Journal: Frontiers in Psychology

Article Title: Evidence for adjustable bandwidth orientation channels

doi: 10.3389/fpsyg.2014.00578

Figure Lengend Snippet: The black dotted lines show the one-dimensional circularly summed classification images for two observers in three conditions . The red lines show the best difference of Gaussian fits to the empirical data. The x -axis in each sub-figure is orientation, the y -axis the weight of the classification image.

Article Snippet: Researchers have used two-dimensional luminance noise most frequently, but studies have also used one-dimensional luminance noise (i.e., noise that is constrained to vary along a single dimension) as well as visual noise that varies in other ways such as color (Gegenfurtner and Kiper, ), motion (Dakin et al., ), orientation (Girshick et al., ), Gaussian spatial windowing or bubbles (Gosselin and Schyns, ), and zero-dimensional noise pedestal increments (Baker and Meese, ).

Techniques:

Journal: Frontiers in Psychology

Article Title: Evidence for adjustable bandwidth orientation channels

doi: 10.3389/fpsyg.2014.00578

Figure Lengend Snippet: Fitted difference of Gaussian bandwidth parameters for two observers, AP in circles and MB in triangles . The error bars are 95% confidence intervals on the fitted parameter obtained via the bootstrap. Blue symbols are for the positive, or central Gaussian, and the red symbols the negative or surround Gaussian. R was used to obtain a weighted least squares fit to the parameters as a function of bandwidth. Both increased by roughly 50% for both observers as bandwidth was increased from the narrowest stimulus bandwidth to the widest.

Article Snippet: Researchers have used two-dimensional luminance noise most frequently, but studies have also used one-dimensional luminance noise (i.e., noise that is constrained to vary along a single dimension) as well as visual noise that varies in other ways such as color (Gegenfurtner and Kiper, ), motion (Dakin et al., ), orientation (Girshick et al., ), Gaussian spatial windowing or bubbles (Gosselin and Schyns, ), and zero-dimensional noise pedestal increments (Baker and Meese, ).

Techniques: