Data from: Testing the equivalency of human “predators” and deep neural networks in the detection of cryptic moths

Researchers have shown growing interest in using deep neural networks
(DNNs) to efficiently test the effects of perceptual processes on the
evolution of color patterns and morphologies. Whether this is a valid
approach remains unclear, as it is unknown whether the relative
detectability of ecologically relevant stimuli to DNNs actually matches
that of biological neural networks. To test this, we compare image
classification performance by humans and six DNNs (AlexNet, VGG-16,
VGG-19, ResNet-18, SqueezeNet, and GoogLeNet) trained to detect artificial
moths on tree trunks. Moths varied in their degree of crypsis, conferred
by different sizes and spatial configurations of transparent wing
elements. Like humans, four of six DNN architectures found moths with
larger transparent elements harder to detect. However, humans and only one
DNN architecture (GoogLeNet) found moths with transparent elements
touching one side of the moth’s outline harder to detect than moths with
untouched outlines. When moths took up a smaller proportion of the image
(i.e., were viewed from further away), the camouflaging effect of
transparent elements touching the moth’s outline was reduced for DNNs but
enhanced for humans. Viewing distance can thus interact with camouflage
type in opposing directions in humans and DNNs, which warrants a deeper
investigation of viewing distance/size interactions with a broader range
of stimuli. Overall, our results suggest that humans and DNN responses had
some similarities, but not enough to justify widespread use of DNNs for
studies of camouflage.