  Humans learn quickly even in tasks that contain complex visual information.
This is due in part to the efficient formation of compressed representations of
visual information, allowing for better generalization and robustness. However,
compressed representations alone are insufficient for explaining the high speed
of human learning. Reinforcement learning (RL) models that seek to replicate
this impressive efficiency may do so through the use of factored
representations of tasks. These informationally simplistic representations of
tasks are similarly motivated as the use of compressed representations of
visual information. Recent studies have connected biological visual perception
to disentangled and compressed representations. This raises the question of how
humans learn to efficiently represent visual information in a manner useful for
learning tasks. In this paper we present a model of human factored
representation learning based on an altered form of a $\beta$-Variational
Auto-encoder used in a visual learning task. Modelling results demonstrate a
trade-off in the informational complexity of model latent dimension spaces,
between the speed of learning and the accuracy of reconstructions.
