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Convolutional neural networks (CNNs) share some similarity in representational structure to the primate ventral visual stream, however less is known about whether low-level visual features are represented in the same way by CNNs and the brain. Here, we focus on orientation perception, a well-understood aspect of the primate visual system. We asked whether convolutional neural networks trained to perform object recognition on a natural image database would exhibit an “oblique effect” such that cardinal (vertical and horizontal) orientations are represented with higher precision than oblique (diagonal) orientations, as has been measured in the primate brain. We obtained activation patterns from two networks (NASnet and Inception-V3) presented with oriented grating stimuli, and used a Euclidean distance metric to measure the discriminability between patterns corresponding to different pairs of orientations. In agreement with human perception, we find that the discriminability of representations generally peaks around the cardinal axes. This finding suggests that cardinality effects in human visual perception are not dependent on a hard-wired anatomical bias, but can instead emerge through experience with the statistics of natural images.

Orientation representations in convolutional neural networks are more discriminable around the cardinal axes