联合行动中的自我-他人整合:基于层级任务表征的视角OACHSSCD

Joint action constitutes a core component of social life.Its tasks often exhibit a hierarchical structure ranging from concrete to abstract,requiring co-actors to achieve effective self-other integration at each level.However,existing research has largely been confined to isolated investigations of single levels,treating interpersonal coordination as a static mapping and thus failing to capture the dynamic interactions and information accumulation mechanisms that operate across hierarchical levels.Adopting the perspective of"hierarchical task representation,"this study explores:(1)the dissociation and interaction of self-other integration at higher and lower levels,at both behavioral and neural levels;(2)whether level specificity arises from differences in the patterns of prior information accumulation,and how cross-level interactions are realized through the dynamic coupling of prior and posterior distributions;and(3)how different interpersonal common ground contexts modulate this process.Methodologically,this study constructs a hierarchically nested task paradigm,integrating behavioral experiments,EEG hyperscanning,and cognitive computational modeling to investigate level specificity and interaction patterns along three dimensions:phenomenon,mechanism,and modulation.Furthermore,this study proposes a Bayesian computation-based theoretical model of self-other integration,enabling a paradigm shift from"static single-level representation"to"dynamic hierarchical prediction."This research deepens the understanding of the dynamic mechanisms underlying interpersonal integration across higher and lower levels,and provides a computable and verifiable psychological foundation for the development of collaborative multi-agent systems.