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Cited by 454

The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.

Feature detection and letter identification

Open access
Pelli, Denis G., Burns, Catherine W., Farell, Bart, Moore-Page, Deborah C.·2006·Vision Research

Abstract

Seeking to understand how people recognize objects, we have examined how they identify letters. We expected this 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), three- and 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 three 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 meager memory span for random strings of these characters as observers seeing them for the first time. We compare performance across tasks and stimuli that vary in difficulty 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 by a similar computation 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 perimetric complexity (perimeter squared over "ink" area) and nearly independent of everything else. This, and the surprisingly fixed ratio of detection and identification thresholds, indicate that identifying a letter is mediated by detection of about 7 visual features.

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Metadata

Authors
Pelli, D. G., Burns, C. W., Farell, B., & Moore-Page, D. C.
Venue
Vision Research
Publisher
Elsevier BV
Published
2006-12
Volume
46
Issue
28
Pages
4646-4674
ISSN
0042-6989 (ISSN-L)
OpenAlex
W2138786365
Cited by
454
References
105
FWCI
7.52
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Feature detection and letter identification. https://app.cochat.ai/papers/10.1016/j.visres.2006.04.023
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Pelli, D. G., Burns, C. W., Farell, B., & Moore-Page, D. C. (2006). Feature detection and letter identification. Vision Research. https://doi.org/10.1016/j.visres.2006.04.023

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