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The effect of the AI-augmented scientific approach to entrepreneurial decision-making on new venture idea formation TAO Yida, YU Xiaoyu 2026, 34 (12):  2121-2138.  doi: 10.3724/SP.J.1042.2026.2121 Abstract ( 66 )   PDF (781KB) ( 179 )   Peer Review Comments The rapid advancement of artificial intelligence (AI) is reshaping how entrepreneurs generate, test, and refine new venture ideas. Concurrently, the scientific approach to entrepreneurial decision-making—which encourages entrepreneurs to think, act, and decide like scientists by building theories of value creation, formulating testable hypotheses, collecting evidence through experimentation, evaluating results rigorously, and updating theories accordingly—has attracted increasing scholarly attention. Despite the growing convergence of these two streams of research, the theoretical underpinnings of the scientific approach in AI contexts and the mechanisms through which it shapes new venture idea formation remain underexplored. To address this gap, this research proposes the concept of the “AI-augmented scientific approach to entrepreneurial decision-making” and investigates how this approach affects new venture idea formation. Drawing on the entrepreneur-as-scientist perspective, we present four interconnected sub-studies that reveal how entrepreneurs leverage this approach to iteratively refine new venture ideas toward convergence. Study 1 introduces the concept of the “AI-augmented scientific approach to entrepreneurial decision-making” and elaborates on its theoretical underpinnings. Building on the five-step scientific decision-making framework—Theory, Hypothesis, Evidence, Evaluation, and Decision—we illustrate how AI is integrated into each step. On the one hand, AI enhances decision-making by expanding the space for theorizing, accelerating hypothesis generation, enabling market simulation, reducing cognitive biases, and improving theory-evidence consistency during theory updating. On the other hand, AI also introduces several risks, including over-reliance on data-driven theorizing, inferential disconnects between theories and hypotheses, inadequate validation due to the lack of real-world feedback, deviations from entrepreneurs’ strategic commitments, and convergence in decision-making that reduces the dispersion of theories. Overall, the effects of the AI-augmented scientific approach are highly context-dependent, varying across entrepreneur-level and task-level contexts. Study 2 investigates the effects of the AI-augmented scientific approach on new venture idea construction during the theory-building stage. We argue that this approach enhances the innovativeness of new venture ideas by combining the systematic logic of the scientific approach with AI’s pattern recognition capabilities. However, because AI relies heavily on high-frequency patterns in training data, it tends to generate statistically probable solutions while overlooking low-probability but potentially critical insights. Consequently, it reduces idea innovativeness dispersion by producing solutions concentrated around dominant patterns. With respect to theory-market alignment, the combination of the scientific approach’s emphasis on systematic observation and AI’s sophisticated data analytics improves entrepreneurs’ structured understanding of complex markets, thereby enabling stronger alignment between value-creation theories and market needs. We further argue that entrepreneurial imaginativeness moderates these relationships. Study 3 investigates the effects of the AI-augmented scientific approach on new venture idea testing during the theory-testing stage. We argue that this approach increases the degree of experimentation because the scientific approach emphasizes proactive evidence seeking, whereas AI enhances rigor in experimental design and reduces biases in interpreting results. Moreover, AI accelerates experimentation through automated data processing, predictive modeling, and timely feedback generation. We further argue that entrepreneurial resources—including both resource availability and resource bricolage—serve as boundary conditions that moderate these effects. Study 4 investigates the effects of the AI-augmented scientific approach on new venture idea updating and convergence during the theory-updating stage. We argue that this approach is more likely to trigger entrepreneurial pivots because AI generates recommendations that are relatively independent of entrepreneurs’ psychological attachments, thereby enabling more objective evaluation of evidence. Moreover, this approach is more likely to induce incremental rather than radical pivots, as the scientific framework provides structured reference points for adjustment while AI enhances decision efficiency within this framework. Crucially, by influencing the iterative cycle of theory building, theory testing, and theory updating, the AI-augmented scientific approach facilitates new venture idea convergence and improves the quality of converged ideas in terms of both innovativeness and economic value. This research makes three theoretical contributions. First, it conceptualizes the AI-augmented scientific approach to entrepreneurial decision-making by embedding AI into each step of the “Theory-Hypothesis-Evidence-Evaluation-Decision” process. In doing so, this research provides a refined theoretical understanding of the scientific approach in AI contexts and lays the foundation for future empirical investigation. Second, drawing on the entrepreneur-as-scientist perspective, this research unpacks the mechanisms through which the AI-augmented scientific approach influences the quality of converged new venture ideas. Specifically, it demonstrates how this approach shapes stage-specific variables across theory building, theory testing, and theory updating. In doing so, this research deepens our understanding of how—not merely whether—the AI-augmented scientific approach affects new venture idea formation, thereby opening the black box of how this approach works. Third, this research enriches the literature on new venture ideas by examining both innovativeness—including its dispersion—and economic value. It further conceptualizes new venture idea formation as a dynamic process in which ideas converge iteratively through cycles of theory building, testing, and updating. This dynamic view also helps clarify the boundary between new venture idea research and creativity research. 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AI work identity threat: The construct, antecedents, and double-edged sword effect HUANG Jie, LI Yali, ZHOU Kong, ZHU Run, ZHANG Yongjun 2026, 34 (12):  2139-2151.  doi: 10.3724/SP.J.1042.2026.2139 Abstract ( 124 )   PDF (555KB) ( 122 )   Peer Review Comments With the rapid advancement and widespread application of artificial intelligence (AI), AI technologies are increasingly encroaching upon domains once considered uniquely human, and employees increasingly experience threats to their professional identity as a result. Although AI work identity threat has emerged as a salient organizational phenomenon, its connotation, antecedents, and behavioral consequences remain theoretically underdeveloped: existing research largely conflates AI-induced identity threat with other threat types (e.g., stigma-related threat), overlooking AI's unique characteristics as a threat source. Grounded in identity threat process theory, this research develops an integrative framework—organized around “novel dimensions, triggering mechanisms, and consequences”—through three interrelated studies. Study 1 attempts to explore what AI work identity threat actually entails. Using the symbolic-instrumental framework as a heuristic lens, and jointly considering the uniqueness of the work-identity domain and of the AI threat source, this study attempts to identify the possible new dimensions of AI work identity threat. In the AI context, threat may arise not only at the symbolic level of “who am I / how valuable am I,” but also at the instrumental level concerning whether an individual can retain an irreplaceable position as labor/human capital. Accordingly, this study divides AI work identity threat into two interrelated yet distinguishable categories: symbolic threat, referring to challenges to employees' self-concept, such as perceived threats to identity value, identity meaning, and identity enactment; and instrumental threat, referring to challenges to one's capability/function as labor, such as perceived threats to identity uniqueness and identity continuity. This reconceptualization broadens the understanding of identity threat and extends the explanatory scope of identity threat theory. Study 2 focuses on two core AI characteristics—anthropomorphism and explainability—to examine the triggering mechanisms of AI work identity threat, proposing two parallel pathways. On the self-referenced pathway, AI anthropomorphism makes AI appear more human-like and capable, lowering employees' perceived employability (internal and external), which heightens AI work identity threat. On the leader-referenced pathway, AI explainability increases the perceived credibility of AI outputs and, correspondingly, employees' perception of their leader's trust in AI; this perceived leader AI trust likewise heightens AI work identity threat, as employees infer a greater risk of being judged replaceable. AI application pattern (automation vs. augmentation) is proposed as a boundary condition: automation, which takes over tasks entirely, strengthens both pathways, whereas augmentation, which mainly extends human capabilities, weakens them. This dual-pathway, boundary-conditioned model offers a more fine-grained, characteristic-level explanation of AI work identity threat than prior research's coarse focus on the degree and frequency of technological change. Study 3 uncovers a “double-edged sword” effect of AI work identity threat on employee innovative behavior—an outcome central to organizational competitiveness yet previously unexamined in this context. Rather than assuming a uniformly negative consequence, this study proposes that the behavioral translation of AI work identity threat hinges on employees' trait regulatory focus. Employees high in trait prevention focus are theorized to respond via work withdrawal—a defensive, identity-protective strategy—which suppresses innovative behavior. Employees high in trait promotion focus are theorized to respond via job crafting—an identity-expansive strategy encompassing cognitive, relational, and task reshaping—which promotes innovative behavior. By specifying this individual-difference boundary condition and the underlying mediating mechanisms, the