Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization

Recurrent neural networks (RNNs) have been shown to perform better than feedforward architectures in visual object categorization, especially in challenging conditions such as cluttered images. However, little is known about the role recurrent information flow plays in these computations. Here we test an RNN trained for object categorization on the hypothesis that recurrence iteratively aids object categorization via the communication of category-orthogonal auxiliary variables. Using diagnostic linear readouts, we find that: (a) information about auxiliary variables increases across time in all network layers, (b) this information is indeed present in the recurrent information flow, and (c) its manipulation affects task performance. These observations confirm the hypothesis that category-orthogonal auxiliary variable information is conveyed through recurrent connectivity and is used to optimize category judgements in cluttered environments.