Abstract
Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50–100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.
Highlights
The square grid-textures were of various sizes, and various densities, which we report as mark:space ratios
Human performance was better than predicted by models that either maxed or summed over the signal and internal noise, including the inter-element internal noise
We discuss the implications of this model for our understanding of area summation of luminance contrast, and the perception of texture density/ numerosity
Summary
At one extreme (often taken to be a minimum combination rule[13], akin to what is sometimes called probability summation14) the observer might monitor their noisy neural activities across their spatial representation, picking the largest (MAX) response as the best evidence for signal in that stimulus interval This has the advantage that irrelevant noise is not accumulated over space into the decision variable, but the disadvantage that there is little benefit from increasing the number of local signals in the display (e.g. from increasing overall stimulus size). For very sparse textures (e.g. the rightmost panel in Fig. 1a) very little of the stimulus energy will be detectable To ameliorate this problem we normalized the contrast of our stimulus elements using detailed measurements of binocular sensitivity across the visual field[9,16] for the same three observers who took part in the study here. We compared them to the predictions of several computational models
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