Topic: rule representations

In the human brain, some individual neurons respond selectively to abstract variables, invariant to sensory grounding (Mansouri, Freedman, & Buckley, 2020). Similarly tuned units also appear in artificial networks trained on cognitive tasks (Goh et al., 2021). It is often implicitly assumed that the emergence of such interpretable neurons plays a key role in the behavior of the trained network. Here we show that this is not necessarily the case. We train a biologically inspired artificial agent comprising two key components—a recurrent network and an associative memory—on a canonical rule