General Object Constancy mechanism Let
s be the retinal separation and
δ the binocular disparity between two points in space. Corresponding perceptual measures are given by the General Object Constancy model as follows. Brain first scales
s by a dimensionless factor
k. k is a function of the relative depth
d/
d0, where
d0 stands for the reference viewing distance, e.g., the distance wherefrom the perceived motion in depth started in our optic flow paradigm. Based on our previous experiments (Qian & Petrov,
2012), function
k(
d/
d0) is approximately linear for small motion amplitude factors. This formulation is in agreement with the retinal size decreasing as a linear function of the viewing distance
d. Correspondingly,
δ is scaled by the square of
k, because binocular disparity decreases as a square of the viewing distance, and therefore requires the squared factor
k to keep its percept invariant to the viewing distance:
where
S and
D stand for the perceived size and depth respectively. In addition, Experiments 1 and 2 demonstrate that increasing perceived size (the size illusion) makes the perceived depth gradient illusion stronger. This is accounted by adding a factor
k′ to the depth equation:
where
S(
d0) is the perceived size at the starting viewing distance
d0, and
S(
d) is the perceived size at the current viewing distance
d. In other words, the perceived depth is additionally scaled by the relative perceived size:
S(
d)/
S(
d0). This model is illustrated in
Figure 5. Without a loss of generality we can assign
k(1) = 1 and therefore
S(
d0) =
s(
d0) and
D(
d0) =
δ(
d0). If the retinal size
s remains constant (Experiment 2, size illusion), we obtain the illusion of the perceived size
S as
Because the perceived depth gradient (pencil's sharpness) is defined as the length of the pencil tip (encoded as its perceived disparity) over its perceived size,
D/
S, the depth gradient,
DG, is given by
Hence, we obtain for the strength of the depth gradient illusion in Experiment 1:
and, therefore,
This relationship is plotted by the red curve in
Figure 3. The red curve does not pass through all the data points, but given the large error bars, it is unclear whether the model needs revision. Since the prediction is parameter free, any revision would have to be principled, rather than just by adjusting parameters. If the perceived size
S remain constant (Experiment 2, depth gradient illusion), we obtain for the depth gradient illusion,
Therefore,
This relationship shown by the black curve in
Figure 3 fits the data very well given that the relationship is parameter free.