In our new paper, we show that adding self-modeling to artificial networks causes a significant reduction in network complexity
When artificial networks learn to predict their internal states as an auxiliary task, they change in a fundamental way
To better perform the self-model task, the network learns to make itself more simple, regularized, + parameter-efficient, + therefore more amenable to being predictively modeled. We tested our approach in 3 classification tasks across 2 modalities.
In all cases, adding self-modeling significantly reduced network complexity (smaller real log canonical threshold (RLCT) & narrower distribution of weights)
These results show that simply having a network learning to predict itself has strong effects on how it performs a task