  This paper considers neural representation through the lens of active
inference, a normative framework for understanding brain function. It delves
into how living organisms employ generative models to minimize the discrepancy
between predictions and observations (as scored with variational free energy).
The ensuing analysis suggests that the brain learns generative models to
navigate the world adaptively, not (or not solely) to understand it. Different
living organisms may possess an array of generative models, spanning from those
that support action-perception cycles to those that underwrite planning and
imagination; namely, from "explicit" models that entail variables for
predicting concurrent sensations, like objects, faces, or people - to
"action-oriented models" that predict action outcomes. It then elucidates how
generative models and belief dynamics might link to neural representation and
the implications of different types of generative models for understanding an
agent's cognitive capabilities in relation to its ecological niche. The paper
concludes with open questions regarding the evolution of generative models and
the development of advanced cognitive abilities - and the gradual transition
from "pragmatic" to "detached" neural representations. The analysis on offer
foregrounds the diverse roles that generative models play in cognitive
processes and the evolution of neural representation.
