Reverse engineering the centered self

In certain problem-solving contexts, people organize their domain through treating themselves as the perceptual and cognitive center of their world. They identify and solve a particular problem from their perspective as a particular agent, with a particular location, at a particular time, in a particular environment. When they do this, they engage in a distinctive kind of agent-centered problem-solving. For many contexts involving intelligent agents, partially observable Markov decision processes (POMDPs), a framework for modeling decision-making in uncertain environments unfolding over time, have effectively become a "standard model." Yet, as these models are ordinarily interpreted, they do not explicitly represent agent-centered problem-solving. Accordingly, to model this type of problem-solving, we begin by extending the standard POMDP framework to define "ePOMDPs." This formalism models how an agent, once it centers itself on a particular self-and-world representation, plans and acts rationally from its own perspective. To capture the way that such agents choose which problem to solve, we build on our ePOMDPs to develop a "meta-ePOMDP" agent within a hierarchical Bayesian framework. We implement meta-ePOMDP agents for two different suites of "centering game" tasks that highlight different aspects of our theory. We find that our models explain signatures of agent-centered problem-solving not captured by alternative models, in particular, the difficulty of navigating spaces of possible problem representations. We close by suggesting that our model could provide the beginnings of a computational framework for how a person could have a self. (PsycInfo Database Record (c) 2026 APA, all rights reserved).