dual-pathway model reconciles the largely negative findings of quantitative identity threat research with the more optimistic, qualitative literature on adaptive identity responses. In summary, this research contributes to identity threat theory in three ways. First, it theorizes new dimensions of instrumental threat within AI work identity threat, extending the content coverage of existing constructs. Second, it identifies AI anthropomorphism and AI explainability as characteristic-specific antecedents and specifies the mediating mechanisms (employability, perceived leader AI trust) and moderating mechanism (AI application pattern) through which they operate, offering a more fine-grained causal explanation than prior generic-technology perspectives. Third, it uncovers the mechanism by which AI work identity threat produces a double-edged sword effect on employee innovative behavior through competing mediating pathways (work withdrawal vs. job crafting) moderated by trait regulatory focus, extending identity threat theory into a previously unexplored, performance-relevant outcome domain. Practically, the framework offers organizations concrete points of intervention: calibrating AI anthropomorphism and explainability in interface and communication design, deliberately configuring automation versus augmentation modes according to desired identity outcomes, and designing regulatory-focus-informed interventions that guide identity-threatened employees toward job crafting rather than withdrawal. In sum, this research deepens theoretical understanding of how AI reshapes the meaning and boundaries of individual work identity, while offering a systematic, mechanism-based approach to managing AI work identity threat in the contemporary workplace. References | Related Articles | Metrics

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Fostering intergenerational human capital development: A reverse mentoring perspective ZHU Yue, JIN Yanghua, SI Wei 2026, 34 (12):  2152-2167.  doi: 10.3724/SP.J.1042.2026.2152 Abstract ( 60 )   PDF (678KB) ( 45 )   Peer Review Comments As organizations face the dual challenges of workforce aging and rapid digital transformation, facilitating effective knowledge exchange and mutual development across generations has become increasingly important. Reverse mentoring, in which younger employees mentor older colleagues, has emerged as a promising approach for integrating complementary forms of human capital. While existing research has highlighted its potential benefits, current understanding remains fragmented. Prior studies have largely focused on role reversal itself, providing limited insight into how reverse mentoring relationships are initiated, how they generate mutual developmental outcomes, and how their benefits extend beyond individual participants to the broader organization. To address these gaps, this research develops a multilevel theoretical framework of reverse mentoring grounded in relational mentoring theory. Specifically, we conceptualize reverse mentoring as a dynamic process that unfolds across three interconnected stages: relationship initiation, relationship development, and benefit diffusion. Through this framework, we seek to explain how intergenerational employees overcome initial resistance, establish high-quality mentoring relationships, achieve mutual growth, and ultimately contribute to organizational-level development. Study 1 examines the initiation of reverse mentoring relationships. Existing mentoring schemas are typically rooted in age-based assumptions that position older employees as mentors and younger employees as learners. Reverse mentoring challenges these assumptions by introducing a role reversal between generations. Drawing on relational mentoring theory and age-related stereotypes research, we propose that this role reversal creates distinct psychological threats for both parties. Older employees may experience fear of status loss when learning from younger colleagues, whereas younger employees may experience fear of losing face when guiding more senior coworkers. These emotional reactions reduce willingness to participate in reverse mentoring relationships. We further identify age-inclusive human resource management as a critical organizational condition that weakens the translation of age stereotypes into these defensive emotional responses. Study 2 investigates how reverse mentoring relationships evolve into high-quality developmental relationships and generate mutual growth. Existing mentoring research often assumes that developmental benefits flow primarily from mentors to mentees. In contrast, relational mentoring theory suggests that high-quality mentoring relationships emerge when both parties’ needs are fulfilled. Integrating relational mentoring theory with socioemotional selectivity theory, we identify distinct developmental needs among younger mentors and older mentees. Younger mentors seek growth-related outcomes, including leadership development, professional identity formation, and competence validation. Older mentees, in contrast, seek socioemotional fulfillment, including social connectedness, respect, and recognition. We propose that older employees’ active receptivity satisfies younger mentors’ growth needs, whereas younger employees’ collaborative mentoring behaviors satisfy older mentees’ socioemotional needs. These complementary processes foster high-quality mentoring relationships characterized by mutual trust, shared influence, and communal norms. Furthermore, we argue that high-quality relationships generate reciprocal feedback loops that promote bilateral skill development and reshape participants’ intergenerational mentoring schemas. Study 3 extends the analysis from the individual level to the organizational level. Although reverse mentoring is often implemented as an organizational practice, existing research has rarely examined how its effects diffuse beyond individual mentoring dyads. Drawing upon multilevel emergence theory, we propose a “cognitive diffusion-behavioral diffusion-structural diffusion” mechanism. Specifically, positive intergenerational mentoring schemas developed through reverse mentoring are expected to encourage employees to engage in broader intergenerational networking behaviors. As increasing numbers of employees establish such cross-generational ties, organizational network structures gradually become more interconnected across age groups. This structural transformation facilitates knowledge exchange, reduces generational silos, and enhances organizational learning. We further propose that organizational age diversity and innovation demands strengthen the translation of positive intergenerational schemas into networking behavior. Overall, this research makes three primary contributions. First, it extends relational mentoring theory into the context of intergenerational role reversal, demonstrating how mentoring processes operate when traditional age-based assumptions are challenged. Second, it develops a process-based explanation of reverse mentoring by integrating relationship initiation, relationship development, and organizational diffusion into a unified theoretical framework. Third, it shifts the focus of mentoring research from unilateral knowledge transfer toward mutual development and collective human capital growth. By revealing how reverse mentoring emerges, functions, and diffuses within organizations, this research provides a novel theoretical foundation for understanding intergenerational collaboration and offers practical insights for managing age-diverse workforces in the digital era. References | Related Articles | Metrics

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Leisure-work synergizing: A typology and an integrative model of its antecedents and consequences LU Hailing, LUO Yang, TAN Ling 2026, 34 (12):  2168-2185.  doi: 10.3724/SP.J.1042.2026.2168 Abstract ( 75 )   PDF (767KB) ( 106 )   Peer Review Comments Leisure-work synergizing refers to employees’ deliberate incorporation of work-related elements into leisure activities to develop work-relevant skills and capabilities. Although this emerging construct offers a new perspective on the work-leisure interface, existing research remains limited in three respects. First, leisure-work synergizing has largely been treated as a unidimensional construct, leaving its potentially distinct forms underexplored. Second, prior research has focused mainly on positive psychological outcomes, providing an incomplete account of its broader behavioral and performance consequences. Third, little is known about the conditions and social practices that encourage employees to engage in leisure-work synergizing. Drawing on the antecedent-behavior-consequence framework, this research develops an integrative model and examines it across four interrelated studies to investigate the types, consequences, and antecedents of leisure-work synergizing. Study 1 develops a boundary-based typology of leisure-work synergizing. Rather than classifying employees according to their stable boundary preferences, the study treats specific leisure-work synergizing activities as the units of analysis and distinguishes between boundary-segmented and boundary-integrated forms. Boundary-segmented leisure-work synergizing occurs when employees deliberately separate work-related developmental activities from ordinary leisure activities in terms of time, space, or psychological focus. Examples include setting aside a fixed period to read professional books or attend career-related events. Boundary-integrated leisure-work synergizing occurs when work-related learning, ideas, or interactions are fluidly embedded in ongoing leisure activities, such as capturing a work-related insight while browsing social media or traveling. This distinction moves beyond the prevailing unidimensional conceptualization and provides a theoretical basis for explaining why different forms of leisure-work synergizing may generate different outcomes. The study will combine qualitative interviews, expert assessments, and survey-based validation to develop a multidimensional measurement instrument and establish its nomological validity. Study 2 examines how the two forms of leisure-work synergizing differentially influence employee innovation. Drawing on cognitive load theory, the study proposes that boundary-segmented leisure-work synergizing reduces interruptions and supports sustained and systematic information processing. It therefore facilitates learning depth, which, in turn, promotes exploitative innovation by helping employees refine, improve, and apply existing knowledge. In contrast, boundary-integrated leisure-work synergizing increases employees’ exposure