  Models based on the Transformer neural network architecture have seen success
on a wide variety of tasks that appear to require complex "cognitive branching"
-- or the ability to maintain pursuit of one goal while accomplishing others.
In cognitive neuroscience, success on such tasks is thought to rely on
sophisticated frontostriatal mechanisms for selective \textit{gating}, which
enable role-addressable updating -- and later readout -- of information to and
from distinct "addresses" of memory, in the form of clusters of neurons.
However, Transformer models have no such mechanisms intentionally built-in. It
is thus an open question how Transformers solve such tasks, and whether the
mechanisms that emerge to help them to do so bear any resemblance to the gating
mechanisms in the human brain. In this work, we analyze the mechanisms that
emerge within a vanilla attention-only Transformer trained on a simple sequence
modeling task inspired by a task explicitly designed to study working memory
gating in computational cognitive neuroscience. We find that, as a result of
training, the self-attention mechanism within the Transformer specializes in a
way that mirrors the input and output gating mechanisms which were explicitly
incorporated into earlier, more biologically-inspired architectures. These
results suggest opportunities for future research on computational similarities
between modern AI architectures and models of the human brain.
