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Sticky and Magnetic: Evaluating Error Correction and User Adaptation in Gaze and Pinch Interaction

Jazmin Collins, Prasanthi Gurumurthy, Eric J. Gonzalez, Mar Gonzalez-Franco

Abstract

The gaze-and-pinch framework offers a high-fidelity interaction modality for spatial computing in virtual reality (VR), yet it remains vulnerable to coordination errors--timing misalignments between gaze fixation and pinch gestures. These errors are categorized into two types: late triggers (gaze leaves a target before pinch) and early triggers (pinch before gaze arrival on target). While late triggers are well-studied, early triggers lack robust solutions. We investigate two heuristics--STICKY selection (temporal buffer) and MAGNETIC selection (spatial field)--to mitigate these errors. A within-subjects study (N = 9) on the Samsung Galaxy XR evaluated these heuristics against a baseline. Findings indicate that while throughput and selection time remained stable, the heuristics fundamentally shifted user behavior and significantly reduced errors during selection. Notably, MAGNETIC selection induced an "offloading" effect where users traded precision for speed. Additionally, the heuristics reclassified ambiguous failures as explainable coordination errors. We provide recommendations for selection heuristics that enhance interaction speed and cognitive agency in virtual reality.

Sticky and Magnetic: Evaluating Error Correction and User Adaptation in Gaze and Pinch Interaction

Abstract

The gaze-and-pinch framework offers a high-fidelity interaction modality for spatial computing in virtual reality (VR), yet it remains vulnerable to coordination errors--timing misalignments between gaze fixation and pinch gestures. These errors are categorized into two types: late triggers (gaze leaves a target before pinch) and early triggers (pinch before gaze arrival on target). While late triggers are well-studied, early triggers lack robust solutions. We investigate two heuristics--STICKY selection (temporal buffer) and MAGNETIC selection (spatial field)--to mitigate these errors. A within-subjects study (N = 9) on the Samsung Galaxy XR evaluated these heuristics against a baseline. Findings indicate that while throughput and selection time remained stable, the heuristics fundamentally shifted user behavior and significantly reduced errors during selection. Notably, MAGNETIC selection induced an "offloading" effect where users traded precision for speed. Additionally, the heuristics reclassified ambiguous failures as explainable coordination errors. We provide recommendations for selection heuristics that enhance interaction speed and cognitive agency in virtual reality.

Paper Structure

This paper contains 14 sections, 4 figures.

Figures (4)

  • Figure 1: Target setup in the selection task showing correct (middle) and incorrect (right) selections.
  • Figure 2: Different types of errors recorded across each study condition (by percentage). Red = Early Trigger errors, Green = Late trigger errors, Blue = All other errors not classified as late or early triggers (i.e., outside the 350 ms window).
  • Figure 3: Late trigger error rates recorded across each study condition (by percentage). Lines connect the points on the plot represented by the same participant, to show the trend of their performance across conditions.
  • Figure 4: Error reduction rates recorded across each study condition (by percentage). Lines connect points represented by the same participant, to show difference between error rates. Solitary red points indicate cases where the error rate was identical with and without heuristics.