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Duration perception: Evidence from a series of studies and theoretical integration YIN Huazhan, WANG Siyu, HUANG Xiting 2027, 35 (1):  1-17.  doi: 10.3724/SP.J.1042.2027.0001 Abstract ( 61 )   PDF (681KB) ( 71 )   Peer Review Comments Duration perception is a fundamental cognitive ability, yet its mechanisms remain contested. A central question is whether durations of different lengths are processed by a single timing system or by qualitatively distinct mechanisms. Drawing on a series of studies conducted by our research group over the past two decades, together with key findings from independent laboratories, this review integrates evidence across four themes—segmentation, attention and working memory, emotional modulation, and special populations—and proposes three theoretical hypotheses that move the field from description toward mechanistic explanation. The segmentation issue has a long history. Behavioral, electrophysiological, and neurostimulation evidence converges on the view that duration processing is not homogeneous. Rather than sharp boundaries at 300-500 ms and 2-3 s, the evidence supports functional transition zones. These zones delineate three processing levels: a sensory-automatic level below roughly 300-500 ms, a perceptual level between 300-500 ms and 2-3 s that is sensitive to attentional resources, and a memory-dependent level beyond 2-3 s that increasingly relies on working memory. We emphasize that these zones are probabilistic and task-dependent, not fixed critical points. The proposed representational forms—iconic, pre-semantic, and semantic codes—are offered as theoretical propositions that require direct empirical test. Closely related to segmentation is the distinction between attention and working memory. We propose the attention-working memory duration-dependency hypothesis. Attention functions as a general resource that modulates pulse accumulation across a wide range of durations, from milliseconds to seconds. Working memory capacity, by contrast, is selectively recruited for durations beyond 2-3 s, when temporal representations require active maintenance and reconstruction. This dissociation helps explain why some studies report working memory effects on timing while others do not. It also yields testable predictions: manipulations of attentional load should affect both short and long durations, whereas individual differences in working memory capacity should primarily affect long durations. We further note boundary conditions, as task paradigms may shift the transition zone. Emotion adds another layer of complexity. We propose the segmentation-integrated hypothesis of emotional effects on duration perception. Existing accounts often assume that emotion either speeds up or slows down an internal clock or consumes attentional resources. We argue that the direction and magnitude of emotional effects depend on the target duration range being judged. In the short-duration range, attentional capture by salient emotional stimuli dominates, often lengthening perceived duration. In the medium range, arousal-driven clock acceleration becomes primary. In the long-duration range, arousal returns to baseline, and cognitive factors such as memory and decision-making dominate. This framework integrates disembodied (arousal/attention) and embodied (bodily state simulation) pathways and explains why the same emotion can produce opposite effects across studies. It also generates testable predictions: early attentional capture should affect all salient stimuli regardless of valence; high-arousal emotions should produce the strongest distortions in the medium window; and emotional effects should diminish for very long durations. Clinical and special populations provide a complementary perspective. We propose a dual-pathway model of abnormalities in duration perception. The cognitive resource deficit pathway centers on working memory impairment and selectively affects long-duration processing beyond 2-3 s, as observed in depression and alcohol use disorder. The perceptual encoding deficit pathway centers on reduced temporal resolution and selectively affects short-duration processing below 1 s, as observed in dyslexia and deafness, sometimes with sensory compensation at longer durations. State factors such as anxiety and pain constitute a parallel modulatory mechanism that dynamically shifts the relative weight of arousal and attention without altering stable cognitive architecture. This model offers a unified account of seemingly disparate timing abnormalities and provides converging evidence for the segmentation framework. The theoretical contributions are threefold. We advance segmentation theory from a descriptive taxonomy to a mechanistic framework with explicit resource-dependency predictions. We dissociate attention from working memory in timing, moving beyond the generic notion of cognitive resources. We integrate emotional effects and clinical abnormalities into the same segmentation framework, demonstrating cross-domain consistency. These hypotheses are falsifiable and point to specific future studies. Clinically, the model offers a theoretical basis for understanding timing disturbances in clinical populations, although direct application to screening or treatment evaluation requires further validation of reliability and incremental validity. In sum, this work synthesizes two decades of research to propose an integrated, testable account of duration perception. It highlights that time processing is not a single uniform system but a dynamic interplay of sensory, attentional, working memory, and emotional mechanisms that unfold differentially across time scales. References | Related Articles | Metrics

Conceptual Framework

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Employees' interpersonally oriented AI-directed help-seeking strategies: Types, formation, and effects CHEN Keyu, ZHOU Qiwei, LI Qian 2027, 35 (1):  18-32.  doi: 10.3724/SP.J.1042.2027.0018 Abstract ( 60 )   PDF (660KB) ( 59 )   Peer Review Comments Artificial intelligence (AI) is becoming a new source of help in the workplace, reshaping how employees solve work-related problems and maintain relationships with coworkers. Prior research on workplace help-seeking has mainly examined interpersonal exchanges between help seekers and human help providers. However, when employees can also seek help from AI, help-seeking becomes a triadic process involving help seekers, AI, and coworkers. This research addresses how employees strategically coordinate help-seeking from AI and help-seeking from coworkers, and proposes a theoretical framework of employees' interpersonally oriented AI-directed help-seeking strategies. The framework distinguishes between two strategies that reflect different interpersonal orientations. The interpersonal promotion-oriented AI-directed help-seeking strategy refers to integrating help-seeking from AI with help-seeking from coworkers to pursue interpersonal gains. Employees using this strategy may first turn to AI to clarify problems, generate preliminary solutions, organize questions, or explore alternative ideas, and then approach coworkers for experience-based judgment, feedback, discussion, and contextualized support. In this strategy, AI does not replace coworkers; instead, it helps employees improve the quality of subsequent interpersonal help-seeking and create more valuable coworker interactions. By contrast, the interpersonal prevention-oriented AI-directed help-seeking strategy refers to using help-seeking from AI to replace or reduce help-seeking from coworkers to avoid interpersonal risks. Employees using this strategy may rely on AI to solve problems while reducing direct exposure to coworkers, thereby avoiding displays of incompetence, possible rejection, or negative social evaluation. This typology clarifies that AI-directed help-seeking is not merely a matter of human-AI interaction. It may either strengthen or weaken interpersonal engagement depending on whether employees use AI to complement or substitute for help from coworkers. This research further explains how these two strategies emerge. Drawing on social cognitive theory, we propose that employees' AI capability and team cooperative climate jointly shape their strategy choices. AI capability gives employees the ability and confidence to incorporate AI into problem solving, but its interpersonal implications depend on how employees perceive coworkers within their team context. When the team cooperative climate is strong, coworkers are more likely to be viewed as partners for resource integration. Under this condition, employees with higher AI capability are more likely to develop a cooperative motivational state and use AI to enhance coworker interaction. When the team cooperative climate is weak, coworkers are more likely to be viewed as sources of evaluative pressure, potential resource loss, or competitive threat. Under this condition, employees with higher AI capability are more likely to develop a competitive motivational state and reduce interpersonal exposure through AI. This research then specifies the consequences of the two strategies for help seekers. Based on conservation of resources theory, we identify two mechanisms: actors' contextualized tacit knowledge acquisition and actors' momentary social-evaluative pressure. The interpersonal promotion-oriented strategy may increase contextualized tacit knowledge acquisition because it preserves coworker interaction and enables employees to access task-specific experience, team routines, organizational norms, and interpersonal knowledge that are difficult to obtain from AI alone. At the same time, it may increase momentary social-evaluative pressure because employees need to present problems, acknowledge limitations, and receive coworker feedback. Conversely, the interpersonal prevention-oriented strategy may reduce contextualized tacit knowledge acquisition by limiting coworker interaction, but it may also reduce momentary social-evaluative pressure by lowering direct interpersonal exposure. These two mechanisms explain why both strategies may have ambivalent effects on employees' daily work engagement and subsequent job performance. Finally, the research examines coworker responses. Drawing on sociometer theory, it argues that coworkers may infer employees' AI-directed help-seeking strategies from observable interaction cues, such as whether employees involve them in problem solving, seek their advice after using AI, bypass collaboration routines, or rely on AI outputs in team discussions. When employees use an interpersonal promotion-oriented strategy, coworkers may perceive that their competence, experience, and relational value remain important in the AI era, which enhances perceived competence and warmth evaluations and promotes social support. When employees use an interpersonal prevention-oriented strategy, coworkers may perceive that their expertise is being devalued or that their relational value is being discounted, which may reduce these evaluations and increase social undermining. Overall, this research extends workplace help-seeking research by showing how AI changes the relationship between help-seeking from AI and help-seeking from coworkers. It contributes to research on workplace help-seeking, human-AI interaction, and interpersonal dynamics in organizations, and offers practical implications for guiding employees to use AI while maintaining constructive coworker relationships. References | Related Articles | Metrics

