  Neural networks struggle in continual learning settings from catastrophic
forgetting: when trials are blocked, new learning can overwrite the learning
from previous blocks. Humans learn effectively in these settings, in some cases
even showing an advantage of blocking, suggesting the brain contains mechanisms
to overcome this problem. Here, we build on previous work and show that neural
networks equipped with a mechanism for cognitive control do not exhibit
catastrophic forgetting when trials are blocked. We further show an advantage
of blocking over interleaving when there is a bias for active maintenance in
the control signal, implying a tradeoff between maintenance and the strength of
control. Analyses of map-like representations learned by the networks provided
additional insights into these mechanisms. Our work highlights the potential of
cognitive control to aid continual learning in neural networks, and offers an
explanation for the advantage of blocking that has been observed in humans.
