Identifying Letters

We have explored pattern recognition by examining how observers identify letters. We expected 26-way classification of familiar forms to challenge the popular notion of independent feature detection (“probability summation”), but find instead that this theory parsimoniously accounts for our results. We measured the contrast required for identification of a letter briefly presented in visual noise. We tested a wide range of alphabets and scripts (English, Arabic, Armenian, Chinese, Devanagari, Hebrew, and several artificial ones), threeand five-letter words, and various type styles, sizes, contrasts, durations, and eccentricities, with observers ranging widely in age (3 to 68) and experience (none to fluent). Foreign alphabets are learned quickly. In just a few thousand trials, new observers attain the same proficiency in letter identification as fluent readers. Surprisingly, despite this training, the observers—like clinical letter-by-letter readers—have the same subnormal memory span for random strings of these characters as observers seeing them for the first time. Comparisons across tasks and stimuli that vary in difficulty are made possible by pitting the human against the ideal observer, and expressing the results as efficiency. We find that efficiency for letter identification is independent of duration, overall contrast, and eccentricity, and only weakly dependent on size, suggesting that letters are identified similarly across this wide range of viewing conditions. Efficiency is also independent of age and years of reading. However, efficiency does vary across alphabets and type styles, with more complex forms yielding lower efficiencies, as one might expect from Gestalt theories of perception. In fact, we find that efficiency is inversely proportional to complexity (perimeter squared over “ink” area) and nearly independent of everything else. This, and the surprisingly fixed ratio of detection and identification thresholds, suggest that letter identification is mediated by detection of 20 visual features.