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When AI becomes a teammate: The impact of collaborative autonomy on human-AI team dynamics FAN Zili, CHENG Ken, ZHU Mengting 2027, 35 (1):  33-43.  doi: 10.3724/SP.J.1042.2027.0033 Abstract ( 57 )   PDF (594KB) ( 106 )   Peer Review Comments With the deepening development of "artificial intelligence+", more and more work requires collaboration between humans and AI. However, previous studies have mostly explored the impact of AI systems on human-AI collaboration from a technical perspective, but have paid less attention to the fact that with the increasing autonomy of AI technology, the relationship between humans and AI is shifting from "tools" to "teammates". Faced with the challenges brought about by the transformation of this relationship model, how to ensure the effectiveness of human-AI collaboration has become a key issue that urgently needs to be addressed. In the face of this issue, this paper breaks away from the previous practice of viewing AI and human interaction unilaterally from the perspective of AI technology systems, and instead incorporates humans into the analysis system, focusing on the four dimensions of human-AI collaborative autonomy: adaptive learning, uncertainty, opacity, and openness. Based on role theory, three studies were conducted using system screening method, content analysis method, critical synthesis method, etc. Study 1 points out that the uncertainty dimension and opacity dimension of human-AI collaborative autonomy lead to role conflicts that hinder human-AI collaboration, aiming to explore how the two dimensions of uncertainty and opacity can ultimately reduce human-AI collaboration through role conflicts and human-AI fit; Study 2, from the perspective of "AI actively adapting to human roles", points out that the openness dimension will enable AI systems to further understand and grasp the technical characteristics and role requirements required for collaboration with humans through role taking, thereby enhancing AI's resilience and enabling AI to better understand human intentions and collaborate; Study 3, from the perspective of "humans are willing to accept AI as a 'new teammate'", points out that the adaptive learning dimension will further enhance their anthropomorphism through role learning, allowing human teams to better understand and accept AI, ultimately achieving the goal of promoting human-AI collaboration. Three studies complement each other and construct a human-AI collaboration model, aiming at exploring how human-AI collaborative autonomy ensures effective collaboration between humans and AI. Based on previous research, this paper has three main innovations: firstly, it extends the research boundary of AI technology (automation → autonomy) and introduces the concept of "human-AI collaborative autonomy". Compared with other technological features, the human-AI collaborative autonomy breaks away from the shortcomings of exploring the interaction between AI and humans unilaterally and through a static logic of mutual substitution. Instead, it takes the human-AI joint cognitive system as the basic analysis unit, including humans in the analysis system, emphasizing the collaborative complementarity, interactive trust, and dynamic balance between both parties. Secondly, it broadens the research perspective on the interaction between humans and AI (technical perspective → coupling perspective). Most existing research explores human-AI interaction from a technological perspective, focusing on the impact of the advantages of AI technology on human work and life. Based on the coupling perspective of existing research and role theory, we believe that the collaboration between humans and AI requires not only "AI actively adapting to human roles as an independent individual", but also "humans willing to accept AI as a new teammate. Both are indispensable. Thirdly, it enriches the relationship pattern between humans and AI (tool usage → teammate collaboration). Previous explorations on the relationship between humans and AI mostly placed AI in an auxiliary position, believing that AI is a tool that requires human operation. This paper breaks the previous recognition of the relationship between humans and AI, and argues that with the increasing advancement of technological autonomy, the relationship between humans and AI has gradually shifted from the tool usage to the teammate collaboration. AI has begun to integrate human teams as an independent intelligent entity, interdependent with human team members on an equal footing and playing an irreplaceable role in the team. References | Related Articles | Metrics

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How leaders' underdog narratives influence low-status team innovation YIN Wenjun, OUYANG Xi, LIU Zhiqiang, HU Yuhang 2027, 35 (1):  44-63.  doi: 10.3724/SP.J.1042.2027.0044 Abstract ( 37 )   PDF (778KB) ( 63 )   Peer Review Comments Underdog narratives describe actors who face unfavorable conditions, limited resources, or low expectations but strive to overcome these disadvantages. Previous research has documented the positive effects of such narratives primarily in marketing and strategic contexts, where they are often used to shape external audiences' attitudes and gain support. However, considerably less is known about how underdog narratives function within organizations. In particular, little is known about how organizational leaders can use underdog narratives to influence disadvantaged groups and promote innovation. This research addresses this issue by focusing on low-status teams, an often-overlooked group that may face disadvantages in access to resources, organizational influence, and recognition while also possessing considerable innovative potential. Drawing on sensemaking theory, the research examines how leaders construct and communicate underdog narratives to shape team members' understanding of their disadvantaged circumstances and transform unfavorable positions into a shared basis for collective action and innovation. The research proceeds in several stages. First, it seeks to clarify the definition and characteristics of leaders' underdog narratives. It proposes that leaders' underdog narratives comprise three core dimensions: disadvantage resonance, opportunity guidance, and capability enhancement. Disadvantage resonance refers to constructing a shared understanding of the team's disadvantaged position, encouraging members to confront their disadvantages and stimulating their motivation to overcome them. Opportunity guidance refers to articulating clear goals that help the team identify opportunities to move beyond its disadvantaged position and provide direction for collective action. Capability enhancement refers to strengthening the team's confidence through narratives, enhancing members' perceptions of their collective ability to overcome difficulties, and empowering subsequent action. The research then examines how leaders' underdog narratives promote innovation in low-status teams. Rather than treating team innovation as a single outcome, it distinguishes between idea generation and idea implementation, two interconnected stages with different central tasks. Idea generation primarily involves exploratory activities, such as acquiring diverse information and exploring new possibilities, whereas idea implementation emphasizes the coordination and use of knowledge, capabilities, and resources to transform ideas into practical outcomes. On this basis, the research investigates how leaders' underdog narratives shape low-status teams' needs to seek external resources and integrate internal resources and how these needs subsequently motivate team responses. Two pathways with different emphases across the innovation process are proposed: one centered on external resource-seeking needs and primarily associated with idea generation, and the other centered on internal resource-integration needs and primarily associated with idea implementation. The research also examines how team status disparity and the stability of teams' status shape the effectiveness of leaders' underdog narratives, thereby identifying the status-related conditions under which such narratives are more likely to facilitate innovation in low-status teams. The research will employ a mixed-methods approach integrating qualitative and quantitative research. The initial stage will clarify and refine the conceptualization and dimensions of leaders' underdog narratives. Subsequent studies will develop and validate a measurement scale and empirically examine the proposed relationships, mechanisms, and boundary conditions. The findings will then be integrated to establish an overall model explaining how leaders' underdog narratives influence innovation in low-status teams. The research is expected to strengthen the theoretical foundation of leaders' underdog narratives by moving beyond preliminary conceptual discussions toward a more systematic conceptualization and measurement of the construct in organizational settings. It further broadens the understanding of the effectiveness of leaders' underdog narratives by introducing a resource-needs perspective and examining how such narratives activate low-status teams' needs for external resource seeking and internal resource integration, thereby shaping different responses across the innovation process. In addition, by examining the role of team and organizational status characteristics as boundary conditions, the research seeks to clarify when and under what status-related conditions leaders' underdog narratives are more likely to be effective. These efforts extend underdog narrative research from external audiences to internal organizational contexts, enrich the understanding of leadership communication under disadvantage, and provide a more differentiated explanation of how leadership can mobilize low-status teams to innovate. The findings may also offer practical guidance for leaders and organizations seeking to transform disadvantage into a source of collective motivation, resource mobilization, and innovation. References | Related Articles | Metrics

