Spectral mechanisms of spatially induced blackness: Data and quantitative model

Keizo Shinomori, Brooke E. Schefrin, John S Werner

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Spectral efficiency functions and tests of additivity were obtained with three observers to identify possible chromatic contributions to spatially induced blackness. Stimuli consisted of a series of monochromatic (400-700 nm; 10-nm steps), 52-arcmin circular test lights surrounded by broadband (x = 0.31, y = 0.37), 63-138-arcmin annuli of fixed retinal illuminance. The stimuli were imaged on the fovea in Maxwellian view as 500-ms flashes with 10-s interstimulus intervals. Observers decreased the intensity of the test center until it was first perceived as completely black. Action spectra determined for two surround levels [2.5 and 3.5 log trolands] had three sensitivity peaks (at approximately 440, 540, and 600 nm). However, when monochromatic surrounds were adjusted to induce blackness in a broadband center, action spectra were unimodal and identical to functions obtained by heterochromatic flicker photometry. Tests of additivity revealed that when blackness is induced by a broadband surround into a bichromatic center, there is an additivity failure of the cancellation type. This additivity failure indicates that blackness induction is influenced, in part, by signals from opponent-chromatic pathways. A quantitative model is presented to account for these data. This model assumes that blackness induction is determined by the ratio of responses to the stimulus center and the annulus, and while signals from the annulus are based only on achromatic information, responses from the center are based on both chromatic and achromatic properties of the stimulus.

Original languageEnglish (US)
Pages (from-to)372-387
Number of pages16
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number2
StatePublished - 1997
Externally publishedYes

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Vision and Pattern Recognition
  • Engineering(all)


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