  Throughout the cognitive-science literature, there is widespread agreement
that decision-making agents operating in the real world do so under limited
information-processing capabilities and without access to unbounded cognitive
or computational resources. Prior work has drawn inspiration from this fact and
leveraged an information-theoretic model of such behaviors or policies as
communication channels operating under a bounded rate constraint. Meanwhile, a
parallel line of work also capitalizes on the same principles from
rate-distortion theory to formalize capacity-limited decision making through
the notion of a learning target, which facilitates Bayesian regret bounds for
provably-efficient learning algorithms. In this paper, we aim to elucidate this
latter perspective by presenting a brief survey of these information-theoretic
models of capacity-limited decision making in biological and artificial agents.