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Team moral behavior patterns (TMBP): Conceptualization, formation, and multi-level influencing mechanisms YANG Chunjiang, CHEN Aobo, YANG Xiao 2027, 35 (1):  64-81.  doi: 10.3724/SP.J.1042.2027.0064 Abstract ( 27 )   PDF (848KB) ( 48 )   Peer Review Comments Organizational misconduct and ethical failures increasingly reveal that moral behavior in contemporary organizations is not merely an individual-level phenomenon but a collective process embedded in team interactions. However, existing research on organizational morality has been predominantly dominated by an individual-centered perspective, focusing on employees' personal moral judgments and behaviors while paying limited attention to how moral behaviors collectively emerge within teams. Meanwhile, although teams have become the fundamental units of organizational functioning, team research has traditionally emphasized performance-related outcomes, such as efficiency and innovation, while largely overlooking ethical characteristics and moral consequences at the team level. These limitations hinder a comprehensive understanding of how collective moral behaviors are formed, maintained, and transformed in organizational contexts. To address these theoretical limitations, this study introduces the concept of team moral behavior pattern (TMBP) and develops a theoretical framework for understanding collective moral behavior in teams. We define TMBP as the specific and regularized collective characteristics that emerge from the moral behaviors of all team members. Unlike traditional approaches that simply aggregate individual moral behaviors to represent team morality, our conceptualization views teams as complex social systems in which members' moral behaviors interact, influence one another, and generate emergent collective patterns. By shifting the analytical focus from individual moral attributes to collective behavioral configurations, this study provides a new theoretical lens for examining team morality as a distinct level of analysis. Building on this conceptual foundation, we first develop a dynamic evolutionary model of TMBP formation. Specifically, team moral behavior patterns emerge through a continuous process involving moral events, team moral standards, collective behavioral intentions, moral behaviors, and behavioral evaluations. Through mechanisms of moral anticipation, collective self-regulation, and moral reflection, teams continuously integrate, reinforce, adjust, and reconstruct their moral behavioral patterns. This dynamic perspective challenges the prevailing static assumption of team morality and highlights that collective moral behaviors are continuously shaped by internal interactions and external events. Furthermore, this study develops a typology of TMBP by incorporating the multidimensional characteristics of teams. Based on three fundamental team attributes—skill orientation, authority orientation, and time orientation—we identify three representative types of teams: skill-oriented teams, authority-oriented teams, and time-oriented teams. We theorize that these distinct team structures give rise to different forms of TMBP, including clustered, hierarchical, and equalized patterns, respectively. Each pattern reflects a unique configuration of moral behavior distribution, interaction mechanisms, and evolutionary trajectories. This typological framework advances understanding of why different teams may exhibit substantially different moral characteristics even when composed of similarly moral individuals. Finally, we extend the theoretical implications of TMBP by examining its multilevel consequences. We propose that different team moral behavior patterns may influence outcomes across individual, team, and organizational levels through distinct mechanisms. Clustered TMBP affects outcomes through social network mechanisms, such as group embeddedness, relationship conflict, and organizational learning. Hierarchical TMBP operates through social dominance mechanisms, including psychological safety, status mobility, and organizational change. Egalitarian TMBP influences outcomes through social identity mechanisms, involving responsibility diffusion, team cohesion, and corporate social responsibility. By linking TMBP with individual, team, and organizational outcomes, this framework integrates moral behavior research with broader theories of team effectiveness and organizational development. This study makes several theoretical contributions. First, it extends organizational morality research beyond individual-centered approaches by establishing a team-level perspective that captures collective moral behavioral configurations. Second, it advances team research by introducing TMBP as an emergent team construct rather than simply transferring individual-level concepts to the collective level. Third, it develops an integrated theoretical framework connecting team characteristics, moral behavior patterns, dynamic evolution, and multilevel consequences. Overall, this study establishes a new paradigm for understanding collective morality in organizations and provides a foundation for future empirical research on how teams develop, maintain, and transform their moral behavioral patterns. 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How do virtual streamers turn viewership traffic into sales? Consumer behavior mechanisms from an interaction characteristics perspective QIAO Shuchen, HU Mingye, NIU Xiaoxiao, YANG Chen 2027, 35 (1):  82-100.  doi: 10.3724/SP.J.1042.2027.0082 Abstract ( 45 )   PDF (753KB) ( 46 )   Peer Review Comments Powered by artificial intelligence technologies, virtual streamers have been widely adopted in Chinese livestreaming e-commerce as a scalable alternative to human streamers, enabling round-the-clock product promotion and real-time consumer interactions. Despite their growing industrial prevalence, the conversion effectiveness of virtual streamer livestreams remains highly inconsistent, and the mechanisms through which their interaction characteristics shape consumer behavior lack systematic academic investigation. This study examines how the interaction characteristics of virtual streamers influence consumers' pre-purchase decision-making and post-purchase word-of-mouth, aiming to reveal the underlying logic of converting livestream viewership into actual sales. Grounded in an integrated analytical framework of informational and affective pathways, this study addresses three core research questions via empirical designs. First, it investigates the impact of virtual streamer visual attributes on consumers' pre-purchase behaviors, as well as the moderating effect of product information presentation modes. This part unpacks the cognitive processing mechanism through which visual design and information delivery jointly shape interaction quality and purchase intention. Second, the study explores how virtual streamers' communication styles drive pre-purchase consumer engagement through the affective interaction path, and identifies boundary conditions defined by consumer heterogeneity and product category. Third, it extends the analysis to the post-purchase stage by comparing consumer word-of-mouth evaluations of virtual and human streamers across varying service quality scenarios, and elucidates the asymmetric evaluation patterns and their psychological underpinnings. This study makes three key theoretical contributions to the fields of livestreaming e-commerce and AI consumer behavior. First, from the interaction characteristic perspective, it structurally decomposes virtual livestreaming interaction cues into informational and affective dimensions, establishing a unified analytical paradigm that clarifies the differentiated roles of distinct interaction signals. This framework fills a critical research gap, as existing literature has largely focused on anthropomorphic avatar traits without systematically examining the core interaction process. Second, the study extends the analytical scope beyond pre-purchase intention to post-purchase word-of-mouth formation, revealing the asymmetric nature of consumer evaluations of virtual streamers under different service quality levels. This finding offers a novel theoretical account of the long-term relationship between AI agents and consumers, complementing the dominant focus on pre-purchase conversion in prior research. Third, it identifies heterogeneous boundary effects from both stable trait and situational state dimensions. Specifically, it introduces social overload as a dynamic situational variable to explore the adaptive boundaries of virtual streamer interaction strategies, addressing the limitation of existing studies that have paid insufficient attention to dynamic consumer states. Practically, this study provides actionable implications for merchants, e-commerce platforms and digital marketing practitioners. For brand operators, the dual-pathway framework guides the optimization of virtual streamer design, including visual image customization and communication style matching, to enhance interaction efficiency and sales conversion. For livestreaming platforms, the findings support the development of virtual streamer configuration tools and post-purchase review management systems. For consumers, clarifying the interaction mechanisms helps mitigate irrational consumption caused by information asymmetry or affective persuasion, and improves overall purchase decision quality and post-purchase experience. In sum, this study expands the theoretical boundaries of livestreaming e-commerce and consumer online behavior research, and provides theoretical support for the high-quality development of the digital economy and e-commerce sector. By integrating cognitive and affective mechanisms and covering the full consumer decision journey, it offers a comprehensive analytical framework for virtual streamer marketing and lays a solid foundation for future research on AI-enabled digital marketing interactions. References | Related Articles | Metrics