to heterogeneous, cross-domain, and unplanned information. It therefore facilitates learning breadth, which supports exploratory innovation by enabling employees to connect diverse elements of knowledge and identify novel possibilities. Cognitive style is introduced as an important boundary condition. Field-independent employees are expected to benefit more from the structured and focused environment created by boundary-segmented synergizing, whereas field-dependent employees are more likely to benefit from the diverse contextual cues embedded in boundary-integrated synergizing. By linking different boundary forms to distinct learning processes and innovation outcomes, this study moves beyond the assumption that all leisure-work synergizing activities operate through the same mechanism. Study 3 theorizes the daily double-edged effects of leisure-work synergizing on next-day job performance. Integrating conservation of resources theory with recovery research, the study identifies two competing pathways. On the positive side, voluntary developmental activities can strengthen self-assurance by providing employees with a sense of progress, capability, and control over their future development. Enhanced self-assurance should increase employees’ confidence, initiative, and persistence at work, thereby improving their next-day performance. On the negative side, leisure-work synergizing may occupy time and attention that could otherwise support psychological detachment, relaxation, family interaction, and emotional recovery. It may therefore contribute to inadequate recovery and reduce employees’ energy and psychological availability on the following workday. Family support is proposed to strengthen the self-assurance pathway while buffering the pathway through inadequate recovery. The study further predicts that boundary-segmented synergizing will produce stronger self-assurance and less recovery impairment than boundary-integrated synergizing because clearer activity boundaries provide more stable cues of progress and make it easier for employees to disengage after the activity ends. Study 4 investigates the antecedents of leisure-work synergizing by focusing on the practices of interest-based communities. Drawing on self-determination theory, the study proposes that such communities promote leisure-work synergizing by satisfying employees’ needs for autonomy, competence, and relatedness. Autonomy is enhanced when employees can freely choose whether, when, and how to participate. Competence develops through opportunities for practice, knowledge exchange, problem-solving, and informational feedback. Relatedness emerges through sustained interactions with others who share similar interests and developmental goals. The satisfaction of these psychological needs increases employees’ willingness and confidence in their ability to integrate work-related knowledge, skills, and social resources into their leisure activities. Community network heterogeneity is further proposed as a key boundary condition that exerts an inverted U-shaped moderating effect. Moderate heterogeneity introduces diverse knowledge, experiences, and social connections while preserving mutual understanding and a shared interest base. By contrast, very low heterogeneity may generate redundant interactions, whereas very high heterogeneity may increase communication costs and weaken group identification. Overall, this research makes three primary contributions. First, it reconceptualizes leisure-work synergizing as a multidimensional activity rather than a uniform behavioral tendency. Second, it develops a differentiated consequence model that connects distinct boundary forms to innovation and daily performance through learning, motivation, and recovery processes. Third, it identifies the practices of interest-based communities and psychological need satisfaction as important antecedent mechanisms, thereby extending research on leisure-work synergizing from its consequences to its formation. By integrating qualitative and quantitative methods, the proposed research provides a systematic foundation for future empirical inquiry and offers practical guidance on supporting employees’ career development, innovation, performance, and well-being. References | Related Articles | Metrics

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Innovation tensions in digital innovation teams: Conceptualization and multilevel effects ZHANG Maolong, LIN Yanmei, LI Jin, Wang Li 2026, 34 (12):  2186-2201.  doi: 10.3724/SP.J.1042.2026.2186 Abstract ( 56 )   PDF (687KB) ( 25 )   Peer Review Comments As the basic units for conducting digital innovation activities within organizations, digital innovation teams face persistent contradictory relationships among interdependent socio-technical elements, giving rise to complex and salient innovation tensions. How these innovation tensions can be transformed into drivers of, rather than barriers to, innovation remains an urgent question. Although existing literature has examined both the commonalities and specificities of innovation tensions in digital contexts, systematic understanding of their manifestations and mechanisms in digital innovation teams is still lacking. To address this gap, this study draws on paradox theory as an overarching lens to investigate the conceptualization and multilevel effects of innovation tensions in digital innovation teams. First, grounded in socio-technical systems theory, the study defines innovation tensions in digital innovation teams as “persistent contradictory relationships among interdependent socio-technical elements during digital innovation activities” and preliminarily identify three core dimensions: social-focused tensions, technology-focused tensions, and hybrid-focused tensions. On this basis, the study outlines a rigorous scale development procedure for developing a corresponding measurement instrument, providing a measurement foundation for empirical research on innovation tensions in digital innovation teams. Second, building on the stage-specific characteristics of the innovation process, the study constructs a multilevel mechanism model of innovation tensions at both the individual and team levels. At the individual level, drawing on activation theory, the study proposes a “too much of a good thing” effect of innovation tensions on individual innovative idea generation. Specifically, innovation tensions in digital innovation teams are proposed to exhibit an inverted U-shaped relationship with individual agile responsiveness; that is, a moderate level of innovation tension is most conducive to stimulating individual agile responsiveness, whereas both excessively low and excessively high levels of tension weaken agile responsiveness, thereby affecting individual innovative idea generation. Paradox mindset and core self-evaluation moderate this inverted U-shaped relationship between innovation tensions and individual agile responsiveness. Specifically, individuals with a strong paradox mindset can maintain agile responsiveness under higher levels of innovation tension, while individuals with high core self-evaluation show a smaller decline in agile responsiveness under high tension. Furthermore, both moderators also shape the indirect inverted U-shaped relationship through which innovation tensions influence individual innovative idea generation via individual agility. At the team level, adopting a team process perspective, the study proposes a “double-edged sword” effect of innovation tensions on the generation and implementation of team innovative ideas. On the one hand, innovation tensions facilitate team conventional routine replication, which in turn benefits the implementation of team innovative ideas but hinders the generation of team innovative ideas. On the other hand, innovation tensions also promote team flexible routine replication, which in turn benefits the generation of team innovative ideas but hinders the implementation of team innovative ideas. Digital platform ecosystem embeddedness and team shared vision can optimize the overall impact of innovation tensions on the team innovation process by amplifying their positive effects while mitigating their negative effects. The expected contributions of this study are threefold. First, by focusing on digital innovation teams as basic innovation units, this study systematically elucidates the conceptualization of innovation tensions in digital contexts and lays the foundation for developing a corresponding measurement instrument. This not only advances the contextualized development of the innovation tension construct and its manifestations but also facilitates the transition of research on innovation tensions in digital innovation teams from theoretical discussion to empirical testing. Second, by exploring the multilevel and multifaceted effects of innovation tensions in digital innovation teams, this study reveals a “too much of a good thing” effect at the individual level and a “double-edged sword” effect at the team level, thereby deepening the overall understanding of the micro-mechanisms linking innovation tensions and innovation outcomes in digital contexts and providing a more integrative theoretical framework for understanding and guiding the effective management of innovation tensions in digital innovation teams. Third, this study extends research on the antecedent mechanisms of innovation outputs in digital innovation teams, offering a new theoretical perspective for explaining the generative processes and activation pathways of team innovation outputs in digital contexts, while also providing practical implications for promoting innovation outputs in digital innovation teams. References | Related Articles | Metrics

