Direct vs. Score-based Selection: Understanding the Heisenberg Effect in Target Acquisition Across Input Modalities in Virtual Reality

2026-02-01

AI Summary

This study addresses the Heisenberg effect in virtual reality—where confirmation actions such as button presses or pinch gestures perturb tracking pose, particularly under barehand input, whose underlying mechanisms remain unclear. Through a within-subjects experiment, the authors systematically compare controller-based and barehand modalities under two selection paradigms: direct selection and rating-based selection. They propose the weighted VOTE model, a voting algorithm that dynamically reweights historical interaction intents to mitigate pose disturbances. Results demonstrate that barehand input is more susceptible to perturbation; direct selection is primarily influenced by target width, whereas rating-based selection is more affected by target density. The proposed model significantly outperforms baseline methods, effectively enhancing selection accuracy in VR environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Voting
• Intelligent Robots: Manipulation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Heisenberg Effect
• target acquisition
• virtual reality
• input modality
• selection mechanism

Innovation

• Heisenberg Effect
• score-based selection
• weighted VOTE
• bare-hand input
• target acquisition