Weber–Fechner law

W/F is cool. But it's not the whole story. The dipper function (https://link.springer.com/article/10.3758/APP.71.3.435) describes a deviation from W/F behaviour that seems to represent a direct link between population neuronal activity and perception : basically, neurons respond in a sigmoidal way to increasing input and hitting the steep part of the sigmoid reduces your discrimination threshold. After a while you are past that steep bit and back to the boring old W/F regime.

> An illustration of the Weber–Fechner law. On each side, the lower square contains 10 more dots than the upper one. However the perception is different: On the left side [10 vs 20], the difference between upper and lower square is clearly visible. On the right side [110 vs 120], the two squares look almost the same.

This is exactly what one would expect, since you are adding a smaller fraction of the total population in the second case (i.e. 110/120 > 10/20). If you kept the ratio equal in both cases, then the RHS would also look very different. This would work, up until the point where the dots begin to saturate the medium.

It's all logarithmic. The most common application of this law in the software industry is the volume control.