent uncertainty remains underexplored. Furthermore, the specific emotional and cognitive processes underlying TPP under these two conditions of uncertainty remain unclear. This investigation aims to systematically examine the effects of outcome and intent uncertainty of norm violations on TPP decisions and its underlying brain network mechanisms. Further, we also explore the motives responsible for the changes in TPP behavior under uncertain contexts. By dissecting the neurocognitive interface between uncertainty appraisal and normative enforcement, this research can advance a mechanistic understanding of the architecture of normative decision-making and the psychological foundations of social norm maintenance. The research findings to be obtained may hold translational potential in terms of developing targeted interventions that nudge third-party norm enforcement behaviors in real-world contexts.
This study aims to elucidate the neural network representations of TPP under uncertainty during emotional processing and intention inference by integrating interdisciplinary techniques from psychology and cognitive neuroscience, particularly employing graph-theoretical analysis of complex brain networks. These methods have revolutionized traditional approaches by redefining the brain not as a collection of discrete anatomical units, but as an interconnected system of neurons, then focusing on and emphasizing how cognitive systems operate in an organized manner. Studying TPP from an integrated perspective of large-scale brain networks represents a significant breakthrough compared to traditional emphases on brain activation patterns and unidirectional mappings between behavior and neural activity. It helps to reveal critical mechanisms that were previously overlooked or undetectable through conventional approaches. In addition, uncertainty typically leads to a reduction in TPP engagement, with behavioral attenuation potentially attributable to motivational heterogeneity among individuals. For example, in situations of uncertainty, concerns about committing a Type I error (misinflicting harm) and cost avoidance both diminish TPP behavior. However, individuals motivated by these two factors may behave differently regarding fairness maintenance. While such behavioral attenuation is not easily observable through external behavioral measures, recent advances in neuroimaging and graph-based network techniques offer promising avenues to disentangle the specific motivational subgroups influenced by uncertainty. This framework could ultimately guide targeted neural interventions aimed at enhancing fairness-maintenance efforts in cost-avoidance motivated individuals. Thus, this study has the potential to expand theoretical boundaries and methodological approaches in TPP research, thereby enabling novel developments.

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The relationship between time perception and pain

YIN Huazhan, XIAO Chunhua

2025, 33 (6): 1047-1056. doi: 10.3724/SP.J.1042.2025.1047

Abstract ( 23 )

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The bidirectional relationship between temporal cognition and pain has attracted increasing attention due to its theoretical and practical implications. While existing studies have explored these interactions, their findings remain fragmented and lack a coherent theoretical framework. This study systematically integrates empirical research conducted between 1984 and 2024 to elucidate the underlying mechanisms of the bidirectional interactions between temporal cognition and pain. Additionally, it identifies critical future directions to address unresolved issues in the field, thereby contributing to the development of more effective approaches for pain management and temporal cognition interventions. This study highlights three key innovations. Firstly, it identifies the bidirectional nature of the relationship between temporal cognition and pain. On the one hand, pain influences temporal cognition, leading to outcomes such as lengthening, shortening, or having no significant effect on perceived durations. On the other hand, temporal cognition affects pain perception, whereby temporal cues and perceived durations can increase, decrease, or have no impact on pain intensity, tolerance, and sensitivity. Secondly, the study explains these findings within three established theoretical frameworks: the Attentional Gate Theory, Gate Control Theory, and Neuromatrix Theory, offering a multifaceted understanding of these phenomena. To be specific, regarding the influence of pain on temporal cognition, the findings are linked to higher arousal or attentional resource allocation to time, attentional focus on pain, or normalized arousal with disrupted temporal and normal processing or reduced cognitive variability. These mechanisms emphasize the role of attentional shifts and arousal levels in shaping the perception of time during painful experiences. Regarding the influence of temporal cognition on pain perception, the impact on pain intensity is associated with the inhibition of neurotransmitter release, negative emotions or emotional stability; increased and decreased pain tolerance are linked to attentional focus on time and negative emotions, respectively; increased and decreased pain sensitivity are associated with negative emotions and the inhibition of neurotransmitter release, respectively. These findings not only highlight the role of emotion and attention but also underscore the importance of physiological mechanisms. Finally, this study outlines three key future research priorities that aim to advance the field: it emphasizes the need to focus on the dynamic relationship between chronic pain intensity (the vector of arousal and valence) and temporal cognition, as well as its moderating factors and investigate the mediating mechanisms of attention and arousal in the influence of pain on deviations in temporal cognition, along with their boundary conditions; it also calls for further exploration of the mediating mechanisms of attention and arousal in the effects of temporal cues and durations on pain tolerance or sensitivity, as well as their boundary conditions; what’s more, it advocates for the development and evaluation of the manipulation mechanisms of pain and temporal interventions, with a focus on practical applications in clinical settings, such as, the timing management of medication administration. This study holds significant theoretical and practical implications. It not only provides a solid theoretical foundation for understanding the bidirectional relationship between temporal cognition and pain but also offers a more integrated perspective compared to previous fragmented findings. Furthermore, it sheds light on the complex roles of attention and arousal, delivering valuable insights for developing strategies to manage and intervene in pain effectively. In summary, this study systematically integrates empirical research based on diverse findings and interprets them within theoretical models of pain and temporal cognition. It explores the mechanisms involving attention, arousal, emotion, and physiological processes, highlighting the mediating roles of attention and arousal. These insights lay a solid foundation for future research aimed at uncovering these mediating mechanisms and developing effective interventions targeting both pain and temporal cognition. By advancing theoretical understanding and practical applications, this study contributes to a growing body of knowledge that holds the potential to improve pain management and enhance temporal cognition interventions.