Research Method

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Understanding time and change: Theory, method and future direction for qualitative longitudinal research WANG Guangxin, XIONG Yan, QIN Lixuan, SHANG Jiayi, YUAN Chenxi, SUO Xinyi, LIU Jiayi 2027, 35 (1):  101-118.  doi: 10.3724/SP.J.1042.2027.0101 Abstract ( 40 )   PDF (753KB) ( 67 )   Peer Review Comments Qualitative Longitudinal Research (QLR) has emerged as a distinctive framework that places time, its passage, and the process of change itself at the analytical center. By prospectively and repeatedly collecting qualitative data—such as in-depth interviews and participant observation—from the same participants or settings, QLR aims to track how individual experiences, meanings, and behaviors unfold, shift, and take shape over time. Its central purpose is to reveal the underlying mechanisms through which specific phenomena evolve. Three defining features distinguish QLR: centering “time and change” as the analytical core, employing prospective tracking and recursive data generation, and committing to uncovering the processual mechanisms of change. To clarify QLR's unique position, it is useful to distinguish it from quantitative longitudinal and cross-sectional research. Unlike quantitative longitudinal research, which pursues statistical inference and causal modeling with large samples, QLR uses in-depth qualitative data to reveal the subjective meanings, contextual mechanisms, and non-linear trajectories that underlie change. Compared with cross-sectional research (whether quantitative or qualitative), QLR moves beyond snapshot descriptions at a single time point. Instead, it takes the “process of change” itself as the core analytical object through prospective and recursive data collection across multiple waves. The theoretical contribution of QLR lies in its advancement of the conceptualization of temporality. Moving beyond the conventional view of time as a linear, measurable background, QLR integrates “fixed time” (Chronos)—the institutional, measurable intervals that provide the structural skeleton for research design—with “fluid time”(Kairos)—the subjective, context-bound experiences of tempo, rhythm, and critical moments. Rather than treating these as binary opposites, QLR creatively synthesizes them through recursive data collection strategies, effectively turning “time itself into data.” Furthermore, QLR employs nested temporal analysis to reveal the dynamic co-construction between micro-level individual life courses and macro-level socio-historical processes. Key conceptual tools for decoding temporality include sequence, rhythm, periodicity, and non-linear turning points, enabling researchers to dissect the complex temporal texture of lived experience. In terms of research practice, this paper provides a systematic integration of QLR data collection and analysis. Core data collection strategies include repeated recursive interviewing, in which participants reflect on prior statements across waves, and visual tools such as timeline drawing and Photo-voice, which are repeatedly employed to make change visible and dialogically accessible over time. For analysis, the paper elaborates two signature techniques: recurrent cross-sectional analysis, which compares group-level thematic shifts across time points while retaining flexibility in sample composition, and trajectory analysis, which maps individual or small-group change pathways in depth to identify non-linear patterns, critical transitions, and underlying mechanisms. The “three-strand analysis” framework is also explicated, requiring researchers to simultaneously track themes, individual cases, and temporal flows. Regarding research quality, this paper proposes a time-sensitive evaluation framework grounded in the credibility and depth of interpretations of “change.” Beyond general qualitative criteria—credibility, transferability, confirmability, and transparency—QLR requires attention to specific temporal indicators, including sustained engagement, temporal sensitivity, and dynamic theoretical saturation. Recognizing the inherently fluid nature of QLR phenomena, the paper introduces the concept of “period-specific saturation” as a pragmatic and defensible endpoint for theory construction within bounded research cycles. This approach acknowledges the provisional yet rigorous nature of theoretical claims developed in dialogue with unfolding time. Finally, the paper discusses future directions, including the temporal integration of mixed methods, technological enhancements such as digital trace mining and natural language processing, and the application of QLR within the Chinese psychological context. By situating QLR within indigenous research on psychological adaptation, cultural value transmission, and intervention processes, this paper calls for culturally sensitive analytical tools that capture relational and familial time. In sum, QLR offers a powerful, systematic pathway for moving beyond static snapshots, enabling researchers to dynamically theorize how psychological life is generated, sustained, and transformed in and through time. References | Related Articles | Metrics

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Psychological resilience from a complex dynamic systems perspective: Approaches and applications of psychopathological symptom networks ZHANG Liying, TONG Xuewei, LIU Luming, LI Yang, WANG Wenchao, WU Xinchun 2027, 35 (1):  119-136.  doi: 10.3724/SP.J.1042.2027.0119 Abstract ( 39 )   PDF (2854KB) ( 75 )   Peer Review Comments Psychological resilience, as a crucial capacity for maintaining mental health, has long faced challenges in its conceptual definition and operationalization. The recent process-oriented perspective views psychological resilience not as a fixed trait, but as a dynamic process involving continuous interactions between individuals and multi-systemic environmental factors. Complex dynamic systems theory provides a theoretical foundation for operationalizing this procedural nature of psychological resilience. Drawing on this framework, the present paper delineates two subsystems: the mental health state system, which is composed of various indicators of mental health state, and the influencing factors system, which consists of factors that affect mental health state. The influencing factors system can drive the mental health state system to undergo mere state fluctuations, or it can reshape the system's characteristics while simultaneously inducing state changes. Within this framework, the present paper defines psychological resilience as a system characteristic exhibited by the mental health state system under the drive of an influencing factors system, specifically encompassing two dimensions: stability and tendency toward health. High-level psychological resilience is characterized by a mental health state system that is both stable and inclined toward health. Network analysis serves as a powerful tool for characterizing this framework. In a network, elements are represented as nodes, which refer to the indicators within a system, and the connections between nodes are referred to as edges. By constructing a mental health state network based on indicators of the mental health state system, the parameters of this network can be used to quantify psychological resilience. In this paper, we propose this approach as the "network resilience paradigm." Current research often focuses the mental health state system on psychopathological symptoms, mapping the spontaneous activation tendency (threshold) of symptom networks onto health tendency and edge weights onto stability. A symptom network characterized by low spontaneous activation tendency (a tendency toward health) and weak edge weights is considered to reflect a higher level of symptom network resilience. This line of work has thereby formed a "symptom network resilience" paradigm as a specific instantiation of the broader network resilience approach. Based on the centrality hypothesis in network analysis, key mental health state indicators that determine symptom network resilience can be identified, either by their spontaneous activation tendency (reflecting health tendency) or by their edge weights (reflecting stability). Influencing factors of psychological resilience can also be incorporated into the psychopathological symptom network to form a hybrid symptom-and-resilience-factor network. This allows researchers to identify risk and protective factors for symptom network resilience. Furthermore, it enables researchers to examine the circular interactions between symptom network resilience and its influencing factors, for example, through the personality-resilience-psychopathology network, which considers the fast-changing process of psychopathological symptoms, the slow-changing process of personality, and the reciprocal influences between them. However, this field is still in its initial stage, with evidence largely confined to theoretical reasoning and simulation studies. The present paper discusses several key limitations and future directions in order of urgency. First, with respect to the quantitative accuracy of theoretical constructs, future research needs to develop statistical methods for quantifying individualized network resilience from multimodal data (physiological, environmental, and self-report) through intensive longitudinal designs, rather than relying on cross-sectional self-report data for group-level network estimations. Second, given the novelty of this paradigm, further work is needed to examine how network resilience compares with other resilience paradigms such as self-report-based psychological resilience. Third, the ultimate goal of this paradigm should be clinical practice and application. Future efforts should aim to achieve monitoring, early warning, and intervention for individualized network resilience with the aid of experience sampling methods and smart mobile devices. Finally, future prospects may also extend to resilience at the group level beyond the individual, as well as to alternative approaches to defining psychological resilience within the complex dynamic systems framework beyond network analysis (such as differential equation models). References | Related Articles | Metrics