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Citizens’ attitude bias toward the public sector from a behavioral governance perspective: Antecedents and consequence CHEN Guoliang, ZHANG Shuwei, WANG Yan 2026, 34 (12):  2202-2218.  doi: 10.3724/SP.J.1042.2026.2202 Abstract ( 66 )   PDF (723KB) ( 59 )   Peer Review Comments In the context of building a people-centered, service-oriented government, two types of “satisfaction paradox” emerge in government-citizen interactions, manifesting a divergence between citizens’ subjective perceptions and objective performance. These include the anti-public-sector bias, reflected when excellent performance receives negative evaluations, and the pro-public-sector bias, reflected when mediocre performance receives positive evaluations. Both types of paradoxes stem from citizens’ attitude bias toward the public sector (CAB-PS), which undermines the accuracy of public-sector performance assessment and hinders collaborative efforts between citizens and the public sector to improve public services. Therefore, through three studies, we systematically examine the antecedents and consequences of CAB-PS from a behavioral governance perspective. In study 1, we aim to develop a more suitable measurement instrument for CAB-PS. To address the limitation that existing measurement instruments are not well suited to contexts in which public services are provided exclusively by the public sector, we incorporate the Single Category Implicit Association Test (SC-IAT) technique to develop an indirect measurement instrument with strong reliability and validity that is specifically tailored to public-sector monopoly service provision settings. This instrument will provide a solid measurement foundation for subsequent theoretical and empirical research on CAB-PS. In study 2, we aim to systematically examine the multilevel antecedents of CAB-PS. Although existing research has categorized the factors influencing CAB-PS into three types—social, organizational, and individual—these factors have not yet been subjected to empirical testing. By developing a three-level analytical framework that incorporates social-level factors (social capital and media coverage), organizational-level factors (organizational reputation and red tape), and individual-level factors (prior beliefs and sector expectations), we employ a conjoint experiment to assess the marginal effects and relative importance of these antecedents. In Study 3, we focus on the behavioral consequences of CAB-PS and the boundary conditions of its effects. Given that the association between CAB-PS and coproduction behavior has received limited scholarly attention, we adopt a behavioral governance perspective and use vignette experiments to examine how CAB-PS influences citizens’ coproduction behavior. In addition, we investigate the moderating roles of service agencies’ responsiveness and service recipients’ trust in government in contexts where public services are provided exclusively by the public sector. We also examine the moderating roles of service agencies’ autonomy and service recipients’ sense of efficacy in contexts where public services are provided through competitive arrangements. This study makes three main theoretical contributions. First, we further clarify the conceptual connotation of CAB-PS and develop a measurement instrument with cross-context validity, thereby providing a solid measurement foundation for future empirical research on CAB-PS. Second, by drawing on Social Information Processing Theory, Signaling Theory, Motivated Reasoning Theory, and Expectancy Disconfirmation Theory, we systematically synthesize and validate perspectives on the factors influencing CAB-PS and compare the relative explanatory power of these theoretical perspectives. Finally, based on a behavioral governance perspective and Theory of Planned Behavior, we reveal the effects of CAB-PS on coproduction behavior and identify the boundary conditions of these effects, thereby enriching the theoretical literature on interventions designed to promote coproduction. This study makes three main practical contributions. First, it helps public managers design low-cost interventions to mitigate bias and advance the development of a service-oriented government based on public opinion. Second, it helps encourage citizens to contribute resources to public service delivery through both direct and indirect forms of coproduction. Third, it helps public managers recognize and address the potential “satisfaction-coproduction paradox” and promote the governance. References | Related Articles | Metrics

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The dual-edged sword impact of generative AI product usage on consumer memory efficacy and behavior WANG Xuefeng, MA Zengguang 2026, 34 (12):  2219-2238.  doi: 10.3724/SP.J.1042.2026.2219 Abstract ( 87 )   PDF (775KB) ( 84 )   Peer Review Comments The rapid proliferation of Generative Artificial Intelligence (GenAI) is fundamentally reshaping consumer landscapes. Yet its profound implications for consumer cognition and subsequent behavioral outcomes remain insufficiently explored. Prior research has predominantly focused on short-term drivers of technology adoption, such as perceived usefulness and trust, while largely neglecting the deeper cognitive consequences of generative AI use. In particular, little is known about how generative AI affects memory efficacy and metamemory monitoring. Moreover, existing studies rarely connect the distinctive technological affordances of generative AI (e.g., content generation and natural-language interaction) to their downstream behavioral consequences, resulting in a fragmented understanding of how technology use shapes consumer behavior through cognitive pathways. To address these gaps, this research develops an interdisciplinary “technology-cognition-behavior” framework to systematically examine the dynamic, dual-edged effects of generative AI product usage on consumer memory efficacy and behavior. We propose that the impact of generative AI usage is temporally contingent. In the short term, it enhances memory efficacy and judgment confidence through cognitive offloading, yielding positive or neutral behavioral outcomes; over time, however, prolonged reliance may undermine individuals’ memory processing capabilities and impair metamemory calibration, producing negative behavioral consequences. Building on this premise, the research addresses four research questions: (1) whether and how generative AI usage affects consumer memory efficacy, and how such effects evolve over time; (2) how short-term improvements in memory efficacy translate into positive or neutral consumer behaviors; (3) how long-term impairments in memory efficacy generate negative behavioral outcomes; and (4) which marketing interventions can effectively mitigate these adverse effects. To empirically investigate these questions, the research is organized into four sequential studies. Study 1 adopts a longitudinal and exploratory approach, combining machine learning-based text analysis with survey and interview data to uncover the non-linear relationship between generative AI usage and memory efficacy. We anticipate an inverted U-shaped trajectory, moderated by factors such as cognitive autonomy and usage patterns. Building on these findings, Studies 2 and 3 examine the downstream effects of memory efficacy changes. Study 2 focuses on the positive and neutral behavioral consequences arising from short-term cognitive gains, whereas Study 3 investigates the negative outcomes associated with long-term cognitive inhibition. Finally, Study 4 identifies and experimentally evaluates marketing interventions —specifically, “assistant role positioning” and “cyclical temporal interfaces”—designed to mitigate the negative effects identified in Study 3. This work offers three primary theoretical contributions. First, by integrating insights from information systems (IS), cognitive psychology, and marketing, it proposes a cross-layer framework that elucidates how generative AI alters memory processing, judgment confidence, and choice. By synthesizing cognitive offloading theory and metamemory monitoring theory, it introduces memory efficacy as a critical cognitive construct within consumer decision-making, thereby extending information processing and cognitive decision theories in technology-mediated contexts. Second, it advances the literature on the double-edged sword effect by incorporating a temporal dimension, revealing the dynamic tension between immediate benefits (e.g., increased decision confidence) and cumulative costs (e.g., diminished future self-continuity) of generative AI usage. Moving beyond the fragmented treatment of positive and negative effects in prior studies, the framework provides a more coherent account of the interplay between technological dependence and cognitive autonomy. Third, the study extends metamemory theory into real-world consumption settings through field experiments and longitudinal tracking, offering new insights into how consumers form judgment confidence, calibrate memory accuracy, and make behavioral choices in technology-augmented environments. From a practical standpoint, the findings will inform the design and development of generative AI products by helping firms balance efficiency gains with potential cognitive risks. The research proposes actionable intervention strategies, such as framing AI systems as collaborative assistants and implementing “cyclical temporal interfaces,” to reduce long-term cognitive dependency and its associated negative consequences. Additionally, these insights carry implications for consumer cognitive protection and policy-making, contributing to the responsible development and deployment of generative AI technologies and, ultimately, promoting long-term societal welfare. 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The impact of residential mobility on parents’ intergenerational consumption decisions for children: A life-course perspective HE Qiong, LIU Wumei, GU Feng 2026, 34 (12):  2239-2256.  doi: 10.3724/SP.J.1042.2026.2239 Abstract ( 66 )   PDF (686KB) ( 51 )   Peer Review Comments Residential mobility has become an increasingly common feature of modern society, shaping individuals’ cognition, emotions, social relationships, and consumer behavior. While previous research has examined its effects on individuals’ own consumption decisions, little attention has been paid to how parents’ residential mobility influences the consumption decisions they make for their children. This gap is noteworthy because parents frequently act as decision-makers for their children across a wide range of consumption contexts. Moreover, their decision priorities may vary substantially as children progress through different developmental stages. Consequently, understanding the intergenerational impact of residential mobility requires a life-course perspective that takes children’s developmental characteristics and stage-specific needs into consideration. Drawing upon the literature on residential mobility, family consumption decision-making, and child development, the present research proposes a novel theoretical framework to explain how parents’ residential mobility affects the consumption decisions and preferences parents make for their children across different stages of the child life course. Specifically, we focus on three representative decision domains corresponding to three key developmental stages: food decisions during early childhood, educational decisions during middle childhood, and entertainment decisions during adolescence. For food decisions during early childhood, we propose that residential mobility shapes