Abstract
Assessing visual function is a fundamental aspect of eye research. However, existing tests often face limitations due to their design for in-clinic use, the necessity for trained personnel, time consumption, and the provision of coarse result resolution.
This presentation will review the current limitations of vision assessment and introduce a range of new visual function tools developed to address these challenges. Specifically, it will describe various rapid, generalizable psychophysical paradigms capable of constructing personalized performance models. Additionally, it will cover tools for continuous perceptual multistability measurement. The advent of these tools has secondary effects, such as enabling the use of machine learning to detect novel categories of atypical vision and to identify redundant and predictive visual functions for specific populations.
The presentation will include examples from typical clinical populations such as individuals with refractive errors, color vision deficits, amblyopia, albinism, and retinal disorders. Moreover, the talk will advocate for expanding vision assessments beyond conventional screening of e.g., acuity, contrast, or color, highlighting the importance of evaluating other visual modalities such as form, motion, face, and object perception and their clinical relevance.