Standard models of spatial vision mispredict edge sensitivity at low spatial frequencies

One well-established characteristic of early visual processing is the contrast sensitivity function (CSF) which describes how sensitivity varies with the spatial frequency (SF) content of the visual input. The CSF prompted the development of a now standard model of spatial vision. It represents the visual input by activity in orientation- and SF selective channels which are nonlinearly recombined to predict a perceptual decision. The standard spatial vision model has been extensively tested with sinusoidal gratings at low contrast because their narrow SF spectra isolate the underlying SF selective mechanisms. It is less studied how well these mechanisms account for sensitivity to more behaviourally relevant stimuli such as sharp edges at high contrast (i.e. object boundaries) which abound in the natural environment and have broader SF spectra. Here, we probe sensitivity to edges (2-AFC, edge localization) in the presence of broadband and narrowband noises. We use Cornsweet luminance profiles with peak frequencies at 0.5, 3 and 9 cpd as edge stimuli. To test how well mechanisms underlying sinusoidal contrast sensitivity can account for edge sensitivity, we implement a single- and a multi-scale model building upon standard spatial vision model components. Both models account for most of the data but also systematically deviate in their predictions, particularly in the presence of pink noise and for the lowest SF edge. These deviations might indicate a transition from contrast- to luminance-based detection at low SFs. Alternatively, they might point to a missing component in current spatial vision models.