  Animals (especially humans) have an amazing ability to learn new tasks
quickly, and switch between them flexibly. How brains support this ability is
largely unknown, both neuroscientifically and algorithmically. One reasonable
supposition is that modules drawing on an underlying general-purpose sensory
representation are dynamically allocated on a per-task basis. Recent results
from neuroscience and artificial intelligence suggest the role of the general
purpose visual representation may be played by a deep convolutional neural
network, and give some clues how task modules based on such a representation
might be discovered and constructed. In this work, we investigate module
architectures in an embodied two-dimensional touchscreen environment, in which
an agent's learning must occur via interactions with an environment that emits
images and rewards, and accepts touches as input. This environment is designed
to capture the physical structure of the task environments that are commonly
deployed in visual neuroscience and psychophysics. We show that in this
context, very simple changes in the nonlinear activations used by such a module
can significantly influence how fast it is at learning visual tasks and how
suitable it is for switching to new tasks.
