  Computational descriptions of purposeful behavior comprise both descriptive
and normative} aspects. The former are used to ascertain current (or future)
states of the world and the latter to evaluate the desirability, or lack
thereof, of these states under some goal. In Reinforcement Learning, the
normative aspect (reward and value functions) is assumed to depend on a
predefined and fixed descriptive one (state representation). Alternatively,
these two aspects may emerge interdependently: goals can be, and indeed often
are, approximated by state-dependent reward functions, but they may also shape
the acquired state representations themselves. Here, we present a novel
computational framework for state representation learning in bounded agents,
where descriptive and normative aspects are coupled through the notion of
goal-directed, or telic, states. We introduce the concept of telic
controllability to characterize the tradeoff between the granularity of a telic
state representation and the policy complexity required to reach all telic
states. We propose an algorithm for learning controllable state
representations, illustrating it using a simple navigation task with shifting
goals. Our framework highlights the crucial role of deliberate ignorance --
knowing which features of experience to ignore -- for learning state
representations that balance goal flexibility and policy complexity. More
broadly, our work advances a unified theoretical perspective on goal-directed
state representation learning in natural and artificial agents.
