Using geometric moments to explain human letter recognition near the acuity limit.

When the size of a letter stimulus is near the visual acuity limit of a human subject, details of the stimulus become unavailable due to ocular optical and neural filtering. In this study we tested the hypothesis that letter recognition near the acuity limit is dependent on more global features, which could be parsimoniously described by a few easy-to-visualize and perceptually meaningful low-order geometric moments (i.e., the ink area, variance, skewness, and kurtosis). We constructed confusion matrices from a large set of data (approximately 110,000 trials) for recognition of English letters and Chinese characters of various spatial complexities near their acuity limits. We found that a major portion of letter confusions reported by human subjects could be accounted for by a geometric moment model, in which letter confusions were quantified in a space defined by low-order geometric moments. This geometric moment model is universally applicable to recognition of visual patterns of various complexities near their acuity limits.