  What spatial frequency information do humans and neural networks use to
recognize objects? In neuroscience, critical band masking is an established
tool that can reveal the frequency-selective filters used for object
recognition. Critical band masking measures the sensitivity of recognition
performance to noise added at each spatial frequency. Existing critical band
masking studies show that humans recognize periodic patterns (gratings) and
letters by means of a spatial-frequency filter (or "channel") that has a
frequency bandwidth of one octave (doubling of frequency). Here, we introduce
critical band masking as a task for network-human comparison and test 14 humans
and 76 neural networks on 16-way ImageNet categorization in the presence of
narrowband noise. We find that humans recognize objects in natural images using
the same one-octave-wide channel that they use for letters and gratings, making
it a canonical feature of human object recognition. Unlike humans, the neural
network channel is very broad, 2-4 times wider than the human channel. Thus,
noise at certain high and low frequencies will impair network performance and
spare human performance. Adversarial and augmented-image training are commonly
used to increase network robustness and shape bias. Does this training align
network and human object recognition channels? Three network channel properties
(bandwidth, center frequency, peak noise sensitivity) correlate strongly with
shape bias (51% variance explained) and robustness of adversarially-trained
networks (66% variance explained). Adversarial training increases robustness
but expands the channel bandwidth even further beyond the human bandwidth.
Thus, critical band masking reveals that the network channel is more than twice
as wide as the human channel, and that adversarial training only makes it
worse. Networks with narrower channels might be more robust.
