发表机构
Uşak University（乌沙克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出社会对偶性这一关系过程框架，通过嵌套单元定义人机交互过程，经实验验证其可用于分析人机交互中贡献的形成及路径信息。
AI 中文摘要
人机研究常评估个体能力、组合性能或最终输出，但这些方法未保留一方的响应如何成为另一方后续贡献形成条件的过程。本文提出社会对偶性，这是两个可区分主体间的序列性、互惠性且承载历史的关系过程，其中一方的响应会成为另一方后续贡献、判断、决策或行动形成的可观测条件的一部分。该构念针对人机二元组，采用嵌套单元：行动（moves）、已确认的社会对偶 episodes、关联路径及更宽泛的交互容器。一个最小 episode A1-B1-A2 需要响应偶然性和返回偶然性的证据；候选 episodes 在二次编码响应取向和实质性贡献重构前，被分为已确认、非社会对偶或不确定三类。三个命题分别涉及历史条件下的形成、路径分歧及终点等价路径间的鲁棒性差异。通过两个独立执行的基于模型的评估器系列，在三个此前未见过的自然人机记录上校准了一个冻结操作协议。在两个案例中，行动与候选重构完全一致，第三个案例仅在一个局部多模态单元化决策上存在差异；剩余分歧集中在返回偶然性边界。因此，社会对偶性提供了一个有界且经验上可处理的过程构念，用于分析人机贡献如何通过交互形成，同时保留以终点为中心的分析无法恢复的路径信息。
英文摘要
Human-AI research often evaluates individual capabilities, joint performance, or final outputs, but these approaches can lose the interaction process that produced the result. This article introduces socioduality: a sequential, reciprocal, and history-carrying process in which one party's response becomes part of the observable conditions shaping the other party's next contribution, judgement, decision, or action. For human-AI dyads, the framework identifies moves, candidate episodes, confirmed episodes, and maximal pathways. A minimum episode A1 -> B1 -> A2 requires evidence that B1 responds to A1 and that B1 then enters the formation of A2; candidates are classified as confirmed, non-sociodual, or indeterminate. A frozen coding protocol was calibrated on three natural human-AI records using two separate model-based evaluator series. A supplementary exploratory analysis then compared frozen Sociodual pathways with blind developmental/task-process segmentations. Across six examined interactions, the two representations were empirically non-equivalent: task-stage changes could occur within a continuing Sociodual pathway, while formal pathway breaks could occur within a continuing task context. This distinction persisted under fine-grained re-segmentation and record-format checks and was reproduced in all three prospectively selected unseen records using a fresh model-based Sociodual coding line. Socioduality therefore offers a bounded process-level framework for studying how human and AI contributions become relationally linked across time, preserving information that task-stage and endpoint-centred analyses do not uniquely recover.
Comments50 pages, 4 figures, 12 tables. Revised version adds a Supplementary Exploratory Analysis comparing Sociodual pathways with blind developmental/task-process segmentation, with granularity and record-format checks and an unseen-case extension. Main construct specification unchanged; minor editorial and terminology refinements
