Abstract
Early spatial vision was explored extensively over several decades in many psychophysical detection and discrimination experiments, and thus a large body of data is available. Goris et al. (2013; Psych. Rev.) integrated this psychophysical literature and proposed a model based on maximum-likelihood decoding of a neurophysiologically inspired population of model neurons. Their neural population model (NPM) is able to predict several data sets simultaneously, using a single set of parameters. However, the NPM is only one-dimensional, operating on the activity of abstract spatial frequency channels. Thus it cannot be applied to arbitrary images as a generic front-end to explore the influence of early visual processing on mid- or high-level vision. Bradley et. al. (2014; JoV), on the other hand, presented a model operating on images. Their model is thus able to make predictions for arbitrary images. However, compared to the NPM, their model lacks in nonlinear processing, which is replaced by an effective masking contrast depending on the detection target. Thus while Bradley et al. fit a range of detection data they do not fit nonlinear aspects of early vision like the dipper function. Here we combine both approaches and present a model which includes nonlinear processing and operates on images. In addition, the model applies optical degradation and retinal processing to the image before it is passed to a spatial frequency and orientation decomposition followed by divisive inhibition. For the optical transfer function of the eye and the distribution of retinal midget ganglion cells we use the approximations of Watson (2013, 2014; JoV). We tested the predictions of our model against a broad range of early psychophysical literature and found it predicts some hallmarks of early visual processing like the contrast sensitivity function under different temporal conditions and the dipper function for contrast discrimination.
Meeting abstract presented at VSS 2016