  Humans can learn several tasks in succession with minimal mutual interference
but perform more poorly when trained on multiple tasks at once. The opposite is
true for standard deep neural networks. Here, we propose novel computational
constraints for artificial neural networks, inspired by earlier work on gating
in the primate prefrontal cortex, that capture the cost of interleaved training
and allow the network to learn two tasks in sequence without forgetting. We
augment standard stochastic gradient descent with two algorithmic motifs,
so-called "sluggish" task units and a Hebbian training step that strengthens
connections between task units and hidden units that encode task-relevant
information. We found that the "sluggish" units introduce a switch-cost during
training, which biases representations under interleaved training towards a
joint representation that ignores the contextual cue, while the Hebbian step
promotes the formation of a gating scheme from task units to the hidden layer
that produces orthogonal representations which are perfectly guarded against
interference. Validating the model on previously published human behavioural
data revealed that it matches performance of participants who had been trained
on blocked or interleaved curricula, and that these performance differences
were driven by misestimation of the true category boundary.
