In the literature on enactive approaches to cognition, representationalism is often seen as a rival theory. In this paper, I argue that enactivism can be fruitfully combined with representationalism by adopting Frances Egan’s content pragmatism. This representational enactivism avoids some of the problems faced by anti-representational versions of enactivism. Most significantly, representational enactivism accommodates empirical evidence that neural systems manipulate representations. In addition, representational enactivism provides a valuable insight into how to identify representational content, especially in brainless organisms: we can identify representational content by investigating autopoietic processes.

Bayesian approaches to human cognition have been extensively advocated in the last decades, but sharp objections have been raised too. We outline a diagnosis of what has gone wrong with prevalent strands of Bayesian cognitive science (pure Bayesian cognitive science), relying on selected illustrations from the psychology of reasoning and tools from the philosophy of science. Bayesians’ reliance on so-called method of rational analysis is a key point of our discussion. We tentatively conclude on a constructive note, though: an appropriately modified variant of Bayesian cognitive science can still be coherently pursued.

“According to [Bayesian] models” says a recent textbook in cognitive neuroscience, “the human mind behaves like a capable data scientist”. Do they? That is to say, do such models show we are rational? I argue that Bayesian models of cognition, perhaps surprisingly, do not and indeed cannot, show that we are Bayesian-rational. The key reason is that they appeal to approximations, a fact that carries significant implications. After outlining the argument, I critique two responses, seen in recent cognitive neuroscience. One says that the mind can be seen as approximately Bayes-rational, while the other reconceives norms of rationality.