Improved prediction of behavioral and neural similarity spaces using pruned DNNs.

Deep Neural Networks (DNNs) have become an important tool for modeling brain and behavior. One key area of interest has been to apply these networks to model human similarity judgements. Several previous works have used the embeddings from the penultimate layer of vision DNNs and showed that a reweighting of these features improves the fit between human similarity judgments and DNNs. These studies underline the idea that these embeddings form a good basis set but lack the correct level of salience. Here we re-examined the grounds for this idea and on the contrary, we hypothesized that these embeddings, beyond forming a good basis set, also have the correct level of salience to account for similarity judgments. It is just that the huge dimensional embedding needs to be pruned to select those features relevant for the considered domain for which a similarity space is modeled. In Study 1 we supervised DNN pruning based on a subset of human similarity judgments. We found that pruning: i) improved out-of-sample prediction of human similarity judgments from DNN embeddings, ii) produced better alignment with WordNet hierarchy, and iii) retained much higher classification accuracy than reweighting. Study 2 showed that pruning by neurobiological data is highly effective in improving out-of-sample prediction of brain-derived representational dissimilarity matrices from DNN embeddings, at times fleshing out isomorphisms not otherwise observable. Using pruned DNNs, image-level heatmaps can be produced to identify image sections whose features load on dimensions coded by a brain area. Pruning supervised by human brain/behavior therefore effectively identifies alignable dimensions of knowledge between DNNs and humans and constitutes an effective method for understanding the organization of knowledge in neural networks.