First, for each of these six overt

First, for each of these six overt functions in the session for each assessor, the best fitting least-squares linear regression equation was calculated, entering either logarithms of amounts or untransformed percentages of fat. The norm points were calculated by interpolation, e.g. for acceptance, to “always choose” to give the personal ideal point (IP) for the visible labeled fat content or amount of spread. The half-discriminated disparities (HDDs) were determined by entering the slope of the regression line and the mean squared error about the line into the traditional formula for the just-noticeable difference (JND)