  We show that classical hue cancellation experiments lead to human-like
opponent curves even if the task is done by trivial (identity) artificial
networks. Specifically, human-like opponent spectral sensitivities always
emerge in artificial networks as long as (i) the retina converts the input
radiation into any tristimulus-like representation, and (ii) the post-retinal
network solves the standard hue cancellation task, e.g. the network looks for
the weights of the cancelling lights so that every monochromatic stimulus plus
the weighted cancelling lights match a grey reference in the (arbitrary) color
representation used by the network. In fact, the specific cancellation lights
(and not the network architecture) are key to obtain human-like curves: results
show that the classical choice of the lights is the one that leads to the best
(more human-like) result, and any other choices lead to progressively different
spectral sensitivities. We show this in two ways: through artificial
psychophysics using a range of networks with different architectures and a
range of cancellation lights, and through a change-of-basis theoretical analogy
of the experiments. This suggests that the opponent curves of the classical
experiment are just a by-product of the front-end photoreceptors and of a very
specific experimental choice but they do not inform about the downstream color
representation. In fact, the architecture of the post-retinal network (signal
recombination or internal color space) seems irrelevant for the emergence of
the curves in the classical experiment. This result in artificial networks
questions the conventional interpretation of the classical result in humans by
Jameson and Hurvich.
