  Recently, Zhang et al. (2018) proposed an interesting model of attention
guidance that uses visual features learnt by convolutional neural networks for
object recognition. I adapted this model for search experiments with accuracy
as the measure of performance. Simulation of our previously published feature
and conjunction search experiments revealed that CNN-based search model
considerably underestimates human attention guidance by simple visual features.
A simple explanation is that the model has no bottom-up guidance of attention.
Another view might be that standard CNNs do not learn features required for
human-like attention guidance.
