A salience map is a topographic map that has inputs at each x,y location from many different feature maps and summarizes the combined salience of all those inputs as a real number, salience, which is represented in the map. Of the more than 1 million Google references to salience maps, nearly all use the map for computing the relative priority of visual image components for subsequent processing. We observe that salience processing is an instance of substance-invariant processing, analogous to household measuring cups, weight scales, and measuring tapes, all of which make single-number substance-invariant measurements. Like these devices, the brain also collects material for substance-invariant measurements but by a different mechanism: salience maps that collect visual substances for subsequent measurement. Each salience map can be used by many different measurements. The instruction to attend is implemented by increasing the salience of the to-be-attended items so they can be collected in a salience map and then further processed. Here we show that, beyond processing priority, the following measurement tasks are substance invariant and therefore use salience maps: computing distance in the frontal plane, computing centroids (center of a cluster of items), computing the numerosity of a collection of items, and identifying alphabetic letters. We painstakingly demonstrate that defining items exclusively by color or texture not only is sufficient for these tasks, but that light-dark luminance information significantly improves performance only for letter recognition. Obviously, visual features are represented in the brain but their salience alone is sufficient for these four judgments.