Cognitive Control as Precision Regulation: From Predictive Coding to Adaptive Behaviou
Abstract
Cognitive control is traditionally conceptualized as a set of top-down mechanisms that select and regulate stimulus–response mappings to support goal-directed and flexible behaviour. Traditional accounts have provided fundamental insights into the neural architecture of cognitive control and identified a set of core control processes, including goal maintenance, task switching, control allocation, conflict monitoring, and cognitive effort. However, they rarely address how these functions can be integrated within a common computational architecture. Furthermore, most theories tend to assume that the core control functions use specialized mechanisms that are fundamentally distinct from those supporting action–perception loops. Here, we review an alternative perspective grounded in predictive coding and active inference, according to which cognitive control emerges from hierarchical elaborations of the same generative inference supporting perception and action. Within predictive coding and active inference, the brain continuously generates predictions about sensory inputs, actions, and their expected consequences, and updates these predictions on the basis of precision-weighted prediction errors. We argue that the same precision-weighting mechanism, when operating beyond sensory processing—to optimize the precision of higher-level beliefs and of courses of action (policies)—provides the foundations of cognitive control, allowing a cognitive system to balance the stability of current goals and task representations against the flexibility required to respond to unexpected information and environmental changes. From this perspective, the core control processes emerge as different facets of precision regulation within a hierarchical generative model: the precision of higher-level beliefs supports goal maintenance and task switching; policy precision supports control allocation, setting the balance between habitual and goal-directed control and prioritizing the latter when required by task demands; conflict monitoring provides information about whether the selected policy continues to produce expected and preferred outcomes; and cognitive effort reflects the cost of increasing precision to change the resulting control configuration. Finally, we link the precision-based computations to putative neural processes in frontoparietal networks and neuromodulatory processes spanning dopaminergic, noradrenergic, and cholinergic systems, and derive empirical predictions that can test the proposed framework.
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BibTeX
@misc{giraldi2026cognitive,
title = {{Cognitive Control as Precision Regulation: From Predictive Coding to Adaptive Behaviou}},
author = {Enrico Giraldi and Riccardo Proietti and Giovanni Pezzulo},
howpublished = {PsyArXiv},
year = {2026},
month = nov,
doi = {10.31234/osf.io/b8vpm_v1},
url = {https://osf.io/preprints/psyarxiv/b8vpm_v1/},
note = {CC0},
}
RIS
TY - UNPB
TI - Cognitive Control as Precision Regulation: From Predictive Coding to Adaptive Behaviou
AU - Enrico Giraldi
AU - Riccardo Proietti
AU - Giovanni Pezzulo
PY - 2026
DA - 2026/11/03
PB - PsyArXiv
DO - 10.31234/osf.io/b8vpm_v1
UR - https://osf.io/preprints/psyarxiv/b8vpm_v1/
AB - Cognitive control is traditionally conceptualized as a set of top-down mechanisms that select and regulate stimulus–response mappings to support goal-directed and flexible behaviour. Traditional accounts have provided fundamental insights into the neural architecture of cognitive control and identified a set of core control processes, including goal maintenance, task switching, control allocation, conflict monitoring, and cognitive effort. However, they rarely address how these functions can be integrated within a common computational architecture. Furthermore, most theories tend to assume that the core control functions use specialized mechanisms that are fundamentally distinct from those supporting action–perception loops. Here, we review an alternative perspective grounded in predictive coding and active inference, according to which cognitive control emerges from hierarchical elaborations of the same generative inference supporting perception and action. Within predictive coding and active inference, the brain continuously generates predictions about sensory inputs, actions, and their expected consequences, and updates these predictions on the basis of precision-weighted prediction errors. We argue that the same precision-weighting mechanism, when operating beyond sensory processing—to optimize the precision of higher-level beliefs and of courses of action (policies)—provides the foundations of cognitive control, allowing a cognitive system to balance the stability of current goals and task representations against the flexibility required to respond to unexpected information and environmental changes. From this perspective, the core control processes emerge as different facets of precision regulation within a hierarchical generative model: the precision of higher-level beliefs supports goal maintenance and task switching; policy precision supports control allocation, setting the balance between habitual and goal-directed control and prioritizing the latter when required by task demands; conflict monitoring provides information about whether the selected policy continues to produce expected and preferred outcomes; and cognitive effort reflects the cost of increasing precision to change the resulting control configuration. Finally, we link the precision-based computations to putative neural processes in frontoparietal networks and neuromodulatory processes spanning dopaminergic, noradrenergic, and cholinergic systems, and derive empirical predictions that can test the proposed framework.
ER -