Feature detection and letter identification

Feature detection and letter identification 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Vision Research Elsevier

Feature detection and letter identification

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Publisher
Elsevier
Copyright
Copyright © 2006 Elsevier Ltd
ISSN
0042-6989
eISSN
1878-5646
DOI
10.1016/j.visres.2006.04.023
Publisher site
See Article on Publisher Site

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.

Journal

Vision ResearchElsevier

Published: Dec 1, 2006

References

  • Early-visual factors in letter confusions
    Blommaert, F.J.
  • Human efficiency for recognizing and detecting low-pass filtered objects
    Braje, W.L.; Tjan, B.S.; Legge, G.E.
  • The application of Fourier analysis to the visibility of gratings
    Campbell, F.W.; Robson, J.G.
  • Spatial-frequency characteristics of letter identification in central and peripheral vision
    Chung, S.T.L.; Legge, G.E.; Tjan, B.S.
  • Learning letter identification in peripheral vision
    Chung, S.T.L.; Levi, D.M.; Tjan, B.S.
  • Identification of band-pass filtered letters and faces by human and ideal observers
    Gold, J.; Bennett, P.J.; Sekuler, A.B.
  • Visual pattern analyzers
    Graham, N.V.S.
  • The role of spatial frequency channels in letter identification
    Majaj, N.J.; Pelli, D.G.; Kurshan, P.; Palomares, M.
  • Neural specialization for letter recognition
    Polk, A.T.; Stallcup, M.; Aquirre, G.K.; Alsop, D.C.; D’Esposito, M.; Detre, J.A.
  • Coding visual images of objects in the inferotemporal cortex of the macaque monkey
    Tanaka, K.; Saito, H.; Fukada, Y.; Moriya, M.
  • Cellular processes of learning and memory in the mammalian CNS
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  • Human efficiency for recognizing 3-D objects in luminance noise
    Tjan, B.S.; Braje, W.L.; Legge, G.E.; Kersten, D.J.
  • Foundations of vision
    Wandell, B.A.

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