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In an effort to understand the factors influencing text legibility in natural reading, we adapted the visual spread method (Poirier, Gosselin, &amp; Arguin, 2008) to natural text. Stimuli were sentences conforming to MNREAD standards (Legge, Ross, Luebker, &amp; LaMay 1989) mixed with dynamic probabilistic noise-i.e., each pixel in the image is associated with a probability that its polarity is inverted on a given refresh cycle of the display screen. 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 assume that participants increased (or decreased) noise at locations where stimulus features were easy (or difficult) to encode and thus that local noise settings correlate with legibility. Data from 11 participants and 30 sentences revealed interesting effects, demonstrating the validity of the method for assessing the impact of various factors on noise resistance in natural text. For example, participants increased noise over (a) spaces and adjacent letters, (b) the second half of words, (c) words with more orthographic neighbors but fewer phonological neighbors, (d) less useful word types, (e) less complex letters, and (f) diagnostic letters (a novel metric). Our observations also offer significant insights on constraints acting upon letter identification as well as on higher-level processes that are involved in reading.

Subjectively homogeneous noise over written text as a tool to investigate the perceptual mechanisms involved in reading