parents’ attention to nutritional information when making food choices for their children. Specifically, parents with higher residential mobility become more concerned about their children’s safety and adopt a prevention-focused orientation, leading them to pay greater attention to negative nutritional attributes rather than positive nutritional attributes. For educational decisions during middle childhood, we argue that residential mobility influences parents’ preferences for educational products. Because parents with higher residential mobility place greater emphasis on their children’s emotional experiences and emotional support, they are more likely to prefer affective educational products over cognitive educational products. For entertainment decisions during adolescence, we propose that residential mobility affects parents’ preferences for entertainment products. Specifically, parents with higher residential mobility perceive their children’s future environment as more competitive and become more motivated to expand their children’s social networks, leading them to favor cooperative rather than independently completed entertainment products. Beyond identifying these effects, the proposed framework also specifies the psychological mechanisms and boundary conditions underlying each decision domain. By examining how residential mobility shapes parental concerns, motivations, and developmental priorities across different stages of childhood, the study provides a more comprehensive understanding of the process through which residential mobility influences family consumption decisions. The framework further demonstrates that the consequences of residential mobility are not uniform but depend on the developmental context in which parental decisions are made. Overall, this research contributes to the literature in three ways. First, it extends residential mobility research from the individual level to family and intergenerational consumption decision-making contexts. Second, it advances family consumption decision-making theory by highlighting residential mobility as an important socio-ecological life experience that shapes parental judgments and preferences. Third, it integrates residential mobility theory with a child life-course perspective, providing a dynamic framework for understanding stage-specific variations in parental decision-making. The proposed framework also offers important practical implications. First, it provides guidance for family consumption decisions by helping parents better understand how residential mobility influences the choices they make for children across different developmental stages. Second, it offers useful insights for marketing strategies, enabling firms to better understand families with different mobility experiences and to develop more targeted products, services, and marketing approaches. Third, it provides a basis for policymaking by helping policymakers better identify family needs and social development trends when designing education-, nutrition-, and family-related policies. References | Related Articles | Metrics

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The cascading retaliation effect of algorithmic price discrimination NIU Xiaoxiao, REN Qianyu, QIAO Shuchen 2026, 34 (12):  2257-2273.  doi: 10.3724/SP.J.1042.2026.2257 Abstract ( 66 )   PDF (633KB) ( 39 )   Peer Review Comments As e-commerce platforms increasingly deploy algorithms to implement price discrimination, consumers who detect such practices often retaliate against the offending platform, yet whether and how this retaliation propagates beyond a single platform remains poorly understood. Prior research has documented diverse forms of algorithmic price discrimination and the algorithmic biases consumers hold toward them, such as algorithm aversion and algorithmic transference, but has rarely asked whether and how these distinct forms of discrimination give rise to a cascading retaliation effect that spreads across platforms and contexts, or under what individual and situational conditions this spillover intensifies. Equally unclear is how classic cognitive biases shape the strength of this contagion. To address the theoretical gap, this study investigates these chain-reaction behavioral patterns and establishes a comprehensive analytical framework through two interconnected studies. Study 1 aims to develop a multidimensional classification system for algorithmic price discrimination intensity, categorizing it into four levels based on the degree of discrimination: dynamic pricing, consumer segment-based pricing, personalized pricing, and personalized dynamic pricing. The study proposes and confirms that higher algorithmic price discrimination intensity systematically undermines three dimensions of consumer-perceived fairness: distributive fairness (triggered by the magnitude of price disparities), procedural fairness (stemming from opaque pricing mechanisms), and interactional fairness (triggered by the algorithm's lack of emotion and human-centric considerations). These three dimensions collectively mediate the transmission path from algorithmic price discrimination to retaliatory chain reactions. Furthermore, the study identifies two key boundary conditions. Price sensitivity amplifies the sense of distributive unfairness, thereby intensifying the retaliatory chain reaction. Conversely, algorithmic explainability acts as a psychological buffer; by enabling consumers to understand algorithmic rules and recognize differences in algorithmic logic, it disrupts the perception of algorithmic homogeneity, effectively confining consumer dissatisfaction to the current platform. Drawing on the perspective of bounded rationality, Study 2 aims to reveal the cognitive mechanisms driving this cross-platform retaliation contagion, identifying the interplay between fairness perceptions and two cognitive biases: loss aversion and the availability heuristic. First, loss aversion, the phenomenon where the psychological pain of a loss outweighs the pleasure of an equivalent gain, systematically amplifies the sense of distributive unfairness. Consumers with higher levels of loss aversion perceive greater unfairness and exhibit more intense retaliatory chain reactions. Second, the availability heuristic influences the cognitive salience of the event. Variations, such as the timing of algorithmic price discrimination incidents and the intensity of the negative emotions elicited, lead to differences in how easily consumers recall these events, which in turn influence their judgments regarding the likelihood of platform discrimination. The discrimination-related memory is activated by perceptions of unfairness, subsequently determining the intensity of the resulting spillover effects. This study makes four major theoretical contributions. First, it constructs an algorithmic price discrimination classification system from the consumer’s perspective, extending classical price discrimination theory to the context of algorithm-driven markets. Second, it examines the three-dimensional theory of fairness within the context of AI interfaces, offering new insights into the understanding of algorithmic bias. Third, it shifts the paradigm of consumer retaliation from isolated "stimulus-response" frictions to systemic, cross-platform, and cross-contextual boycotts, thereby providing a novel explanation for the spread of distrust within digital ecosystems. Fourth, it expands the scope of the "algorithmic spillover effect" from technical malfunctions to the realm of pricing, revealing how bounded rationality can trigger widespread algorithmic distrust. By mapping the complete behavioral trajectory, ranging from types of discrimination and perceptions of three-dimensional fairness to cognitive biases and chain reactions of retaliation, this study redefines algorithmic pricing discrimination as a systemic risk at the ecosystem level rather than a localized issue confined to a single firm, thereby providing a crucial behavioral basis for platform governance in the AI ​​era. References | Related Articles | Metrics

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The association between socioeconomic status and belief in a just world: A three-level meta-analysis WU Jing-Ying, JIA Ting-Rui, WANG Ya-Meng, LV Sa-Sa, WANG Zuo-Jun, WANG Li-Jun, CHAN Kai Qin 2026, 34 (12):  2274-2294.  doi: 10.3724/SP.J.1042.2026.2274 Abstract ( 75 )   PDF (1269KB) ( 94 )   Peer Review Comments The relationship between socioeconomic status (SES) and belief in a just world (BJW) has long been contested, with two competing theoretical frameworks offering opposing predictions. The just-world hypothesis suggests that disadvantaged groups develop stronger BJW as a palliative coping mechanism, whereas the just-capital perspective posits that privileged positions afford greater exposure to fair and predictable outcomes, thereby fostering stronger BJW. Existing empirical evidence remains fragmented and contradictory, leaving the direction, magnitude, and boundary conditions of this association unresolved. The present study addresses these controversies through a comprehensive three-level meta-analysis that simultaneously tests these competing hypotheses and systematically maps the moderating architecture underlying the SES-BJW link. We synthesized data from 58 independent studies comprising 106,585 participants and 113 effect sizes—the largest and most diverse evidence base on this topic to date. Crucially, we adopted a three-level meta-analytic model that explicitly accounts for non-independence among multiple effect sizes nested within the same study, a statistical refinement that yields more accurate standard errors and reduces false-positive rates compared to conventional meta-analytic approaches that either ignore dependency or adopt overly conservative corrections. Our primary finding reveals a small but statistically significant positive association between SES and BJW (r = 0.12, p < 0.001), unambiguously favoring the just-capital framework over the compensatory just-world hypothesis across the full dataset. This overall effect, however, obscures substantial heterogeneity that is systematically explained by three classes of moderators operating at macro-structural, measurement, and conceptual levels—a tripartite moderation framework that constitutes the principal theoretical contribution of this work. At the macro-structural level, we identified national income inequality, operationalized by the Gini coefficient, as a powerful contextual moderator. The positive SES-BJW association progressively weakened as inequality intensified and, notably, reversed in direction under highly unequal conditions (e.g., Gini > 0.45). This crossover pattern