# The role of spatial frequency channels in letter identification

The role of spatial frequency channels in letter identification How we see is today explained by physical optics and retinal transduction, followed by feature detection, in the cortex, by a bank of parallel independent spatial-frequency-selective channels. It is assumed that the observer uses whichever channels are best for the task at hand. Our current results demand a revision of this framework: Observers are not free to choose which channels they use. We used critical-band masking to characterize the channels mediating identification of broadband signals: letters in a wide range of fonts (Sloan, Bookman, Künstler, Yung), alphabets (Roman and Chinese), and sizes (0.1–55°). We also tested sinewave and squarewave gratings. Masking always revealed a single channel, 1.6±0.7 octaves wide, with a center frequency that depends on letter size and alphabet. We define an alphabet's stroke frequency as the average number of lines crossed by a slice through a letter, divided by the letter width. For sharp-edged (i.e. broadband) signals, we find that stroke frequency completely determines channel frequency, independent of alphabet, font, and size. Moreover, even though observers have multiple channels, they always use the same channel for the same signals, even after hundreds of trials, regardless of whether the noise is low-pass, high-pass, or all-pass. This shows that observers identify letters through a single channel that is selected bottom-up, by the signal, not top-down by the observer. We thought shape would be processed similarly at all sizes. Bandlimited signals conform more to this expectation than do broadband signals. Here, we characterize processing by channel frequency. For sinewave gratings, as expected, channel frequency equals sinewave frequency f channel = f . For bandpass-filtered letters, channel frequency is proportional to center frequency f channel ∝ f center (log–log slope 1) when size is varied and the band (c/letter) is fixed, but channel frequency is less than proportional to center frequency f channel ∝ f center 2/3 (log–log slope 2/3) when the band is varied and size is fixed. Finally, our main result, for sharp-edged (i.e. broadband) letters and squarewaves, channel frequency depends solely on stroke frequency, f channel /10 c/deg = f stroke /10 c/deg 2/3 , with a log–log slope of 2/3. Thus, large letters (and coarse squarewaves) are identified by their edges; small letters (and fine squarewaves) are identified by their gross strokes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Vision Research Elsevier

# The role of spatial frequency channels in letter identification

Vision Research, Volume 42 (9) – Apr 1, 2002
20 pages

/lp/elsevier/the-role-of-spatial-frequency-channels-in-letter-identification-z4lXZ38LSZ
Publisher
Elsevier
ISSN
0042-6989
eISSN
1878-5646
DOI
10.1016/S0042-6989(02)00045-7
Publisher site
See Article on Publisher Site

### Abstract

How we see is today explained by physical optics and retinal transduction, followed by feature detection, in the cortex, by a bank of parallel independent spatial-frequency-selective channels. It is assumed that the observer uses whichever channels are best for the task at hand. Our current results demand a revision of this framework: Observers are not free to choose which channels they use. We used critical-band masking to characterize the channels mediating identification of broadband signals: letters in a wide range of fonts (Sloan, Bookman, Künstler, Yung), alphabets (Roman and Chinese), and sizes (0.1–55°). We also tested sinewave and squarewave gratings. Masking always revealed a single channel, 1.6±0.7 octaves wide, with a center frequency that depends on letter size and alphabet. We define an alphabet's stroke frequency as the average number of lines crossed by a slice through a letter, divided by the letter width. For sharp-edged (i.e. broadband) signals, we find that stroke frequency completely determines channel frequency, independent of alphabet, font, and size. Moreover, even though observers have multiple channels, they always use the same channel for the same signals, even after hundreds of trials, regardless of whether the noise is low-pass, high-pass, or all-pass. This shows that observers identify letters through a single channel that is selected bottom-up, by the signal, not top-down by the observer. We thought shape would be processed similarly at all sizes. Bandlimited signals conform more to this expectation than do broadband signals. Here, we characterize processing by channel frequency. For sinewave gratings, as expected, channel frequency equals sinewave frequency f channel = f . For bandpass-filtered letters, channel frequency is proportional to center frequency f channel ∝ f center (log–log slope 1) when size is varied and the band (c/letter) is fixed, but channel frequency is less than proportional to center frequency f channel ∝ f center 2/3 (log–log slope 2/3) when the band is varied and size is fixed. Finally, our main result, for sharp-edged (i.e. broadband) letters and squarewaves, channel frequency depends solely on stroke frequency, f channel /10 c/deg = f stroke /10 c/deg 2/3 , with a log–log slope of 2/3. Thus, large letters (and coarse squarewaves) are identified by their edges; small letters (and fine squarewaves) are identified by their gross strokes.

### Journal

Vision ResearchElsevier

Published: Apr 1, 2002

### References

• The psychophysics toolbox
Brainard, D.H.
• The application of Fourier analysis to the visibility of gratings
Campbell, F.W.; Robson, J.G.
• Spatial-frequency and contrast properties of crowding
Chung, S.T.L.; Levi, D.M.; Legge, G.E.
• Identification of band-pass filtered letter and faces by human and ideal observers
Gold, J.; Bennett, P.J.; Sekuler, A.B.
• Visual pattern analyzers
Graham, N.V.S.
• The VideoToolbox software for visual psychophysics: transforming numbers into movies
Pelli, D.G.
• Dr. Angry and Mr. Smile: when categorization flexibly modifies the perception of faces in rapid visual presentations
Schyns, P.G.; Oliva, A.
• Edge detectors in human vision
Shapley, R.M.; Tolhurst, D.J.
• Human efficiency for recognizing 3-D objects in luminance noise
Tjan, B.S.; Braje, W.L.; Legge, G.E.; Kersten, D.

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