Exploration of the Underlying Visual Perceptual and Cognitive Mechanisms of Dynamic Visual Acuity

Abstract

Purpose: Dynamic visual acuity (dynamic VA) is a complex, perceptual ability of the visual system that involves determining fine details of objects as they move across one’s field of view (1–4). Over the years, there has been increasing interest in dynamic VA because of its apparent relevance to everyday life, and its ability to account for motion, which static VA is unable to do. Dynamic VA has a crucial role in a variety of real-world situations and daily tasks that involve functioning in a dynamic environment, such as driving, piloting, crossing a busy intersection, and many ball sports (5–8). In addition, dynamic VA is an essential element involved in one’s ability to adapt to moving and changing environments (1). Although various research has been performed, dynamic VA as a visual function is not very well understood. This study was designed to investigate the potential underlying neurophysiological mechanisms that may be associated with dynamic VA. Methods: This study was an observational analysis of visual and cognitive function data collected from 130 participants. Participants were members of the University of Waterloo Department of Psychology Research Experiences Group (i.e., SONA), the University of Waterloo undergraduate and graduate community, the University of Waterloo Optometry Program, and the Kitchener-Waterloo Community. Five visual function tasks were studied including static visual acuity (static VA), horizontal and random dynamic VA, global motion (GM), global form (GF), and local motion (LM), along with two cognitive tasks, multiple object tracking (MOT) and the Stroop task. Static VA was measured first at each study visit to confirm participant eligibility, followed by horizontal and random motion dynamic VA (randomized order). After dynamic VA, the remaining visual and cognitive function tasks were measured in a randomized order. Static VA (LogMAR) was tested with an Early Treatment Diabetic Retinopathy Study (ETDRS) chart. Binocular dynamic VA (LogMAR; moV&, V&mp Vision Suite) was assessed using tumbling E optotypes moving in a horizontal (left to right) or unpredictable random motion. GM perception, and LM perception were assessed using random dot kinematograms (RDKs), GF perception was tested using Glass patterns, Stroop was assessed using word stimuli, and MOT was tested using randomly moving ball stimuli. Experimental effects, including the effects of participant age, participant gender, and testing order were examined for each task independently using one-way independent measures ANOVAs (age and visual function task order), and two-sample t-tests (gender and dynamic VA task order). Tukey post-hoc test was used to further evaluate any significant order effects found with the one-way independent measures ANOVAs. Correlation plots, matrices, and tables including Pearson correlation coefficients were calculated to examine the relationships between dynamic VA performance and the visual function tasks. Backwards stepwise regression analyses were conducted to determine which visual or cognitive function tasks were most predictive of dynamic VA performance. The correlation and regression analyses were performed separately for horizontal and random dynamic VA. Results: Highly significant correlations were found between horizontal dynamic VA and random dynamic VA (r = 0.49, p = 4.84e-9), static VA (r = 0.48, p = 6.35e-9), and LM (r = 0.32, p = 2.47e-4); a weak, significant correlation also noted with GM (r = 0.23, p =9.16e-3). Highly significant correlations with random dynamic VA were found with static VA (r = 0.46, p = 4.39e-8) and horizontal dynamic VA (r = 0.49, p = 4.84e-9); weak, significant correlations were found with LM (r =0.16, p = 6.43e-2), and GF (r = 0.15, p = 9.89e-2). Statistically significant predictors for horizontal dynamic VA were static VA (p = 6.09e-4), LM (p = 3.96e-2), and random dynamic VA (p = 1.20e-4) . GM (p = 0.139) was not a significant predictor of horizontal dynamic VA but still had a trend towards a positive relationship with the task. Static VA (p = 7.85e-4) and horizontal dynamic VA (p = 8.14e-5) were the only statically significant predictors of random dynamic VA, but there were also trends towards positive relationships between random dynamic VA and LM (p = 8.70e-2), and GF (p = 0.135). Additional analyses determined there to be no age or gender effects on any of the visual function tasks. A statistically significant order effect was present for GF (F(2, 127) = [4.92], p = 1.02e-3), but no other tasks. Conclusion: Horizontal dynamic VA appears to be most closely related to random dynamic VA, static VA, GM, and LM, suggesting the dorsal stream and V1 pathway may be the underlying neurophysiological pathways associated with processing horizontal dynamic VA. This is in comparison to random dynamic VA, which was most closely connected with horizontal dynamic VA, static VA, GF, and LM, suggesting the neuro pathways involved with random dynamic VA could be the ventral stream and V1 pathway. Further research is required to confirm and validate such neurophysiological mechanisms are associated with both horizontal and random dynamic VA.