  Our goals fundamentally shape how we experience the world. For example, when
we are hungry, we tend to view objects in our environment according to whether
or not they are edible (or tasty). Alternatively, when we are cold, we may view
the very same objects according to their ability to produce heat. Computational
theories of learning in cognitive systems, such as reinforcement learning, use
the notion of "state-representation" to describe how agents decide which
features of their environment are behaviorally-relevant and which can be
ignored. However, these approaches typically assume "ground-truth" state
representations that are known by the agent, and reward functions that need to
be learned. Here we suggest an alternative approach in which
state-representations are not assumed veridical, or even pre-defined, but
rather emerge from the agent's goals through interaction with its environment.
We illustrate this novel perspective by inferring the goals driving rat
behavior in an odor-guided choice task and discuss its implications for
developing, from first principles, an information-theoretic account of
goal-directed state representation learning and behavior.