Regular Articles

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Psychological mechanisms of AI-supported generative learning: A dynamic synergy model SU Jiajia, YE Haosheng 2027, 35 (1):  137-150.  doi: 10.3724/SP.J.1042.2027.0137 Abstract ( 72 )   PDF (1115KB) ( 134 )   Peer Review Comments Generative artificial intelligence (GenAI) can improve learners' immediate performance while also creating risks for independent thinking, deep understanding, and sustained self-regulation. This apparent contradiction raises a theoretical question: why can similar forms of AI support lead to different, and sometimes opposing, learning outcomes? This article proposes a dynamic synergy model of AI-supported generative learning to explain these divergent effects. Rather than treating motivation, emotion, cognition, metacognition, and creative activity as isolated predictors of learning, the model examines how these processes continuously influence one another during human-AI interaction. Its central contribution is to conceptualize generative learning as a changing system in which external AI support reshapes the relationships among psychological processes without replacing the learner as the agent of meaning construction. The model integrates five psychological processes into three interrelated dimensions. The first is higher-order metacognitive regulation, through which learners monitor their understanding, evaluate information and feedback, and adjust strategies. The second is the coordinated functioning of cognitive, affective, and volitional processes. Cognition supports the selection, organization, and integration of information; emotion shapes the experience of challenge, uncertainty, and progress; and volition sustains goals, effort, and engagement. The third is the embodied generation of meaning through creative activity. Learners develop and reorganize ideas through exploration, task performance, and feedback from relevant environments. These dimensions are not sequential stages. Metacognitive judgments can change motivation and cognitive effort, new understanding can alter emotional responses, and creative action can generate experiences that initiate further cycles of reflection and learning. A further innovation concerns the position of AI within this system. GenAI can simultaneously function as an external cognitive resource, an interactive partner, a participant in distributed cognitive work, and a source of support for creative exploration. Its feedback quality, prompting style, degree of answer completion and automation, and mode of interaction can influence how learners allocate effort and responsibility. The model therefore distinguishes the availability of AI-generated information from the learner's actual engagement in meaning construction. High-quality explanations or fluent outputs do not automatically produce understanding. Their educational value depends on whether learners critically examine, reorganize, and test the information rather than merely accept it. On this basis, the model proposes two theoretically distinct but potentially reversible pathways: scaffolding facilitation and cognitive substitution. In the scaffolding facilitation pathway, AI reduces auxiliary demands and offers prompts or explanatory feedback while learners retain responsibility for essential processes such as reasoning, comparison, evaluation, and self-explanation. Metacognitive monitoring and task-appropriate experiential feedback help transform external assistance into independently usable knowledge and creative capability. In the cognitive substitution pathway, AI increasingly performs the very operations learners need to practice. Learners may obtain polished answers and complete tasks efficiently while engaging less in independent analysis, monitoring, and verification. Thus, improvements in immediate output quality may coexist with weaker knowledge retention, transfer, or autonomous performance. These pathways describe hypothesized configurations of learning processes, not fixed categories of learners or technologies. The allocation of cognitive responsibility is proposed as a central mechanism through which the two pathways are maintained or transformed. Cognitive offloading is not inherently harmful: delegating routine searches or formatting can preserve resources for meaningful learning, whereas outsourcing essential interpretation and judgment may undermine it. Pathway transitions may occur when learners change how they question AI, verify its suggestions, or reclaim responsibility for key decisions. Such changes are expected to depend jointly on AI literacy, prior knowledge, self-regulatory capacity, support design, and task demands. Embodied experience is also important, but its form should match the task: practical action and environmental feedback are especially relevant where learning requires application, experimentation, or situated judgment. The model yields five directions for empirical testing. First, the effects of AI feedback should vary with learners' active processing and metacognitive regulation. Second, different allocations of cognitive responsibility should produce different learning outcomes even at comparable levels of automation. Third, metacognitive monitoring should help explain the persistence of, and transitions between, facilitative and substitutive learning states. Fourth, creative exploration and task-appropriate experiential verification should influence whether AI-supported outputs become personally understood and transferable knowledge. Fifth, immediate task performance and longer-term independent learning should be assessed separately because they may diverge. These propositions can be examined through experimental manipulations of feedback and automation, process-oriented analyses of human-AI interaction, longitudinal designs, and assessments conducted after AI assistance is withdrawn. Overall, the dynamic synergy model moves beyond asking whether GenAI is beneficial or harmful. It asks how external intelligent support reorganizes the learner's psychological system, under what conditions support becomes substitution, and how learners can preserve agency while benefiting from AI. By connecting metacognitive regulation, cognitive-affective-volitional coordination, embodied creative activity, and cognitive responsibility within one framework, the model offers a theoretically integrated and empirically testable account of the dual effects of AI-supported generative learning. References | Related Articles | Metrics

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The developmental mechanisms of L2 lexical representation from “parasitic” to independent: A comparative analysis of connectionist models LI Ziyi, LU Tianyi, YAO Panpan 2027, 35 (1):  151-171.  doi: 10.3724/SP.J.1042.2027.0151 Abstract ( 30 )   PDF (3037KB) ( 35 )   Peer Review Comments In the field of second language acquisition (SLA), a core theoretical issue concerns how second language (L2) lexical representations develop from a first language (L1)-dependent “parasitic” state into a relatively autonomous and independent representational system. To elucidate the cognitive mechanisms underlying this “parasitic-to-independent” developmental continuum, the present review synthesizes classic empirical phenomena and modulating factors documented in L2 lexical acquisition, and systematically contrasts and evaluates the explanatory scope and inherent limitations of six connectionist models of L2 vocabulary learning in accounting for these phenomena. Previous research has documented some classic L2 processing difficulties, such as age-of-acquisition effects, cross-language competition, asymmetric priming, persistent first-language (L1) interference, and early reliance on formal rather than semantic information. Existing models explained these phenomena through varied aspects, including bilingual words recognition and competition, lexical development, computational simulations, and neurocognitive accounts. Nevertheless, the shared cognitive mechanisms underlying these empirical phenomena have yet to be systematically investigated and thoroughly elucidated. The main innovation of this study is to align these approaches within a unified connectionist framework and to reinterpret the parasitic-to-independent transition as a process of distributed weight reconfiguration in the bilingual lexical network. Building on Hall's (2002) Parasitic Hypothesis of Vocabulary Development, the paper proposes that newly encountered L2 forms initially lack sufficiently strong and differentiated connections with semantic representations. Learners therefore rely on established L1 lexical-semantic pathways as representational scaffolds. From a connectionist perspective, this dependence reflects weak L2-specific connection weights rather than the absence of L2 representations. Because activations generated by the L2 forms are unstable and poorly differentiated, accesses to meaning are mediated primarily through entrenched L1 representations. This network perspective can explain several behavioral phenomena that were often treated separately, including weak L2 activation, strong L1 interference, asymmetric L1-L2 priming, and sensitivity to orthographic and phonological forms rather than semantics. The central theoretical contribution of the paper is to conceptualize lexical development as the gradual strengthening of direct L2 form-meaning mappings, accompanied by a relative reduction of the reliance on L1-mediated routes. Within the framework of connectionist, increased exposure and proficiency play a more important role than merely improve lexical access speed. They reorganize activation patterns, stabilize L2-specific pathways, reduce representational overlap, and increase the efficiency of direct semantic access. The independence of L2 lexical processing, therefore does not require the formation of fully separate L2 lexicons. Rather, it emerges through the self-organization of increasingly stable L2 pathways within an interactive bilingual network. The relationship between parasitic and independent representation is consequently treated as a developmental continuum rather than a categorical distinction. A further innovation lies in the mechanism-level comparison of six influential approaches to L2 lexical acquisition. The Bilingual Interactive Activation Model is interpreted as an account of the competitive dynamics produced by differences in representational strength across languages. Lexical Entrenchment Account explains how repeated L2 exposure gradually stabilizes and entrenches lexical representations. The Fuzzy Lexical Representations hypothesis is incorporated as an account of fuzzy lexical representations resulting from insufficient or inaccurate encoding. The Ontogenesis Model of the L2 Lexical Representation is situated within a distributed architecture in which orthographic, phonological, and semantic representations develop at different rates. The Developmental Lexicon-II Model is treated as a computational realization of fuzzy representation. Finally, the Complementary Learning Systems model provides a neurocognitive substrate for this trajectory: rapid hippocampal learning supports the initial encoding of novel L2 words, whereas slower neocortical consolidation enables their gradual integration, differentiation, and stabilization. By aligning these approaches under shared computational principles, the paper argues that they are not competing explanations but complementary descriptions of the same developmental process at different levels. Behavioral theories describe changes in priming, interference, and lexical access; representational accounts characterize changes in encoding quality and lexical organization; computational models formalize the adjustment of connection weights; and neurocognitive frameworks specify the learning systems that support these changes. This cross-level synthesis clarifies conceptual overlap, and identifies common mechanistic denominators across theories that have traditionally been considered separately. In conclusion, this paper offers a mechanism-driven synthesis of classic theories of L2 lexical acquisition by locating the transition from parasitic to relatively independent representation within a unified connectionist architecture. Its originality lies in demonstrating how existing behavioral, representational, computational, and neurocognitive accounts can be organized around shared principles of distributed representations, competitive activations, experience-dependent weight adjustments, and complementary learning. The resulting framework provides a coherent developmental explanation of L2 lexical independence and a foundation for future longitudinal, computational, neurocognitive, and pedagogical research. References | Related Articles | Metrics

