Introduction: Effective cognitive control, encompassing functions from the suppression of unwanted memories to adaptive learning from delayed outcomes, is fundamental for goal-directed behavior. While brain regions like the prefrontal cortex (PFC) are known to be critical, the precise network-level mechanisms and computational principles that enable robust control to remain under investigation. A key emerging question is whether successful cognition relies on the sheer magnitude of neural signaling or the precision of the underlying neural representations that encode states and actions. Deficits in these control mechanisms are central to numerous psychiatric and neurological disorders. This review synthesizes recent neuroimaging and neuropsychological findings to delineate the contributions of cerebro-cerebellar and prefrontal-hippocampal circuits to cognitive control, focusing on how factors like sleep, network segregation, and the fidelity of neural information govern performance.
Methods: A systematic literature search was conducted in the eLife, PNAS, and Frontiers databases for original research articles published in English between July 2023 and July 2025. Search terms included default mode network, memory suppression, sleep deprivation, and cerebellum. After excluding case studies, four articles were selected for the final synthesis. The selected studies utilized advanced neuroimaging and computational modeling to investigate cognitive control. A unifying methodological feature was the use of multivariate analytical techniques, like representational similarity analysis, to assess the fidelity of neural information within key cognitive circuits during memory and decision-making tasks.
Results: The synthesis of findings reveals three core principles of cognitive control circuitry. First, the PFC acts as a central hub, but its function depends on targeted interactions with other key regions. One night of sleep deprivation impairs memory suppression by reducing right dorsolateral PFC (rDLPFC) activation and its inhibitory control over the hippocampus. Concurrently, the cerebellum, via extensive cortico-ponto-cerebellar loops, collaborates with the PFC to support a wide range of executive functions, attention, and working memory.
Second, a key convergent finding refines classic learning models, demonstrating that the fidelity of neural representations, rather than the magnitude of prediction error signals, is the primary driver of precise learning. In both social and non-social learning, the distinctiveness of stimulus representations in the medial and lateral orbitofrontal cortex (mPFC, lOFC) predicts an individual's ability to assign credit accurately. This principle extends to complex scenarios with delayed outcomes, where the fidelity of reinstated choice information in the lOFC and hippocampus determines learning success. Notably, several studies confirmed that this relationship holds even after accounting for the strength of canonical learning signals in the striatum or vmPFC.
Third, the brain employs dynamic, context-dependent network strategies. When outcomes are delayed by intervening decisions, the lateral frontopolar cortex (lFPC) maintains causal choice information in a pending state, facilitating its later reinstatement in the lOFC-hippocampal circuit. The efficacy of these networks is highly dependent on physiological state; REM sleep, for instance, is specifically correlated with the restoration of rDLPFC activity for memory control and the adaptive segregation of large-scale brain networks (e.g., DMN, CCN). No direct contradictions were found; instead, the findings are complementary, detailing mechanisms at different scales, from physiological modulation to specific computational functions.
Conclusion: The collective evidence indicates that effective cognitive control arises not from isolated brain regions but from dynamic, context-sensitive interactions within large-scale neural circuits, particularly those linking the prefrontal cortex, hippocampus, and cerebellum. A unifying principle is the importance of high-fidelity neural representations, which enable precise linking of states, actions, and outcomes over time. This perspective shifts focus from merely identifying active brain areas to understanding the computational quality of neural information and integrity of network communication.
These findings have significant clinical implications, suggesting that cognitive deficits in disorders characterized by intrusive thoughts (e.g., PTSD) or poor decision-making may stem from degraded representational fidelity or network dysregulation. The impact of a night of sleep deprivation on memory control circuits provides a model for this vulnerability. Future research should bridge these findings by investigating how neuromodulatory states like sleep deprivation impact representational fidelity underlying credit assignment, and whether cerebellar-focused interventions can enhance PFC-hippocampal function. Combining high-temporal-resolution methods (e.g., EEG) with fMRI and sophisticated computational modeling will be crucial to fully mapping the spatiotemporal dynamics of adaptive human cognition.