Abstract
Purpose :
Reading is a fundamental skill and the reading performance is a key endpoint for quantifying normal or abnormal development and aging. Successful reading performance requires ophthalmic, cognitive and oculomotor proficiency. The deficit or pathology in any of these functions can lead to a deficit in reading performance (Legge et al 1985). Despite its importance for clinical and developmental assessment, existing reading tests are time consuming and difficult to administer. In this study, we propose a novel method, the quick Reading method, for automated measurement of reading speed at multiple letter sizes based on Bayesian adaptive testing (Lesmes, et al., 2010).
Methods :
A three-parameter exponential function is used to describe the reading speed vs print size function. The quick Reading method selects the optimal test stimulus (print size and presentation duration) by maximizing the expected information gain in each trial and updates the posterior distribution of the parameters of the reading function. The precision and bias of the estimated reading function of a simulated observer obtained using quick Reading were evaluated. Reading functions measured by the conventional (Psi method, Kontzevich & Tyler, 1999) and quick Reading methods in a true/false paradigm (Crossland et al, 2008) were compared in an experiment.
Results :
The precision of quick Reading method was 0.26, 0.17 and 0.06 log10 unit after 10, 20 and 100 trials, respectively. The bias of the quick Reading method was 0.21, 0.17 and 0.10 log10 unit after 10, 20 and 100 trials, respectively. The estimated reading functions obtained with the conventional and quick Reading methods did not differ significantly (paired t-test, p = 0.184); There were highly correlated (r = 0.969, p = 0.001). The precision of the reading function obtained with 60 quick Reading trials was comparable to that of conventional method with 240 trials.
Conclusions :
The quick Reading method can be used to precisely and efficiently assess reading performance, with great promise in clinical applications.
This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.