PT  - JOURNAL ARTICLE
AU  - Guillermo Puebla
AU  - Jeffrey S. Bowers
TI  - Can Deep Convolutional Neural Networks Learn Same-Different Relations?
AID  - 10.1101/2021.04.06.438551
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.04.06.438551
4099  - http://biorxiv.org/content/early/2021/04/06/2021.04.06.438551.short
4100  - http://biorxiv.org/content/early/2021/04/06/2021.04.06.438551.full
AB  - Same-different visual reasoning is a basic skill central to abstract combinatorial thought. This fact has lead neural networks researchers to test same-different classification on deep convolutional neural networks (DCNNs), which has resulted in a controversy regarding whether this skill is within the capacity of these models. However, most tests of same-different classification rely on testing on images that come from the same pixel-level distribution as the testing images, yielding the results inconclusive. In this study we tested relational same-different reasoning DCNNs. In a series of simulations we show that DCNNs are capable of visual same-different classification, but only when the test images are similar to the training images at the pixel-level. In contrast, even when there are only subtle differences between the testing and training images, the performance of DCNNs drops to chance levels. This is true even when DCNNs’ training regime is augmented with images from new versions of the same-different task or through multi-task learning on the test images.Competing Interest StatementThe authors have declared no competing interest.