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Retrieval practice in clinical populations: A systematic review RAN Guangming, LI Yi, CHEN Ying 2027, 35 (1):  172-185.  doi: 10.3724/SP.J.1042.2027.0172 Abstract ( 36 )   PDF (677KB) ( 56 )   Peer Review Comments Retrieval practice is a learning strategy that improves memory efficiency by actively retrieving information from memory, typically through testing or free recall. Some clinical populations often present with memory and other cognitive impairments. This paper provides the first systematic review of research on retrieval practice in clinical populations, covering studies published between January 2020 and July 2025, and includes 22 studies in total. The findings indicate that, among developmental disorder populations, the positive effects of retrieval practice are manifested in promoting vocabulary memory in children who are deaf and hard of hearing (DHH), children with developmental language disorder (DLD), and patients with developmental dyslexia (DD), and full-text recall in patients with attention deficit/hyperactivity disorder (ADHD). Among acquired and neurodegenerative disease populations, this strategy helps improve long-term episodic memory in persons with Alzheimer's disease (AD), lexical-semantic comprehension in people with aphasia (PWA), and memory performance for names in stroke survivors (SS), as well as enhancing new information encoding efficiency in patients with schizophrenia (SP) and memory retention in patients with early psychosis (EP). Additionally, retrieval practice also exerts certain facilitative effects on health information memory in persons with HIV and word memory in children with Down syndrome (DS). However, in certain disorders, its effects may be moderated. Firstly, preliminary evidence from studies of AD and SP suggests that the clinical benefits of retrieval practice may be moderated by the core cognitive deficits inherent to the disorder. Specifically, when deficits are predominantly at the encoding stage, external support may be needed for the retrieval practice to improve memory efficiency, whereas when deficits lie primarily in retrieval and interference control, retrieval practice itself can serve as a relatively effective rehabilitation approach. Secondly, evidence from children with DLD and patients with ADHD tentatively indicates that material complexity and ecological validity may moderate intervention outcomes. When material complexity exceeds patients' cognitive load capacity, the additional cognitive costs of retrieval may outweigh its memory consolidation benefits; meanwhile, retrieval practice tends to be more facilitative in contexts that resemble everyday learning. Thirdly, preliminary findings from patients with ADHD and SP indicate that the effects of retrieval practice may be shaped by other therapeutic interventions. For instance, the cognitive readiness afforded by pharmacotherapy could be an important prerequisite for retrieval practice to take effect, while external physical interventions (e.g., tDCS) that optimize specific brain region functioning may provide a favorable neural environment for it. Future studies could further validate the moderating factors discussed above and focus on the following aspects. Firstly, future studies should investigate the benefits of retrieval practice for memory deficits in comorbid populations. Priority could be given to clinically prevalent comorbidity patterns, such as schizophrenia comorbid with anxiety disorders. This would help verify the effectiveness of retrieval practice and clarify its impact on shared cognitive deficits like memory impairments. Comorbidity characteristics may also guide intervention optimization in two ways: dynamically adjusting retrieval intervals to address memory decline, and targeting overlapping cognitive impairments across comorbidities to improve clinical relevance. Secondly, further exploration of the indirect effects of retrieval practice in clinical populations, such as strengthening metacognitive monitoring and self-efficacy, could open new avenues for clinical cognitive intervention. Thirdly, the roles of individual difference variables in retrieval practice among clinical populations warrant investigation, including working memory capacity, fluid intelligence, and effort, thereby providing an empirical basis for developing personalized intervention protocols tailored to patients' cognitive profiles. To advance the clinical translation of retrieval practice, this paper proposes the following recommendations. Firstly, intervention goals should be clearly defined and appropriate materials selected accordingly. For example, for lexical-semantic comprehension deficits in PWA, minimal semantic pairs can be used, with each pair consisting of two semantically close but distinct concepts and their corresponding pictures. Secondly, strategy education should be integrated into the intervention process to enhance adherence. Rather than simple task training, the design of retrieval practice interventions may incorporate a comprehensive guidance system covering explanation of principles (knowledge transfer), hands-on experience (skill training), and positive reinforcement (motivational regulation). Thirdly, a stepwise implementation strategy is recommended. In the introduction phase, retrieval practice can supplement patients' existing cognitive training. For instance, at the end of each therapy session, patients may be asked to recall core knowledge points or events of the day. In the expansion phase, the dosage and task difficulty can be gradually increased, for example, by extending the duration of retrieval practice and adopting more functional, personalized materials such as memorizing new names or daily medication schedules. In the generalization phase, the emphasis shifts to strategy transfer, as patients begin to independently apply retrieval practice in daily life, such as spontaneously recalling the day's events before sleep. Through this process, retrieval practice can be internalized as an autonomous, self-regulated learning tool, thereby achieving the goals of clinical intervention. References | Related Articles | Metrics

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Subjective and objective drivers of cognitive effort investment and the moderating role of individual differences YANG Qian, TAN Jiaxin, CHEN Lunzixuan 2027, 35 (1):  186-203.  doi: 10.3724/SP.J.1042.2027.0186 Abstract ( 33 )   PDF (719KB) ( 42 )   Peer Review Comments Cognitive effort investment is not simply a response to how difficult a task is. It is a dynamic decision process in which people evaluate task demands, anticipated benefits, subjective costs, efficacy, and current internal states before allocating control. This review advances an integrative account of this process by organizing recent theories and findings around three linked questions: how objective task demand triggers effort, how subjective experience enters effort decisions, and why the same objective demand elicits different levels of effort across individuals. Rather than treating cognitive effort as either an objective amount of consumed resource or a purely introspective feeling, the review argues that effort investment should be understood as a cost-benefit decision embedded in subjective monitoring and individual motivational systems. First, we make a clearer separation between theories of the origins of subjective cost and theories of effort-allocation mechanisms. Accounts such as the opportunity-cost model address why cognitive effort is experienced as costly: controlled processing occupies limited computational mechanisms and therefore prevents alternative, potentially valuable uses of cognition. Related information-theoretic and neural-control perspectives further suggest that effort costs may arise from information selection, precision regulation, neural state transitions, and control-energy requirements. By contrast, Motivational Intensity Theory and the Expected Value of Control framework primarily explain how individuals decide whether and how much control to allocate once costs, benefits, and feasibility have been represented. This distinction prevents the common conflation between computational subjective cost and phenomenological felt effort, and it provides a more precise theoretical foundation for studying cognitive effort. Then, we specify both the utility and the limits of objective task difficulty. Objective difficulty remains a basic trigger of cognitive effort, as shown in conflict, working-memory, and task-switching paradigms as well as in pupillary, cardiovascular, and neural indicators. However, objective indicators cannot by themselves identify effort investment. Longer reaction times may indicate active effort, but they may also reflect lower ability or inefficient processing; high accuracy may indicate high effort, but it may also reflect an easy task for a particular individual. Moreover, common behavioral paradigms often show low convergent validity, suggesting that they capture partially different constructs, such as effort avoidance, effort discounting, reasoning ability, or cognitive motivation. To reduce this ambiguity, the review highlights a methodological shift from absolute task difficulty to relative task demand. Individualized difficulty calibration, based on baseline ability, staircase procedures, response-time limits, or target accuracy, allows researchers to compare individuals at more equivalent success probabilities or subjective load levels. This approach makes objective difficulty interpretable relative to individual capacity. Moreover, we underscore the role of subjective experience during cognitive effort investment. The review differentiates three dimensions: perceived difficulty, perceived effort, and emotional experience. Perceived difficulty reflects metacognitive evaluation of task demand and coping capacity; perceived effort reflects online monitoring of resource mobilization; emotional experience captures whether effort is felt as aversive, rewarding, threatening, or meaningful. These dimensions are related but not interchangeable. They can diverge from objective task parameters, predict later effort allocation beyond objective difficulty, and show dynamic carry-over across trials or task phases. This perspective reframes subjective experience as an internal input to effort decision-making, not merely as a by-product of task performance. On this basis, we propose a testable dual-path framework for individual-difference moderation. Need for Cognition is proposed to operate mainly through a value/cost evaluation pathway: individuals high in Need for Cognition are more likely to assign intrinsic value to complex thinking, show weaker cognitive effort discounting, and sustain control when tasks are challenging. Growth versus fixed mindsets are proposed to operate mainly through an attributional/efficacy interpretation pathway: growth mindset makes difficulty, effort, and failure more likely to be interpreted as signals of learning and improvement, whereas fixed mindset makes them more likely to be interpreted as evidence of limited ability. These two pathways are conceptually independent but may interact. High Need for Cognition combined with growth mindset may support persistent engagement by increasing challenge value and reducing failure threat, whereas low Need for Cognition combined with fixed mindset may accelerate disengagement. Overall, this review shifts the focus of cognitive effort research from a task-centered view to a dynamic person-task-state framework. Future work should systematically dissociate objective and subjective effects, combine behavioral, self-report, pupillary, cardiovascular, EEG, and neuroimaging measures, and model single-trial dynamics with multilevel or computational approaches. Such work can clarify how effort costs are constructed, how subjective experiences guide later control allocation, and how motivational traits and belief systems shape effort decisions. The framework also has implications for education, clinical intervention, and human-AI interaction, where the goal is not simply to reduce effort but to preserve meaningful, well-calibrated cognitive challenges that people are willing to invest in. References | Related Articles | Metrics

