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Any information processing system should allocate resources where it matters: it should process frequent variable values with higher accuracy than less frequent ones. While this strategy minimizes average error, it also introduces an estimation bias. For example, human subjects perceive local visual orientation with a bias away from the orientations that occur most frequently in the natural world. Here, using an information theoretic measure, we show that pretrained neural networks, like humans, have internal representations that overrepresent frequent variable values at the expense of certainty for less common values. Furthermore, we demonstrate that optimized readouts of local visual orientation from these networks’ internal representations show similar orientation biases and geometric illusions as human subjects. This surprising similarity illustrates that when performing the same perceptual task, similar characteristic illusions and biases emerge for any optimal information processing system that is resource limited.

Shared visual illusions between humans and artificial neural networks