Visual search and childhood vision impairment: A GAMLSS-oriented multiverse analysis approach

Abstract

The aim of this report was to analyze reaction times and accuracy in children with a vision impairment performing a feature-based visual search task using a multiverse statistical approach. The search task consisted of set sizes 4, 16, and 24, consisting of distractors (circle) and a target (ellipse) that were presented randomly to school-aged individuals with or without a vision impairment. Interactions and main effects of key variables relating to reaction times and accuracy were analyzed via a novel statistical method blending GAMLSS (generalized additive models for location, scale, and shape) and distributional regression trees. Reaction times for the target-present and target-absent conditions were significantly slower in the vision impairment group with increasing set sizes (p < .001). Female participants were significantly slower than were males for set sizes 16 and 24 in the target-absent condition (p < .001), with male participants being significantly slower than females in the target-present condition (p < .001). Accuracy was only significantly worse (p = .03) for participants less than 14 years of age for the target-absent condition with set sizes 16 and 24. There was a positive association between binocular visual acuity and search time (p < .001). The application of GAMLSS with distributional regression trees to the analysis of visual search data may provide further insights into underlying factors affecting search performance in case-control studies where psychological or physical differences may influence visual search outcomes.

Assessing Potential Heteroscedasticity in Psychological Data: A GAMLSS approach

“…Thus, GAMLSS stands as a broader applicable alternative to LM without suffering from the problems that emerge from its fundamental, often non-valid assumptions, such as homoscedasticity. Lastly, it is worth noting that present variations of GAMLSS render it appropriate for machine learning analyses, such as distributional regression trees and forests (see for example, the work of Klein, 2024 andConstable et al, 2023).…”