Abstract
Keywords: Reading; Letters; Noise; Crowding. We quantified text legibility as a function of various factors present in natural texts. We adapted the visual spread method (Poirier, Gosselin & Arguin, submitted) to a reading task. Stimuli were sentences conforming to MN Read standards (Legge, Ross & Luebker, 1989, O&VS) mixed with dynamic probabilistic noise - i.e. each pixel in the image was associated with a probability that its polarity was inverted on a given refresh cycle of the display screen. Even low levels of noise can alter letter shapes. Noise level varied continuously over the image as initially determined by Gaussian-filtered noise. Participants adjusted noise levels in the text using the mouse until the text appeared homogenously noisy. We assumed that participants increased (or decreased) noise at locations where stimulus features were easy (or difficult) to encode, thus that local noise setting would correlate with legibility. Data on 5 participants and 10 texts revealed interesting effects; demonstrating the method's validity for assessing legibility in natural texts. For example, participants put more noise (1) over spaces than over letters, (2) over the first and last letter of words than over middle letters, and (3) over letters that are highly discriminable (e.g. for the 15 letters that appear at least 10x in the texts, r = .45–.61 with confusability data using traditional psychophysical techniques; see Fiset, Dupuis-Roy, Arguin & Gosselin, in preparation). Critically, these preliminary analyses demonstrate the sensitivity of the method to the legibility of individual letters within its natural context. In-depth analyses will include various other factors, e.g. letter and word frequency, word length and type, letter position in the word, as well as possible interactions between these factors.
This research was supported by a CIHR grant awarded to Martin Arguin, Frédéric Gosselin, and Dan Bub.