Paper
1 October 1990 Detection of display flicker by a cortical correlation mechanism: a refinement of classical flicker theory
Dieter Bauer
Author Affiliations +
Proceedings Volume 1249, Human Vision and Electronic Imaging: Models, Methods, and Applications; (1990) https://doi.org/10.1117/12.19684
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States
Abstract
If we try to predict flicker on visual displays on the basis of classical data using classical linear models of flicker, we find several inconsistences or even contradictions: 1. A considerable fraction of the population of our observers has critical flicker- fusion-frequencies (CFF) well above the equivalent classical data. 2. The slope of the high frequency roll-off of the temporal modulation function (derived from the CFF-distributions for two different display modulations) is clearly less steep than the slope of classical data. 3. The results of measurements of CFF as a function of field size which were done both for displays and light-boxes (with uniform field modulation) differ' considerably. 4. CFF may increase even if the temporal modulation is decreased. This experiment may thus be considered as the "experimentum crucis" against the classical linear models of flicker of Kelly or De Lange. 5. For decreasing deflection speeds of the electron- beam (with field-size and luminance held constant) , increasing CFFs are found. All of the above findings may be reconciliated if correlation detector cells, which receive signal input from spatially separated receptive fields, are added to the neural pathway.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dieter Bauer "Detection of display flicker by a cortical correlation mechanism: a refinement of classical flicker theory", Proc. SPIE 1249, Human Vision and Electronic Imaging: Models, Methods, and Applications, (1 October 1990); https://doi.org/10.1117/12.19684
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KEYWORDS
Modulation

Data modeling

Visual process modeling

Sensors

Visualization

Electronic imaging

Human vision and color perception

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