Grid-texture mechanisms in human vision: contrast detection of regular sparse micro-patterns requires specialist templates

Previous work has shown that human vision performs spatial integration of luminance contrastenergy, where signals are squared and summed (with internal noise) over area at detectionthreshold. We tested that model here in an experiment using arrays of micro-pattern texturesthat varied in overall stimulus area and sparseness of their target elements, where the contrast ofeach element was normalised for sensitivity across the visual field. We found a power-lawimprovement in performance with stimulus area, and a decrease in sensitivity with sparseness,and rejected a model involving probability summation across all elements. While the contrastintegrator model performed well when target elements constituted 50-100% of the target area(replicating previous results), observers outperformed the model when texture elements weresparser than this. This result required the inclusion of further templates in our model, selective forvarious regular texture densities. By assuming probability summation across these mechanisms themodel also accounted for the increase in the slope of the psychometric function that occurred astexture density decreased. Thus, we have revealed texture density mechanisms for the first time inhuman vision at contrast detection threshold (where the fitted level of intrinsic uncertainty waslow and the only free parameter)