Texture variations suppress suprathreshold brightness and colour variations
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
- McGill University
Discriminating material changes from illumination changes is a key function of early vision. Luminance cues are ambiguous in this regard, but can be disambiguated by co-incident changes in colour and texture. Thus, colour and texture are likely to be given greater prominence than luminance for object segmentation, and better segmentation should in turn produce stronger grouping. We sought to measure the relative strengths of combined luminance, colour and texture contrast using a suprathreshhold, psychophysical grouping task. Stimuli comprised diagonal grids of circular patches bordered by a thin black line and contained combinations of luminance decrements with either violet, red, or texture increments. There were two tasks. In the Separate task the different cues were presented separately in a two-interval design, and participants indicated which interval contained the stronger orientation structure. In the Combined task the cues were combined to produce competing orientation structure in a single image. Participants had to indicate which orientation, and therefore which cue was dominant. Thus we established the relative grouping strength of each cue pair presented separately, and compared this to their relative grouping strength when combined. In this way we observed suprathreshold interactions between cues and were able to assess cue dominance at ecologically relevant signal levels. Participants required significantly more luminance and colour compared to texture contrast in the Combined compared to Separate conditions (contrast ratios differed by about 0.1 log units), showing that suprathreshold texture dominates colour and luminance when the different cues are presented in combination.
|Publication status||Published - 12 Dec 2014|
- Background signal noise, Colour vision, Eye movements, Luminance, Modulation, Psychophysics, Research monitoring, Texture