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

Author:

Constable Paul A.ORCID,Loh Lynne,Prem-Senthil Mallika,Marmolejo-Ramos Fernando

Abstract

AbstractThe 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.

Funder

Flinders University

Publisher

Springer Science and Business Media LLC

Subject

Linguistics and Language,Sensory Systems,Language and Linguistics,Experimental and Cognitive Psychology

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Assessing Potential Heteroscedasticity in Psychological Data: A GAMLSS approach;The Quantitative Methods for Psychology;2023-12-01

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