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The spillover effect: Conceptual clarification, process mechanism, and their extension in the digital age XIONG Yuping, ZHOU Xinqi 2027, 35 (1):  204-221.  doi: 10.3724/SP.J.1042.2027.0204 Abstract ( 40 )   PDF (708KB) ( 39 )   Peer Review Comments This review develops an integrative process-oriented framework for the psychological spillover effect, with the aim of addressing three persistent challenges that have long impeded progress in the field: conceptual ambiguity, mechanistic fragmentation, and insufficient responsiveness to digital-age transformations. The central source of conceptual confusion in spillover research lies in its indiscriminate overlap with related constructs such as compensation, crossover, and ripple effects. To resolve this issue, this article reconstructs the conceptual architecture of spillover by proposing a four-dimensional discriminative framework anchored on transmission subject, transmission direction, intentionality of outcome, and diffusion structure. Within this framework, spillover is operationalized as an intraindividual, same-direction, unintentional, point-to-point transfer process, thereby distinguishing it from compensation, which is characterized as motivation-driven reverse complementarity; from crossover, which involves inter-individual transmission; and from ripple effects, which entail network-based multi-directional diffusion. By establishing clear discriminant criteria, this framework not only enables more standardized and rigorous use of the spillover construct, but also facilitates the integration and comparison of empirical findings across diverse subfields. Existing literature has treated cognitive dissonance, self-perception, social learning transfer, and other theoretical accounts as parallel explanatory mechanisms, yet a unifying meta-mechanism has remained absent. To fill this gap, this article draws upon Lewin's field theory and the S-O-R framework to propose a process mechanism model composed of four functional components distributed across three sequential stages: structural prerequisites, trigger-transformation stage, diffusion-transmission stage, and effect-manifestation stage. The structural prerequisite, psychological boundary permeability, functions as the channel architecture that enables cross-contextual transfer. This article elaborates on its dual expression: baseline width, which refers to trait-level individual differences in boundary strength, and dynamic openness, which denotes state-level fluctuations under conditions of resource depletion or resource gain. Notably, the hypothesis of asymmetrically permeable boundaries is incorporated to demonstrate that directional asymmetry in permeability directly determines spillover probability, activation threshold, and the associated self-regulatory costs. The trigger-transformation stage converts external events into transferable psychological content through a sequential chain of event encoding, meaning association, and state activation. Processing elasticity theory is introduced to account for variability in processing depth: high-intensity negative events may compress meaning association into automated schema activation, whereas deliberative contexts tend to extend controlled processing. The diffusion-transmission stage addresses the critical question of how psychological states originating in Domain A maintain their influence upon entering Domain B. Cross-contextual reactivation is proposed as the unifying meta-mechanism, encompassing three subprocesses: state residue, which represents persistent background activation; contextual transition, which involves decoupling from the prior context and recoupling to the new one; and state reactivation, which refers to foreground activation triggered by Domain B cues. Three specific reactivation pathways are identified and shown to operate as interconnected cascades rather than independent routes: cognitive reactivation operates through consistency principles, emotional reactivation proceeds via affect carryover, and behavioral reactivation occurs through observational learning and generalization. Finally, the effect-manifestation stage captures observable psychological and behavioral changes in Domain B, which are categorized according to influence direction, distinguishing constructive from obstructive effects, and according to influence content, differentiating emotional, cognitive, and behavioral outcomes. Particular attention is given to the mechanistic asymmetry between negative spillover, which is driven by resource depletion and consequent boundary breakdown, and positive spillover, which emerges from resource gain and facilitates boundary extension. The proposed process model is further extended to human-computer interaction contexts without disrupting its core architecture. Digital technologies fundamentally reshape each stage of the process. Psychological boundaries evolve from managing switches between discrete domains to regulating interference among concurrently active threads. The trigger-transformation stage shifts from a dyadic individual-event interaction to a triadic individual-algorithm-event interaction, wherein algorithmic curation substitutes for autonomous selective attention and manufactured meaning resonance replaces self-constructed association. The diffusion-transmission stage transforms from single residue decay to algorithm-anchored multi-residue competition accompanied by fragmented transitions. The effect-manifestation stage exhibits amplified asymmetry: positive spillover becomes superficial and fragile due to fragmented content consumption, whereas negative spillover becomes covert yet cumulatively explosive through persistent micro-arousal. Novel moderators specific to human-AI interaction are further identified, including the degree of anthropomorphism, perceived interaction control, and consistency of interaction history. This framework advances spillover research in three significant ways. First, it provides a unified conceptual grammar that replaces ambiguous construct usage with actionable discriminant criteria. Second, it transforms spillover from a static phenomenological label into a testable temporal process with clearly specified mechanistic targets at each phase. Third, it extends the model to digitally mediated contexts without abandoning its core architecture, thereby equipping future research to address hybrid reality, algorithm-infused environments, and human-AI boundaries. Future empirical priorities should include validating the three-phase model through experimental and longitudinal designs, establishing phase-specific moderators via meta-analytic synthesis, and translating mechanistic insights into targeted interventions across organizational, environmental, and mental health domains. References | Related Articles | Metrics

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The psychological mechanisms by which algorithmic authority influences moral disengagement in Human-AI collaborative decision-making LI Chunlei, XIN Sufei, SHENG Liang 2027, 35 (1):  222-233.  doi: 10.3724/SP.J.1042.2027.0222 Abstract ( 40 )   PDF (565KB) ( 48 )   Peer Review Comments Algorithmic systems increasingly participate in high-stakes decisions in healthcare, judicial practice, and organizational management. When such decisions produce adverse consequences, human decision-makers may attribute their actions to algorithmic recommendations and distance themselves from moral responsibility. Yet empirical findings point in opposite directions: algorithmic authority can facilitate responsibility displacement and moral disengagement, but can also heighten moral vigilance and inhibit disengagement. Existing accounts explain reliance on and resistance to algorithms separately, leaving these contrasting consequences unresolved. This article integrates research on authority, automation bias, mind perception, responsibility attribution, and moral disengagement to propose a Dual Cognitive Appraisal Model of Algorithmic Authority and Moral Disengagement. The model treats algorithmic authority as a subjective relation rather than an inherent technical property. It arises when individuals perceive an algorithm as accurate and professionally competent, grant its judgments interpretive priority, and become more inclined to rely on its recommendations. The model's central proposition is that algorithmic authority does not directly determine moral disengagement. Instead, it reshapes self-responsibility attribution, a context-dependent judgment about whether the individual remains the final decision-maker and is accountable for accepting an algorithmic recommendation and its consequences. Reduced self-responsibility attribution allows individuals to position themselves as executors, transfer responsibility to the algorithm or system, and activate state moral disengagement. Strengthened attribution reaffirms their status as responsible agents and constrains moral disengagement. This mechanism connects two complementary pathways previously discussed separately. In the cognitive offloading and agentic-state pathway, the perceived objectivity and reliability of algorithmic advice encourage individuals to outsource judgment and reduce independent verification. This creates conditions for responsibility displacement and moral disengagement. In the value-conflict and responsibility-return pathway, individuals recognize that an algorithm lacks the experiential capacities required of a complete moral agent. When an algorithmic recommendation conflicts with salient moral values, they are more likely to scrutinize or reject the recommendation and to reclaim responsibility for the final decision. Algorithm aversion alone, however, does not demonstrate responsibility return because rejection may arise from doubts about accuracy, threats to professional autonomy, or general resistance to technology. Responsibility return requires stronger self-responsibility attribution. The direction of self-responsibility attribution is jointly shaped by two appraisals. Explainability defines technical cognitive appraisal by determining whether individuals can understand and scrutinize the basis of an algorithmic recommendation. Moral conflict intensity defines moral cognitive appraisal by determining whether the recommendation creates tension among algorithmic advice, personal moral standards, and responsibility for the outcome. When explainability and moral conflict are both low, individuals have limited grounds and little motivation to examine the recommendation. They are therefore more likely to defer judgment to the algorithm, weaken self-responsibility attribution, and disengage morally through responsibility displacement. When both are high, individuals have both the capacity and the motivation to scrutinize the recommendation, recognize the moral significance of its consequences, and reaffirm their own responsibility, thereby inhibiting moral disengagement. The two mixed conditions reveal mismatches between scrutiny capacity and motivation. High explainability with low moral conflict provides the capacity to inspect the algorithm but may not motivate scrutiny; low explainability with high moral conflict motivates scrutiny but restricts its means. Responsibility attribution under these mixed conditions remains theoretically indeterminate and requires direct examination. The proposed model advances current theorizing in three respects. First, it brings the facilitating and inhibiting effects of algorithmic authority into a single framework. Second, it identifies self-responsibility attribution as their shared proximal mechanism, shifting attention from whether people accept or reject algorithms to how they define their responsibility after an adverse outcome. Third, it formulates an interaction hypothesis in which technical and moral appraisals jointly determine the direction of responsibility attribution. This integration extends accounts centered on obedience to authority, automation bias, or mind perception by specifying when reliance may be disrupted and when moral responsibility may return to the human decision-maker. It also translates a system-level responsibility gap into an individual-level process of post-decision moral self-regulation. The model is derived from indirect empirical evidence and theoretical reasoning, and its complete causal chain has not yet been directly tested. Future studies should manipulate algorithmic authority, explainability, and moral conflict intensity in morally consequential human-AI tasks while measuring self-responsibility attribution and state moral disengagement. They should also examine whether decision-control arrangements, perceived algorithmic mind, organizational responsibility structures, and cultural contexts alter the proposed pathways. By distinguishing the conditions under which algorithms become a shield for responsibility from those under which they prompt renewed human accountability, the model provides testable hypotheses for psychological research and a basis for designing human-AI systems that preserve meaningful scrutiny, clear responsibility boundaries, and human moral agency. References | Related Articles | Metrics