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The ethical turn in mindfulness development: Wisdom-based transcendent morality

PENG Yanqin, SHI Ruiqi

2025, 33 (6): 1057-1066. doi: 10.3724/SP.J.1042.2025.1057

Abstract ( 60 )

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Against the background of mindfulness transitioning from the Buddhist tradition to the field of psychology, this paper explores its ethical dimensions, aiming to resolve the ambiguities of its ethical connotations and mechanisms of action in order to provide theoretical advances and innovative insights.
The study critically examines the evolution of mindfulness, transitioning from ethically neutral first-generation mindfulness to ethically informed second-generation mindfulness, and culminating in a novel, wisdom-oriented mindfulness. The findings indicate an inadequate understanding about the ethical implications of mindfulness across these generations.
To address this shortfall, the paper proposes an innovative classification of Buddhist ethics from the perspective of Buddhism’s Two Truths (Conventional Truth and Ultimate Truth). It identifies a dual dimension of Buddhist ethics, comprising normative morality based on rule-following and transcendental morality based on the enhancement of mental faculties, with a particular emphasis on transcendental morality. Normative morality is represented by the precepts, which delineate between good and evil through rules, emphasizing external moral norms along with their restraining effect on the individual and society. By contrast, transcendental morality is embodied by “loving-kindness”, which results from transcending conventional notions of good and evil. This is achieved through the “refutation of grāha”, leading to a higher cognitive model known in Buddhism as “non-self”, which is characterized by “inwardness”, “non-distinction”, and “endogenous morality”. It enables individuals to gain insight into the nature of reality, fundamentally transforming their ways of perception and interaction with the world, marking a significant breakthrough in the functioning of the mind and the form of wisdom.
Hence, “wisdom”, or the formation of the advanced cognitive model, is the foundation for the development of transcendental morality. Notably, the concept of wisdom in this paper is fundamentally different from the wisdom described in the novel, wisdom-oriented mindfulness, where the latter views wisdom merely as a result of decentering. Instead, drawing on traditional Buddhist theories of mind, this paper emphasizes that wisdom’s essence lies in cognitive enhancement. This perspective seeks to deepen and expand the role of wisdom in mindfulness practice.
To cultivate a wisdom-based transcendent morality in mindfulness training, the paper introduces a “mindfulness training returning to Buddhist ethics” model. This model outlines three stages: the initial phase focuses on attentional training and psychological relief, the intermediate phase integrates normative moral principles to improve virtues, and the advanced phase centers on wisdom-oriented morality in response to more complex ethical dilemmas.
In addition, to address concerns about the potential religionization of mindfulness due to the integration of Buddhist ethics, this paper explores the boundaries of its implementation. In fact, Buddhism, at its core, is not an institutionalized religion but a traditional cultural system centered on mind cultivation, which is a classical theory and technique of psychological adaptation. Therefore, the integration of Buddhist ethics into mindfulness training is not religious indoctrination, let alone a return to the original religious form, but rather a refinement and transformation of psychological theories and techniques to the greatest extent possible.
Finally, given the tendency of Western scholars to return to the traditional ethics of Buddhism, this paper advocates that scholars need to enhance their academic sensitivity and cultural pioneering awareness and try to put forward a relatively complete ethical model of mindfulness based on their own indigenous cultural strengths, so as to provide Chinese scholars with a new perspective for enriching the theoretical framework of mindfulness.
In conclusion, mindfulness is a tool for improving mental health and encompasses the connotation of ethics, morality, and even wisdom. In contemporary society, which is in dire need of spiritual upliftment, it is particularly important to highlight the wisdom’s connotation in the ethical dimension of mindfulness.

