Target selection is a fundamental interaction in virtual reality (VR). But the act of confirming a selection, such as a button press or pinch, can disturb the tracked pose and shift the intended target, which is referred to as the Heisenberg Effect. Prior research has mainly investigated controller input. However, it remains unclear how the effect manifests in the bare-hand input and how score-based techniques may mitigate the effect in different spatial variations. To fill the gap, we conduct a within-subject study to examine the Heisenberg Effect across two input modalities (i.e., controller and hand) and two selection mechanisms (i.e., direct and score-based). Our results show that hand input is more susceptible to the Heisenberg Effect, with direct selection more influenced by target width and score-based selection more sensitive to target density. Based on previous vote-oriented technique and our temporal analysis, we introduce weighted VOTE, a history-based intention accuracy model for target voting, that reweights recent interaction intent to counteract input disturbances. Our evaluation shows the method improves selection accuracy compared to baseline techniques. Finally, we discuss future directions for adaptive selection methods.

翻译：目标选择是虚拟现实（VR）中的一项基础交互。然而，确认选择的行为（如按键或捏合）可能干扰追踪姿态并偏移预期目标，这一现象被称为海森堡效应。先前研究主要集中于控制器输入。然而，该效应在徒手输入中的表现方式，以及基于评分的技术如何在不同空间变化中缓解该效应，目前尚不明确。为填补这一空白，我们开展了一项被试内研究，以考察两种输入模态（即控制器与手部）和两种选择机制（即直接选择与基于评分的选择）下的海森堡效应。研究结果表明，手部输入更易受海森堡效应影响，其中直接选择受目标宽度影响更大，而基于评分的选择对目标密度更为敏感。基于先前的投票导向技术及我们的时序分析，我们提出了加权VOTE——一种基于历史的目标投票意图精度模型，该模型通过重新加权近期交互意图来抵消输入干扰。评估结果显示，相较于基线技术，该方法提升了选择精度。最后，我们讨论了自适应选择方法的未来发展方向。