The information-processing perspective on representation

I introduce a novel framework for theorizing about representations in cognitive science, which relies on two theses. First, representations are, primarily, signals for information transmission, not as a side effect of other functions these signals may have, but for its own sake. Second, these signals aim at efficiently trading-off three cognitive budgets: rate (or transmission and storage costs), distortion (or faithfulness of the transmitted information), and computational complexity of coders. The way to provide empirical evidence that some entity is a representation—and hence that it is involved in information transmission for the sake of information transmission, as per the first thesis—is to show that it present adaptations for efficient information transmission—as per the second thesis. The kinds of properties that cognitive scientists routinely associate with paradigmatic instances of representations are generated by adaptations for rate-distortion-complexity efficiency.