Hierarchical Active Inference: A Theory of Motivated Control

Highlights

Motivated control of action requires the coordination of control and motivational processes in the brain. These have partially orthogonal demands and can be factorized; yet at some point they need to be functionally integrated.

Using active inference, we explain the functional segregation (factorization) and integration of control and motivation.

We propose that control and motivation (implemented mainly in dorsal and ventral neural streams, respectively) conspire to propagate and prioritize goals, respectively, in the service of goal-directed action.

Within active inference, this process appeals to deep goal hierarchies and results in a joint optimization of action sequences (and state transitions) and their precision.

Integrating control and motivation permits to predict future states and infer action sequences or policies, which, ultimately, instigate and motivate behaviour.

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects − goal propagation and prioritization, respectively − of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.