demonstrates that macroeconomic environments do not merely attenuate or amplify the relationship but fundamentally alter its valence—a finding that reconciles prior contradictory reports by showing that both theoretical predictions are conditionally valid depending on the broader distributive context. In egalitarian societies, the just-capital logic prevails, whereas in severely stratified contexts, the compensatory mechanism hypothesized by just-world theory surfaces among lower-SES groups. To our knowledge, this is the first meta-analytic evidence documenting a regime-switching effect of inequality on the SES-BJW association. At the measurement level, we found that the association was significantly stronger for subjective SES (assessed via perceived social standing) than for objective SES (indexed by income, education, or occupation). This divergence implies that individuals' phenomenological experience of their social position exerts greater psychological weight than objective resource possession in shaping justice beliefs, suggesting that interventions targeting status perception may be more efficacious than those attempting to alter structural indicators in the short term. At the conceptual level, SES demonstrated a markedly stronger relationship with personal BJW (beliefs about justice toward oneself) than with general BJW (beliefs about justice in the broader world). This dimension-specific effect indicates that justice beliefs are not monolithic but are differentially anchored to self-relevant versus societal-level experiences, with personal BJW being more directly calibrated to one's own socioeconomic trajectory and thus more sensitive to SES variations. Collectively, these moderating patterns advance a contextualized understanding of BJW that transcends the static dispositional view prevalent in earlier research. BJW emerges not as a stable personality trait but as a dynamically constructed belief system shaped by the interplay of structural opportunities, perceived social standing, and the referential scope of justice judgments. Importantly, the three-level meta-analytic framework we employed enables simultaneous estimation of within-study, between-study, and sampling variance components, providing more precise moderator tests and more reliable heterogeneity quantification than two-level alternatives. Theoretically, our findings integrate competing accounts by specifying their respective domains of applicability, thereby moving beyond the either/or debate toward a conditional model of justice belief formation. Practically, the results carry clear policy implications: reducing macroeconomic inequality through institutional mechanisms—such as expanding legal aid accessibility, transparentizing educational and employment selection procedures, and ensuring procedural fairness in public service delivery—can narrow the "just-capital gap" between SES strata by enabling lower-SES individuals to accumulate positive experiences of institutional responsiveness. Furthermore, when structural change is not immediately feasible, framing public messaging to enhance perceived personal justice (rather than abstract general justice) may more effectively sustain BJW among disadvantaged populations, thereby preserving the adaptive functions of BJW for psychological well-being and social cohesion. We acknowledge certain limitations, including the predominance of cross-sectional designs precluding causal inferences, the geographic concentration of samples in Western and East Asian contexts, and the absence of longitudinal data to examine temporal dynamics. Future research should prioritize experimental and panel studies to establish causality and track within-individual changes in BJW as a function of shifting socioeconomic conditions. Despite these constraints, this meta-analysis provides the most comprehensive empirical synthesis to date, establishing SES as a robust albeit conditional predictor of BJW and charting a nuanced agenda for theory development and policy design in justice motivation research. References | Related Articles | Metrics

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The cognitive and neural mechanisms of vocabulary fast mapping WANG Yixuan, YANG Jianfeng 2026, 34 (12):  2295-2308.  doi: 10.3724/SP.J.1042.2026.2295 Abstract ( 72 )   PDF (499KB) ( 52 )   Peer Review Comments Fast mapping (FM) learning refers to a learning strategy through which learners quickly establish a connection between a novel word and its referent by excluding known words. The concept of “fast mapping” originates from children’s learning of native language vocabulary, in which 3- to 4-year-old children are able to establish the association between a new word (chromium) and an object (olive green) by excluding a known word (red), thus realizing the rapid acquisition of the meaning of a new word. Research on FM learning suggests that new words can rapidly form neural representations in the brain, challenging traditional learning theories and attracting considerable research interest. The current paper reviews recent advances in FM learning from three aspects. First, FM learning demonstrates marked advantages over explicit encoding learning. From infancy, children can rapidly acquire new words through this mechanism; and in adults, FM learning similarly supports the rapid formation and long-term maintenance of word-object associations, with memory exhibiting slower decay and greater stability over time. Second, the underlying cognitive mechanisms of FM learning are examined in detail with reference to its key moderating factors. Specifically, from the perspective of exclusion-based reasoning during the learning process, it is demonstrated that disambiguation contributes to vocabulary acquisition, and that the mechanisms of active decision-making and response selection further facilitate the learning outcomes of FM learning. From the perspective of learners’ prior knowledge, the intrinsic connection between new and existing knowledge is shown to promote the rapid formation of new word memory during FM learning. Finally, a direct neural comparison between FM learning and explicit encoding learning reveals that FM learning relies on a cortical network anchored in the anterior temporal lobe (ATL), with little or even no hippocampal involvement—a pattern that is fundamentally distinct from the hippocampal-prefrontal system that underlies explicit encoding learning. Since it was first proposed, FM learning has been met with questioning and challenges from researchers. The controversies surrounding FM learning can be broadly summarized into three major aspects. The first concerns whether FM learning is unique. At both the behavioral and neural levels, there has been conflicting evidence regarding the learning efficacy and “independence from the hippocampus” of FM learning. However, the uniqueness debate does not negate the encoding mode of FM learning itself, but rather lies in whether the engagement of the “cortical shortcut” may be constrained by the functional state of the hippocampus. The second concerns whether FM learning differs across different types of vocabulary learning. Vocabulary learning can be divided into two aspects: lexical configuration and lexical engagement. FM learning and explicit encoding learning have different applicability to these two aspects, which may be the source of the inconsistencies in previous findings. The third concerns whether FM learning challenges traditional learning theories. The rapid cortical integration mechanism revealed by FM learning poses a challenge to the systems consolidation view advocated by complementary learning systems and standard consolidation theory, whereas memory systems reorganization theory provides a more explanatory framework for these findings. Based on this, future research on FM learning should be further advanced in the following aspects. At the cognitive-behavioral level, the underlying mechanisms by which FM learning facilitates cortical memory need to be thoroughly examined; at the neurophysiological level, techniques from cognitive neuroscience should be integrated to uncover the neural mechanisms of FM learning—for example, distinguishing different cognitive processes during FM learning and revealing dynamic interactions between brain regions through neural oscillatory dynamics, and establishing the causal roles of key brain regions in FM learning using neuromodulation techniques such as tDCS and TMS. Critically, the debates on FM learning have collectively driven learning theory from a linear consolidation view that asks “when information transfers from the hippocampus to the cortex” toward a dynamic reorganization view that asks “under what conditions the cortex can directly participate in the construction of novel word representations.” Future research needs to further clarify the cognitive and neural mechanisms of FM learning within this dynamic perspective, thereby providing empirical foundations for the development of learning theory. References | Related Articles | Metrics

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From fetus to newborn: A continuity perspective on the development of speech perception WANG Menghuan, XU Qiuyang, LIANG Dandan 2026, 34 (12):  2309-2320.  