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Influencing factors and mechanisms of action encoding in facilitating memory in school-aged children with autism spectrum disorder

XIE Tingting, WANG Lijuan

2025, 33 (6): 1067-1076. doi: 10.3724/SP.J.1042.2025.1067

Abstract ( 49 )

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication, restricted interests or activities, and repetitive stereotyped behaviors. In addition to these core features, children with ASD often exhibit deficits in memory function (Griffin et al., 2021). Summers and Craik (1994) demonstrated that action encoding can enhance memory in children with ASD. However, other studies have shown that action encoding may not be effective in certain contexts (Wang et al., 2022; Xie et al., 2022), suggesting that the efficacy of action encoding as a memory enhancement method for children with ASD may depend on specific conditions. Identifying the factors and mechanisms that influence the effectiveness of action encoding in promoting memory could alleviate the stress on parents and schools in raising children with ASD. Additionally, it could provide insight into the memory characteristics of children with ASD, offering valuable guidance for research on the pathogenesis or diagnosis of ASD. Given these implications, this study reviews existing research on the effects of action encoding on memory in children with ASD and addresses the following questions: (1) What factors influence the effectiveness of action encoding in enhancing memory in children with ASD? (2) What theoretical contributions can be made by exploring these influencing factors to improve the predictive power of theories regarding the efficacy of action encoding in children with ASD? (3) What are the limitations of current research, and how can future studies address these gaps to deepen our understanding of the relationship between action encoding and memory in children with ASD.
Previous research revealed that the efficacy of action encoding in promoting memory in children with ASD is influenced by the type of action encoding and the type of ASD. Specifically, action encoding involving self-performed and experimenter-performed actions enhanced memory in children with ASD who had no comorbidities with other disorders and exhibited lower-middle intelligence (SPM scores of 10% and 25%) as well as upper-middle intelligence (SPM scores of 50% and 75%). In contrast, self-performed action encoding did not facilitate memory in children with ASD who had no comorbidities and demonstrated lower-middle intelligence (SPM score of 5%). Additionally, the effects of imagining present-performed actions and imagining future-performed actions on memory differed significantly in children with ASD who had no comorbidities and exhibited upper-middle intelligence.
Previous research on the effects of action encoding on memory in children with ASD has predominantly contributed to theories of self-performed actions, including the non-strategic encoding theory, multimodal theory, motor encoding theory, the ‘four-component theory’, and the ‘five-component theory’. The non-strategic encoding, multimodal, and motor encoding theories emphasize that the motor component, elicited by the execution of an action, is the primary driver of the self-performed effect. In contrast, the ‘four-component’ and ‘five-component’ theories propose that, in addition to the motor component, semantic and imagery components are also critical to the self-performed effect. Following the discovery that self-performed action encoding enhances memory in children with ASD, previous studies have generally concluded that, similar to typically developing (TD) children, children with ASD exhibit self-performed effects due to the motor component, albeit with weaker activation. However, unlike TD children, the self-performed effects in children with ASD do not arise from the imagery component. These similarities and differences underscore the importance of prioritizing the motor component over the imagery component when applying self-performed action encoding theories to predict whether children with ASD will demonstrate self-performed effects.
Despite these valuable findings, previous studies have identified only two types of factors influencing the effectiveness of action encoding in promoting memory in children with ASD—namely, the type of action encoding and intelligence level—without elucidating their specific mechanisms. Moreover, these studies have offered an incomplete exploration of the mechanisms underlying the memory-enhancing effects of action encoding in children with ASD. Specifically, they have predominantly focused on applying self-performed action encoding theory to the ASD population, while neglecting theoretical advancements in understanding experimenter-performed and imagery-performed action encoding. To facilitate a more comprehensive and effective application of action encoding in interventions and to strengthen the theoretical foundation guiding such interventions, future research should: (1) broaden the investigation of factors influencing action encoding in memory enhancement for children with ASD, including action encoding modality, type of ASD, and type of action; and (2) refine existing theories by incorporating identified moderators, particularly through the development of theoretical frameworks that explain the memory-enhancing effects of experimenter-performed and imagery-performed actions in children with ASD, thereby enhancing the predictive accuracy of these theories; and (3) design and implement a targeted memory intervention program specifically tailored to the unique needs of children with ASD.

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