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This paper challenges the dominant hierarchical control model in neuroscience, arguing instead for a heterarchical architecture where control is relational and process-specific, not a fixed chain of command. Evidence from learned action and various neural loops demonstrates that control dynamics shift based on context, practice, and task demands, resisting stable ranking. The authors propose a spectrum of control, indexed by dynamical decoupling, to characterize the brain's repertoire of control configurations across different occasions.
Brain control isn't a hierarchy, but a dynamic spectrum of influence shaped by context and practice, challenging decades of neuroscience dogma.
Neuroscience treats the brain as a control hierarchy, with cortical systems governing subcortical mechanisms. We argue that this mistakes a configuration for an architecture. Local asymmetries of influence are real, but control is relational and process-specific; because neural elements constrain one another across concurrent processes, relations can form cycles that resist a stable ranking. Evidence from learned action, cortico-cerebellar and thalamocortical loops, and defensive behavior shows that control changes with practice, task phase, urgency, viability demands, and affordances. Hierarchical order therefore characterizes time-bounded configurations, whereas heterarchy characterizes the architecture's repertoire across occasions. We propose a spectrum of control indexed by dynamical decoupling: how far behavior can depend on history, internal state, and anticipated consequences. The account yields experimentally testable predictions.