doi: 10.3724/SP.J.1042.2026.2309 Abstract ( 72 )   PDF (647KB) ( 41 )   Peer Review Comments Newborns show remarkable sensitivity to speech within the first hours and days of life. They can distinguish their native language from unfamiliar languages, prefer their mother's voice, respond to prosodic and emotional features of speech, and show early sensitivity to vowel and consonant contrasts. These findings have often been interpreted as evidence for broad biological preparedness or as the beginning of rapid postnatal language learning. This review adopts a prenatal-postnatal continuity perspective and asks how prenatal auditory experience, especially low-frequency prosodic experience in the womb, may contribute to the emergence of newborn speech perception. The review first considers the physiological and acoustic conditions under which prenatal speech experience occurs. The intrauterine environment functions as a natural low-pass filter. High-frequency components of speech, which are crucial for fine-grained consonantal distinctions, are strongly attenuated by maternal tissues, the uterine wall, and amniotic fluid. By contrast, low-frequency information such as rhythm, stress, intonation, pitch contour, and the temporal envelope of speech is relatively well preserved. Thus, the fetus does not encounter speech as a full-spectrum acoustic signal. Prenatal speech input is mainly composed of prosodic and rhythmic information, with only limited access to segmental cues, especially low-frequency vowel-related information. This acoustic constraint defines what can reasonably be learned before birth and also helps explain why prosody occupies a privileged position in fetal and neonatal speech perception. The review then synthesizes behavioral, physiological, and neuroimaging evidence suggesting that prenatal auditory learning is not limited to general sound exposure. Studies using fetal heart rate, movement responses, fetal magnetoencephalography, electroencephalography, and functional near-infrared spectroscopy indicate that late-gestation fetuses and newborns can respond to frequency changes, rhythm, familiar voices, and repeatedly presented speech materials. After birth, newborns show preferences for the maternal voice, the native language, and speech passages heard prenatally. These findings suggest that prenatal auditory experience can generate memory traces that carry over into the neonatal period. Because prosodic information is the most stable and accessible component of speech in the womb, such learning is most plausibly organized around rhythm, stress, intonation, and other slow temporal properties of speech. On this basis, the review develops a speech-perception-specific account of prenatal-postnatal continuity. This account does not assume that fetuses fully acquire phonemes before birth. Instead, it distinguishes several possible roles of prenatal experience. First, prenatal experience may support familiarity with global prosodic patterns of the maternal language, including rhythm, stress, and intonation. Second, low-frequency vowel-related information, especially cues associated with the first formant, may provide a limited prenatal interface for later segmental processing. Third, prenatal prosodic experience may serve as a slow temporal scaffold that helps the newborn brain organize the full-spectrum speech signal after birth. Once the infant is exposed to air-conducted speech, richer acoustic information becomes available, including consonantal details, spectral contrasts, and fine phonemic cues. Under the joint influence of this new input and rapid cortical maturation, the newborn brain may quickly enter a phase of segmental learning and neural tuning. This framework also highlights the role of neural maturation. The development of speech perception cannot be reduced either to prenatal experience or to postnatal input alone. The fetal auditory system becomes functional during gestation, but cortical and fronto-temporal networks continue to mature rapidly around birth and in early infancy. Prenatal experience provides structured input within the limits of the womb, whereas birth introduces a major acoustic transition from a low-pass-filtered, prosody-dominant environment to a full-spectrum speech environment. The newborn brain must therefore recalibrate its auditory and speech-processing system. In this sense, birth is better understood not as an absolute starting point, but as a transition event that reorganizes the relationship among available input, neural readiness, and learning demands. By integrating findings on intrauterine acoustics, fetal auditory maturation, prenatal prosodic learning, and newborn segmental development, this review attempts to clarify how prenatal and postnatal factors may jointly shape early speech perception. Compared with discussions that focus mainly on postnatal neonatal or infant speech perception, this perspective extends the developmental window backward into fetal life. Compared with broader accounts of prenatal experience or perinatal development, it focuses specifically on speech perception and on the transition from prenatal prosodic predominance to postnatal segmental development. Future research should examine this continuity account using longitudinal and multimodal designs. Combining fetal and neonatal measures such as fetal magnetoencephalography, electroencephalography, functional near-infrared spectroscopy, ultrasound-based behavioral indices, and postnatal behavioral paradigms may help distinguish the respective contributions of prenatal exposure, birth-related recalibration, and postnatal speech input. Studies of preterm infants, tone-language environments, bilingual or multilingual exposure, and high-risk populations will be especially informative. Such work may also help identify early neural markers of atypical speech perception and inform early screening and intervention for developmental language disorders. References | Related Articles | Metrics

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Multisensory temporal integration in autism: characteristics, mechanisms, and intervention prospects CHEN Yan, LI Jing 2026, 34 (12):  2321-2343.  doi: 10.3724/SP.J.1042.2026.2321 Abstract ( 81 )   PDF (697KB) ( 71 )   Peer Review Comments The core scientific question addressed in this paper is: Through what mechanism do deficits in multisensory temporal integration in individuals with autism spectrum disorder (ASD) link low-level perceptual anomalies to high-level social dysfunction? To address this question, we propose a neurophysiological-cognitive-behavioral cascade model that delineates the causal pathway from biological disturbances to temporal processing deficits and, ultimately, to social interaction impairments. Based on this model, we derive mechanism-guided intervention principles. The main innovative contributions are as follows: 1. Establishing multisensory temporal integration as a mechanistic hub. Rather than merely cataloguing perceptual and social findings separately, this paper explicitly identifies dysfunction in multisensory temporal integration—specifically, a widened temporal binding window (TBW), impaired dynamic temporal coordination (unstable simultaneity judgments, increased sensorimotor asynchrony and variability), and disrupted integration of dynamic social cues (audiovisual speech, bodily synchrony)—as the critical mechanistic hub linking basic perception to complex social behavior. 2. Proposing a neurophysiological-cognitive-behavioral cascade model. Individuals with ASD exhibit atypical cross-sensory response patterns early in life (hyporesponsiveness, hyperresponsiveness, sensory seeking), which constitute the initial starting point of the cascade and directly affect the precision and stability of their perception of the world. At the neurophysiological level, successful temporal alignment requires precise neural “alignment” of cross-modal signals. ASD is characterized by: (a) autonomic nervous system dysregulation (reduced heart rate variability, atypical electrodermal responses), which compromises the physiological baseline for processing dynamic temporal information; (b) neurotransmitter imbalances (dopamine, melatonin, oxytocin), which affect the perception and judgment of temporal rhythms; and (c) neural activation and connectivity deficits—reduced activation in the temporoparietal junction, inferior frontal gyrus, and superior temporal sulcus, coupled with diminished interregional functional connectivity, particularly reduced gamma-band (30-80 Hz) oscillatory synchrony critical for temporal binding. Together, these abnormalities constitute the biological foundation of temporal integration deficits. At the cognitive level, neurophysiological anomalies are further mediated and amplified by cognitive processing. First, predictive coding impairments—characterized by “weak priors” and maladaptive precision weighting of prediction errors—prevent individuals with ASD from generating and updating precise internal models to predict upcoming sensory input. Second, atypical attention patterns (reduced social orienting, inappropriate attentional allocation) make it difficult for them to selectively bind temporally relevant information within a multisensory stream. These two mechanisms interact bidirectionally and, over the course of development, further impede neurophysiological maturation. At the behavioral level, the convergence of neurophysiological and cognitive abnormalities produces three core features: (a) a widened TBW—an abnormally long temporal window for judging simultaneity, reflecting “blurriness” in the perception of temporal synchrony; (b) dynamic temporal coordination deficits—increased asynchrony and higher variability in sensorimotor synchronization tasks (e.g., finger-tapping to a beat), revealing instability in temporal perception and internal timing; and (c) impaired integration of social cues—difficulty integrating multisensory social signals (e.g., voice-lip movement, facial expression-prosody), leading to deficits in turn-taking, joint attention, and empathic responding. Importantly, these behavioral deficits are modulated by task context (e.g., complexity, social relevance) and produce distal cascading effects: they directly impair the synchronous integration of social cues, erode episodic memory and social associative learning, and ultimately constitute a significant perceptual foundation of core social communication deficits in ASD. The model also highlights a negative feedback loop, whereby behavioral and cognitive difficulties further exacerbate neurophysiological dysregulation. 3.Intervention principles grounded in core deficits. Sensory integration therapy, by providing structured rhythmic vestibular, proprioceptive, and tactile inputs, optimizes arousal levels and neural synchronization, potentially narrowing the TBW and enhancing multisensory integration efficiency. Rhythm-based interpersonal synchrony interventions (e.g., joint drumming, coordinated movement games), by providing rhythmic cues, directly train temporal prediction and online error correction, strengthen internal timing models, and promote functional connectivity within sensorimotor and social brain networks. Theoretically, a combined approach that integrates low-level sensory regulation with high-level temporal prediction training may form a precise intervention pathway from basic perception to social coordination. This paper offers a novel problem-solving framework that reframes social deficits in ASD as arising from fundamental abnormalities in multisensory temporal integration. By specifying a neurophysiological-cognitive-behavioral cascade and deriving testable, targeted intervention principles, it provides a more precise and dynamic theoretical perspective for understanding social interaction difficulties in autism and for developing precision interventions. Future research should employ rigorous randomized controlled trials that directly measure TBW and gamma-band synchrony to establish causal evidence for the effects of these interventions on improving temporal integration and social function. References | Related Articles | Metrics

