Proceedings of the Annual Meeting of the Cognitive Science Society

The amount of control perceived by an agent governs their ability to learn. Bounded rationality, or the idea that weare limited by the amount cognitive work we can perform, provides an appealing framework within which perceived controlcould be formulated. When modeling the world, the bounded-rational agent balances the trade-off between the utility andcomplexity of this constructed model in order to choose an optimal policy. Here, we present a novel formulation of behavioralcontrol, bounded inference, which explicitly models control as the perceived constraint experienced by an agent during theinference process, employing a version of the free energy functional with an additional boundedness parameter as the variationalprinciple of this constrained optimization. The utility of bounded inference is demonstrated in simulations that capture variouscharacteristics of dysfunctional behavioral patterns as observed in a range of psychiatric disorders for which control beliefsplay a central role.