[论文解读] TPIFM: A Task-Aware Model for Evaluating Perceptual Interaction Fluency in Remote AR Collaboration

Jiarun Song, Ninghao Wan+2 more|arXiv (Cornell University)|Mar 10, 2026

Augmented Reality Applications被引用 0

一句话总结

论文提出 TPIFM，一种面向任务的模型，用于在网络阻塞下评估远程增强现实协作中的知觉互动流畅性（PIF），考虑任务特定的 JND 并通过主观实验验证模型。

ABSTRACT

Remote Collaborative Augmented Reality (RCAR) enables geographically distributed users to collaborate by integrating virtual and physical environments. However, because RCAR relies on real-time transmission, it is susceptible to delay and stalling impairments under constrained network conditions. Perceptual interaction fluency (PIF), defined as the perceived pace and responsiveness of collaboration, is influenced not only by physical network impairments but also by intrinsic task characteristics. These characteristics can be interpreted as the task-specific just-noticeable difference (JND), i.e., the maximal tolerable temporal responsiveness before PIF degrades. When the average response time (ART), measured as the mean time per operation from receiving collaborator feedback to initiating the next action, falls within the JND, PIF is generally sustained, whereas values exceeding it indicate disruption. Tasks differ in their JNDs, reflecting distinct temporal responsiveness demands and sensitivities to impairments. From the perspective of the Free Energy Principle (FEP), tasks with lower JNDs impose stricter temporal prediction demands, making PIF more vulnerable to impairments, whereas higher JNDs allow greater tolerance. On this basis, we classify RCAR tasks by JND and evaluate their PIF through controlled subjective experiments under delay, stalling, and hybrid conditions. Building on these findings, we propose the Task-Aware Perceptual Interaction Fluency Model (TPIFM). Experimental results show that TPIFM accurately assesses PIF under network impairments, providing guidance for adaptive RCAR design and user experience optimization under network constraints.

研究动机与目标

• 在 RCAR 中推动并定义知觉互动流畅性（PIF）及其对网络障碍和任务特征的依赖性。
• 引入任务特定的最小可觉差（JND）概念，作为时间响应容忍度的度量。
• 开发面向任务的知觉互动流畅性模型（TPIFM），在延迟、阻塞和混合条件下评估 PIF。
• 将 RCAR 任务按 JND 分类，并通过受控主观实验分析 PIF，以为自适应 RCAR 设计提供依据。

提出的方法

• 在 RCAR 情境中定义 PIF 与 JND，并将 JND 与自由能原理（FEP）预测相关联。
• 在延迟、阻塞和混合障碍下进行受控主观实验，收集跨任务的 PIF 数据。
• 开发 TPIFM，将观测到的 PIF 映射到任务 JND 与障碍类型。
• 评估 TPIFM 在网络障碍下评估 PIF 的准确性，并为 RCAR 优化提供设计指南。

实验结果

研究问题

• RQ1 在网络障碍下，任务特定的 JND 如何影响远程 AR 协作中的知觉互动流畅性？
• RQ2 TPIFM 是否能在延迟、阻塞和混合条件下准确评估 PIF？
• RQ3 按照 FEP 的时间预测需求，如何影响不同 JND 的任务的 PIF 易损性？
• RQ4 TPIFM 能为网络约束下的自适应 RCAR 设计提供哪些指导？

主要发现

• TPIFM 在受控实验中的网络障碍情境下成功评估 PIF。
• JND 较低的任务对时间预测要求更严格，PIF 易受影响程度更大。
• 如模型所预测，JND 较高的任务对时间障碍具有更强的容忍度。
• 结果为在网络约束下的自适应 RCAR 设计和用户体验优化提供指导。