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Low-intensity pulsed ultrasound for improving cognitive and physical dysfunction in dementia: Targets, parameters, and mechanisms QIAO Fuqiang, CHE Bingying, YAN Bingzi, ZHOU Jie 2026, 34 (12):  2344-2360.  doi: 10.3724/SP.J.1042.2026.2344 Abstract ( 68 )   PDF (596KB) ( 34 )   Peer Review Comments Low-intensity pulsed ultrasound (LIPUS) has emerged as a novel non-invasive neuromodulation technique capable of transcranial penetration and precise targeting of deep brain regions, exhibiting unique advantages in the intervention of neurodegenerative dementia. This review systematically summarizes the research progress of LIPUS in improving cognitive and physical dysfunction in Alzheimer’s disease, Parkinson’s disease, vascular dementia, traumatic brain injury-related dementia, as well as Lewy body dementia and frontotemporal dementia, with a particular focus on the internal correlation among disease types, intervention targets, acoustic parameters, and therapeutic efficacy. It further elaborates on the potential neural mechanisms of LIPUS-mediated cognitive regulation from the perspective of cognitive neuropsychology, and clarifies the application value, existing deficiencies and future transformation prospects of this technology in cognitive impairment intervention. The core innovation of this review lies in establishing a standardized analytical framework of “disease-target-parameter-efficacy” for LIPUS intervention in dementia. By sorting out existing preclinical and clinical evidence, this paper systematically delineates the optimal intervention targets for different dementia subtypes: the hippocampus, prefrontal cortex and precuneus are the dominant targets for Alzheimer’s disease intervention; the striatum, subthalamic nucleus, substantia nigra and primary motor cortex serve as core regulatory regions for Parkinson’s disease-related motor and cognitive dysfunction; the medial prefrontal cortex, barrel cortex and hippocampus are effective intervention sites for vascular dementia; and the injured cerebral cortex is the key target for improving post-traumatic cognitive impairment. This review further classifies and summarizes the effective intensity range of ultrasound parameters across animal models and human subjects, clarifying that spatial peak temporal average intensity and duty cycle are the core parameters determining neuroprotective and neuromodulatory effects, while fundamental frequency and pulse repetition frequency mainly play auxiliary regulatory roles. It quantitatively refines the effective parameter intervals of different disease subtypes, and highlights the significant parameter differences between animal experiments and human clinical applications, providing an important reference for subsequent parameter optimization and clinical conversion. In terms of mechanism innovation, this review integrates multi-level pathological evidence and reveals multiple shared pathways by which LIPUS improves cognitive and physical dysfunction: upregulating the expression of brain-derived neurotrophic factor, glial cell line-derived neurotrophic factor and nerve growth factor to maintain synaptic plasticity; inhibiting neuroinflammation by regulating microglial activation and pro-inflammatory cytokine secretion; ameliorating abnormal neural oscillations and functional connectivity disorders in cognitive-related neural circuits; promoting the clearance of amyloid-β plaques and α-synuclein aggregation; activating the Fndc5/irisin signaling pathway to repair neuronal damage; and improving cerebral blood perfusion and white matter integrity to alleviate brain pathological degeneration. In addition, this review proposes that LIPUS can modulate neuronal autophagy pathways, which provides a new mechanistic explanation for the potential intervention of Lewy body dementia and frontotemporal dementia lacking direct experimental evidence. From the perspective of cognitive neuropsychology, this study complements the theoretical system of non-invasive physical intervention on cognitive function, clarifies the specificity of target-cognitive domain matching and the dependence of parameter-efficacy response, and fills the research gap of psychological perspective integration in the field of ultrasonic neuromodulation. At present, the limitations of this field include insufficient high-quality clinical trials, inconsistent parameter standards, unclear long-term efficacy and safety, and prominent interspecies transformation barriers. Future research should focus on optimizing individualized target and parameter schemes, establishing human-adapted ultrasound parameter evaluation systems, revealing in-depth molecular neural mechanisms, and conducting multi-center controlled clinical trials. Collectively, LIPUS exhibits broad application prospects in the precise intervention of cognitive and physical dysfunction in dementia, and its standardized clinical translation will provide a safe, non-invasive and innovative intervention strategy for reducing the global burden of dementia. References | Related Articles | Metrics

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Intuitive competition and deliberative regulation in prosocial decision-making WANG Xinrui, SHI Rong, LIU Chang 2026, 34 (12):  2361-2374.  doi: 10.3724/SP.J.1042.2026.2361 Abstract ( 90 )   PDF (606KB) ( 57 )   Peer Review Comments The dual-process framework has generated two opposing accounts of prosocial decision-making. The reflective model posits that prosocial behavior requires deliberative control to override selfish intuitions, whereas the intuitive model of prosociality proposes that prosocial tendencies are themselves intuitive, with deliberation potentially triggering self-interested calculation. Despite their opposing conclusions, both models share the implicit assumption of single-intuition activation followed by deliberative correction. The Social Heuristics Hypothesis (SHH) advanced this debate by reconceptualizing intuition as the internalization of socially adaptive rules, yet it struggles to explain decisional conflict and the inconsistent direction of deliberative effects. The Hybrid Model addresses these limitations by proposing that multiple intuitive responses can be activated in parallel, with behavior initially determined by the competition among intuitions based on their relative strength, and deliberation serving as a flexible conflict-regulation system. This review offers three interconnected contributions. First, we construct a hierarchical cognitive framework that integrates “Heuristic Formation - Multi-Intuition Competition - Deliberative Regulation”. Unlike previous reviews that treat SHH and the Hybrid Model as separate or competing accounts, we argue that they address distinct but complementary levels of explanation. SHH (alongside its extension, the Internalized Heuristics for Self-Preservation theory) answers where intuitions come from - the source layer. The Hybrid Model answers how those intuitions operate online during a decision - the processing layer. The integration forms a complete explanatory chain: long-term social experience shapes an individual's intuition library (source), which then enters real-time competition in specific decision contexts (processing), with deliberation flexibly intervening to resolve conflict (regulation). This layered framework reconciles seemingly contradictory findings - such as why time pressure promotes cooperation in some samples but not others, or why deliberation sometimes increases and sometimes decreases prosociality - by tracing behavioral differences back to variations in individuals' intuition library structures shaped by divergent social experiences. Second, we reconceptualize the role of deliberation. Drawing on the multifunctional account of deliberation, we characterize deliberation not as a predetermined corrector of default intuitions, but as a context-sensitive cognitive “toolkit” with four dynamically configurable functions: control (suppressing inappropriate intuitive responses), generation (constructing novel responses when intuitions are absent or conflicting), justification (providing post hoc rationales for chosen responses), and regulation (monitoring and allocating cognitive resources). Crucially, these functions are not mutually exclusive; individuals may flexibly switch between or combine them within a single decision episode depending on the intensity structure of competing intuitions. This reframing moves beyond the traditional “correction” metaphor and opens new avenues for investigating when and how deliberation contributes to prosocial behavior. Third, we translate this integrated framework into empirically testable propositions. We outline concrete experimental designs for manipulating the relative strength of prosocial versus selfish intuitions - using social distance, personal cost, identifiable victim information, and emotion induction - paired with the two-response paradigm and process-tracing measures (mouse-tracking, eye-tracking, EEG components such as N2 and P3). We further propose that the internalization of intuitions is itself plastic: brief positive or negative social interactions can update the baseline strength of cooperation versus defection heuristics, and cross-cultural variation in moral foundation network shapes which intuitions are preferentially activated. These propositions move the literature beyond post-hoc explanations toward falsifiable predictions. In applied terms, the framework suggests a dual-pathway intervention strategy. At the immediate level, choice architecture can temporarily boost specific prosocial intuitions (e.g., highlighting victim faces to activate empathy, or using descriptive norms to trigger conformity). At the structural level, policy, education, and cultural narratives can create environments that consistently reward cooperative behaviors, gradually strengthening the baseline advantage of prosocial intuitions in future competition. Together, these pathways offer a mechanism-based approach to promoting prosocial behavior that complements existing training-focused interventions. In sum, this review does not merely add another model to the prosocial decision-making literature; it offers a principled way to integrate the origins, online competition, and dynamic regulation of intuitions within a single framework. By shifting the research focus from “which processing mode dominates” to “how multiple intuitions are activated, compete, and interact with deliberation,” thus offering a more inclusive theoretical architecture for reconciling previously inconsistent findings and for guiding future empirical work on the cognitive mechanisms of prosocial behavior. References | Related Articles | Metrics