The representational dynamics of the animal appearance bias in human visual cortex are indicative of fast feedforward processing

The human visual system has a seemingly unique tendency to interpret zoomorphic objects as animals, not as objects. This animal appearance bias is very strong in the ventral visual pathway as measured through functional magnetic resonance imaging (fMRI), but it is absent in feedforward deep convolutional neural networks. Here we investigate how this bias emerges over time by probing its representational dynamics through multivariate electroencephalography (EEG). The initially activated representations to lookalike zoomorphic objects are very similar to the representations activated by animal pictures and very different from the neural responses to regular objects. Neural responses that reflect the true identity of the zoomorphic objects as inanimate objects are weaker and appear later, as do effects of task context. The strong early emergence of an animal appearance bias strongly supports a feedforward explanation, indicating that lack of recurrence in deep neural networks is not an explanation for their failure to show this bias.