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Integrating and expanding the effects of gratitude expression in organizations CAO Limei, CHENG Xiang, YAO Yuewei, YIN Rui, CHEN Weihua, QIN Xin 2027, 35 (1):  234-247.  doi: 10.3724/SP.J.1042.2027.0234 Abstract ( 34 )   PDF (770KB) ( 34 )   Peer Review Comments Gratitude is a common psychological experience across time and cultures and has been studied as both a personality trait and an emotional state. Existing reviews have primarily focused on trait gratitude and state gratitude, with particular attention to their beneficial effects on individuals themselves. However, gratitude can also be expressed through interpersonal behavior. In organizational contexts, gratitude expression refers to the verbal and nonverbal behaviors through which individuals convey their gratitude to others. A growing body of empirical research has examined gratitude expression, yet this literature has not been systematically integrated. Moreover, although gratitude expression is generally assumed to convey positive social signals and produce beneficial psychological and behavioral outcomes, existing studies show that its effects may vary across contexts. The mechanisms underlying such variation also remain unclear. Thus, a systematic understanding is needed of both the effects of gratitude expression in organizations and the mechanisms that explain why these effects differ across contexts. To clarify the effects of gratitude expression, we systematically review existing empirical research and develop a classification framework along two dimensions: the scope of effects (intrapersonal vs. interpersonal) and the valence of effects (positive vs. negative). This framework distinguishes between the effects of gratitude expression on the expresser and those on others (i.e., recipients or observers), and integrates both positive and negative outcomes. Based on these two dimensions, we identify four types of effects: intrapersonal positive effects, interpersonal positive effects, interpersonal negative effects, and intrapersonal negative effects. We further organize these effects across four domains: health, ethics, relationships, and performance. Together, this framework provides a structured overview of the current literature on gratitude expression. To explain why the effects of gratitude expression differ across contexts, we draw on social relational theory and propose an explanatory framework centered on communal and exchange relationships. We argue that the key lies in how individuals perceive their relationships during interpersonal interactions. When gratitude expression strengthens perceptions of a communal relationship, it generally promotes positive outcomes. In contrast, when contextual cues (e.g., competitive contexts) make an exchange relationship more salient, gratitude expression may produce unintended negative consequences. Importantly, communal and exchange relationships are not mutually exclusive; rather, their relative salience varies across contexts. We therefore categorize the contextual cues that shape relational perceptions into two types: individual cues and interactional cues. These cues influence individuals' information-processing orientations and, in turn, shape the relative salience of communal versus exchange relationship perceptions. Building on the literature review and theoretical framework, we propose three directions for future research. First, future research could further examine intrapersonal and negative effects that remain insufficiently understood. Second, future research could develop a more systematic framework for understanding boundary conditions by examining how different situational characteristics shape relational perceptions and outcomes. Third, future research could adopt a temporal perspective to examine how relational perceptions accumulate and change across repeated interactions and how these dynamics shape the long-term effects of gratitude expression. This study systematically integrates and extends the literature on gratitude expression in organizations. First, we develop a two-dimensional classification framework that provides a structured map of the diverse effects of gratitude expression. Compared with existing reviews, this framework not only integrates the positive effects but also distinguishes between interpersonal and intrapersonal negative effects and their underlying psychological mechanisms. In doing so, it moves the literature beyond a predominantly positive view toward a more balanced and comprehensive understanding of gratitude expression. Second, we propose a theoretical framework to explain why gratitude expression may produce different effects across contexts. By focusing on whether gratitude expression strengthens perceptions of a communal relationship or makes an exchange relationship more salient, this framework offers a unifying explanation for previously fragmented findings and provides a theoretical basis for systematically examining the boundary conditions of gratitude expression effects. References | Related Articles | Metrics

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The effects of visual cues on sustainable consumer behavior and their underlying mechanisms SUN Jin, ZHANG Jiajia 2027, 35 (1):  248-270.  doi: 10.3724/SP.J.1042.2027.0248 Abstract ( 38 )   PDF (959KB) ( 45 )   Peer Review Comments Sustainable consumption has become an increasingly important area of research in consumer behavior and marketing. A central question is how firms and policymakers can communicate products' sustainability attributes in ways that help consumers recognize environmental benefits without increasing the cognitive costs of decision making. Visual cues are particularly relevant to this question because they shape product impressions rapidly and intuitively, guiding consumers' attention, inferences, and evaluations before more deliberative processing takes place. A growing body of research has therefore examined how visual elements, including hue, saturation, brightness, typeface, logos, geometric features, and material cues, shape consumer responses to sustainable products and consumption practices. Despite growing interest in visual marketing and sustainable consumption, existing research remains fragmented across discrete visual elements and isolated sustainable consumption behaviors, limiting understanding of how different types of visual cues shape sustainable consumption across contexts and behavioral stages and making it difficult to reconcile seemingly inconsistent findings. For example, although green color can signal environmental friendliness, it may also lead consumers to draw unfavorable inferences about product performance. Anthropomorphic design can encourage consumers to cherish and use products longer by imbuing them with a sense of life, but it may also reduce repair and maintenance intentions by suggesting that products can recover on their own. These mixed findings underscore the need for an integrative framework that explains when, why, and how visual cues shape sustainable consumption. To address this gap, the present research reviews literature on visual cues in sustainable marketing and develops a conceptual framework for understanding how visual elements influence sustainable consumption. Drawing on Sample et al.'s (2020) classification of visual elements and recent work on visual cues in sustainable consumption, we organize color, imagery, typeface, logos, three-dimensional form, and material into two broad categories: graphic cues and structural cues. Graphic cues, such as color, imagery, typeface, and logos, primarily convey symbolic meaning, whereas structural cues, such as product form and material, communicate physical and functional information about an object. Building on this framework, we systematically review how graphic and structural cues influence sustainable consumption across three behavioral stages: green purchasing, sustainable use, and circular disposal. Further, to integrate the diverse mechanisms identified in prior research, we build on the literature on desirability and feasibility in sustainable consumption decision making to develop an explanatory framework organized around two fundamental judgments, value-meaning evaluation and implementation-feasibility assessment. Specifically, graphic cues primarily influence sustainable consumption through the desirability pathway, with conceptual metaphor and cognitive schema accounting for their effects. By contrast, structural cues primarily operate through the feasibility pathway, with affordance and automatic categorization explaining how these cues influence sustainable consumption decisions and behaviors. We further synthesize boundary conditions related to individual differences, product characteristics, and consumption contexts. Finally, we identify several directions for future research, including comparing the relative effects of graphic and structural cues, extending the classification of visual cues, examining a broader range of sustainable consumption behaviors, and advancing research on multisensory integration in digital contexts and interactive media. This review makes three contributions to the literature. First, it advances research on visual cues in sustainable consumption by moving beyond aesthetic responses and drawing on a desirability-feasibility perspective to explain how graphic and structural cues operate through distinct psychological routes. Second, this review provides a structured account of the mechanisms through which visual cues influence sustainable consumption. Rather than treating visual elements such as color, typeface, logos, material, and shape as isolated cues, we distinguish between graphic cues and structural cues based on the primary information they convey. This distinction helps explain why some cues are more relevant to green purchasing, whereas others are more closely tied to sustainable use or circular disposal, and provides an organizing lens for integrating the psychological processes underlying their effects. Third, this review clarifies the boundary conditions under which visual cues shape sustainable consumption. By synthesizing individual differences, product characteristics, and contextual moderators across purchase, use, and disposal, we extend prior research beyond green purchasing and identify when different visual cues are more or less likely to promote sustainable behavior. 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