  Three decades ago, Atick et al. suggested that human frequency sensitivity
may emerge from the enhancement required for a more efficient analysis of
retinal images. Here we reassess the relevance of low-level vision tasks in the
explanation of the Contrast Sensitivity Functions (CSFs) in light of (1) the
current trend of using artificial neural networks for studying vision, and (2)
the current knowledge of retinal image representations.
  As a first contribution, we show that a very popular type of convolutional
neural networks (CNNs), called autoencoders, may develop human-like CSFs in the
spatio-temporal and chromatic dimensions when trained to perform some basic
low-level vision tasks (like retinal noise and optical blur removal), but not
others (like chromatic adaptation or pure reconstruction after simple
bottlenecks). As an illustrative example, the best CNN (in the considered set
of simple architectures for enhancement of the retinal signal) reproduces the
CSFs with an RMSE error of 11\% of the maximum sensitivity.
  As a second contribution, we provide experimental evidence of the fact that,
for some functional goals (at low abstraction level), deeper CNNs that are
better in reaching the quantitative goal are actually worse in replicating
human-like phenomena (such as the CSFs). This low-level result (for the
explored networks) is not necessarily in contradiction with other works that
report advantages of deeper nets in modeling higher-level vision goals.
However, in line with a growing body of literature, our results suggests
another word of caution about CNNs in vision science since the use of
simplified units or unrealistic architectures in goal optimization may be a
limitation for the modeling and understanding of human vision.
