As virtual reality (VR) becomes widespread, head and hand motion data captured by consumer systems has become substantially more common. However, the extent of what can be inferred from such motion remains unclear. This paper investigates whether \textit{transient cognitive states}, specifically confusion, hesitation, and readiness during different stages of decision-making, can be inferred from VR telemetry alone. We introduce a novel dataset of head and hand motion collected during structured decision-making tasks, with frame-level annotations of these states. We evaluate classical machine learning models, temporal neural networks, and motion foundation models under two protocols: (1) future-in-time prediction for the same users, and (2) cross-user generalization to unseen users. We further propose a VR-native motion adapter that maps sparse VR telemetry to representations compatible with motion foundation models pretrained on large-scale full-body motion data, enabling transfer without explicit full-body reconstruction. To our knowledge, this is the first work to adapt a motion foundation model to VR motion for a classification task. Results show that motion-only sensing captures meaningful signals of cognitive states, and that pretrained motion foundation models generalize more effectively than classical and temporal models even with a small dataset of 24 participants. Our approach achieves 82% accuracy, comparable to and sometimes surpassing human observers. These findings suggest that VR motion encodes richer behavioral information than previously assumed and highlight the potential of large-scale motion pretraining for XR applications. We will release the dataset and modeling framework to support future research.

翻译：随着虚拟现实（VR）技术日益普及，通过消费级系统捕获的头部与手部运动数据变得极其常见。然而，从这类运动数据中能够推断出的信息边界仍不明确。本文探究是否能够仅凭VR遥测数据推断出**瞬态认知状态**——具体而言，即决策过程中不同阶段的困惑、犹豫和准备状态。我们提出一个在结构化决策任务期间收集的头部与手部运动新数据集，并包含这些状态的逐帧标注。我们评估了经典机器学习模型、时序神经网络以及运动基础模型在两种协议下的表现：（1）面向同类用户的未来时序预测，以及（2）跨用户泛化至未见用户。我们进一步提出一种VR原生运动适配器，可将稀疏的VR遥测数据映射为与在大规模全身运动数据上预训练的运动基础模型兼容的表征，从而无需显式重建全身即可实现迁移。据我们所知，这是首个将运动基础模型适配至VR运动分类任务的工作。结果表明，纯运动传感能够捕获有意义的认知状态信号，且预训练的运动基础模型即使基于仅24名参与者的数据集，其泛化效果也优于经典模型和时序模型。我们的方法达到82%的准确率，能够与人类观察者相当甚至超越。这些发现表明，VR运动编码的行为信息比先前假设更为丰富，并突显了大尺度运动预训练在扩展现实（XR）应用中的潜力。我们将公开数据集与建模框架以支持未来研究。