arXiv subjects

Brandon Victor Syiem

Publications and source records attributed to Brandon Victor Syiem.

2 recordsLinked to original sources

arXiv2026

Searching Through Complex Worlds: Visual Search and Spatial Regularity Memory in Mixed Reality

Visual search is a core component of mixed reality (MR) interactions, influenced by the complexities of MR application contexts. In this paper, we investigate how prevalent factors in MR influence visual search performance and spatial regularity memory -- including the physical environment complexity, secondary task presence, virtual content depth and spatial layout configurations. Contrary to prior work, we found that the secondary auditory task did not have a significant main effect on visual search performance, while significantly elevating higher perceived workload measures in all conditions. Complex environments and varied virtual elements depths significantly hinder visual search, but did not significantly increase perceived workload measures. Finally, participants did not explicitly recognize repeated spatial configurations of virtual elements, but performed significantly better when searching repeated spatial configurations, suggesting implicit memory of spatial regularities. Our work presents novel insights on visual search and highlights key considerations when designing MR for different application contexts.

cs.HC↗

arXiv2026

Better Assumptions, Stronger Conclusions: The Case for Ordinal Regression in HCI

Despite the widespread use of ordinal measures in HCI, such as Likert-items, there is little consensus among HCI researchers on the statistical methods used for analysing such data. Both parametric and non-parametric methods have been extensively used within the discipline, with limited reflection on their assumptions and appropriateness for such analyses. In this paper, we examine recent HCI works that report statistical analyses of ordinal measures. We highlight prevalent methods used, discuss their limitations and spotlight key assumptions and oversights that diminish the insights drawn from these methods. Finally, we champion and detail the use of cumulative link (mixed) models (CLM/CLMM) for analysing ordinal data. Further, we provide practical worked examples of applying CLM/CLMMs using R to published open-sourced datasets. This work contributes towards a better understanding of the statistical methods used to analyse ordinal data in HCI and helps to consolidate practices for future work.

stat.ME↗