Close Values on Spatial Dimensions
Norma Graham · Oxford University Press eBooks · 1989
To measure an analyzer's bandwidth, summation experiments using close values on the dimension can be used. This chapter is the natural sequel to the preceding two chapters that discussed summation experiments using far-apart values. It first presents a common simple model used to interpret close-value summation experiments. It then presents more complicated models that allow, e.g., for probability or nonlinear summation across analyzers and for interaction among dimensions. These lead to less-confounded bandwidth measurements. The relation of such models to the concept of matched filters is discussed. The results for spatial dimensions of pattern vision (spatial frequency, position, extent, and phase) are summarized. An appendix briefly gives two popular models of spatial vision.