# A continuum of predictive control between motor and mental actions: language production as a test case

Elin Runnqvist, Christian A Kell

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## To cite this version:

Elin Runnqvist, Christian A Kell. A continuum of predictive control between motor and mental actions: language production as a test case. Current Opinion in Behavioral Sciences, 2025, 65, pp.101573. 10.1016/j.cobeha.2025.101573. hal-05235025

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Review

# A continuum of predictive control between motor and mental actions: language production as a test case

Elin Runnqvist<fnref n="1" /> and Christian A Kell<fnref n="2" />

<figure><img src="image_4.png" /></figure>

Research traditions have studied aspects of control from their respective perspectives, separating motor and cognitive actions. This becomes particularly evident in the field of language and speech motor control in which linguistic language production models leave sensorimotor aspects underspecified and speech production models largely ignore the linguistic processing that provides a speech motor goal in the first place. It would be surprising if the brain organized control of speaking in such a dichotomous way. Here, we review and discuss conceptual proposals and empirical evidence supporting a hierarchical control architecture that allows for an embedded control of both abstract language as well as concrete speech. We propose a control scheme that could explain inner speech, speech planning, speech production, and speech perception in a single model. We propose concrete study designs that may disprove our proposal.

## Addresses

Corresponding author: Kell, Christian A (c.kell@em.uni-frankfurt.de)

**Current Opinion in Behavioral Sciences 2025, 65:101573**

This review comes from a themed issue on **Control Theory**

Edited by **Henry Yin** and **Warren Mansell**

Available online xxxx

Received: 24 March 2025; Revised: 8 June 2025;

Accepted: 20 June 2025

https://doi.org/10.1016/j.cobeha.2025.101573

2352-1546/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Humans excel at controlling actions even when sensory feedback is reduced, for example, when walking rapidly down the stairs or conversing in noisy environments. This ability is thought to rely on predictive control, including internal modeling and active inference, in which the brain generates predictions about sensory outcomes

to guide action execution. Top-down predictions attenuate expected sensory responses (reafference cancellation). Mismatch between predictions and actual sensory feedback will increase sensory responses [1], providing an error signal that can be used for control purposes and for updating top-down predictions [2]. Continually refining predictions through this comparison process, the brain minimizes discrepancies between expected and actual outcomes, known as prediction errors [3–5], optimizing performance [6,7]. Top-down predictions guiding sensory-motor behaviors has long been central to motor control theories [3] (see also Helmholtz’s principles of perception and Karl Friston’s active inference theory [AIF]), establishing that predictions serve the purpose of state estimation, sensory filtering, and context estimation [7]. Besides these probabilistic proposals, an alternative explanatory framework views actions as resulting from the control of agents’ subjective perceptual experiences [8]. Perceptual control theory (PCT) thus provides an ideal framework to explain both covert and overt actions.

Theoretical proposals extending the control of movement to cognition have been proposed, emphasizing shared computational, neurobiological, and evolutionary principles [6,7,9–15]. In this article, we will first review evidence that the brain uses a shared mechanism, scaling with degree of abstraction, to control motor and mental actions alike. Next, we will consider how language production, a skill involving both motor and cognitive control, could be used as a test case for assessing such a continuous predictive control scheme.

## Evidence for a hierarchically organized continuum from motor control to abstract reasoning

If comparable predictive control mechanisms exist for movement and cognition [15], evolutionary and neurobiological evidence should support a gradual, hierarchical organization. Traditionally, one approach to account for the emergence of a novel, complex behavior such as the control of mental actions is linking the emergence of a new function with that of a new neuroanatomical structure. Novel complex behaviors and their neurobiological bases have sometimes been argued to emerge abruptly, through short bursts of significant change followed by long periods of stability [16]. Contrary to this, and in line with an incremental evolution, modern human brain size evolution was rather gradual

<footnote n="1">¹Aix-Marseille Université, CNRS, LPL, Laboratoire Parole et Langage, 5 Avenue Pasteur, Aix-en-Provence 13100, France</footnote>

<footnote n="2">²Cooperative Brain Imaging Center, Goethe University, Heinrich-Hoffmann-Strasse 9, 60528 Frankfurt, Germany</footnote>
[17], speaking for a hierarchically organized neural structure hosting a continuum of structure–function relationships.

Phylogenetically, brain pathways have been proposed to duplicate, conserving properties of the original pathway, subsequently diverging to accommodate a novel function through specialization of genes involved in neural connectivity [18]. Duplication and divergence could represent a general principle by which the addition of new hierarchical layers grants additional degrees of freedom allowing for more abstract processing [10]. This would result in small step-wise functional additions with each additional structural layer, that is, a gradient. In line with this, microstructural properties, connectivity patterns, and gene expression suggest that human brain areas are organized in gradients ranging from sensorimotor (unimodal) areas to areas responding to multiple modalities (transmodal areas) [19–23]. This arrangement of brain areas could provide a structural scaffold, allowing for global functional processing hierarchies to emerge [19]. In the case of control, a continuous hierarchical organization of brain function is empirically supported as control-related hierarchies spanning multiple adjacent cortical or subcortical areas have been reported throughout the brain [24–29]. Studies varying the degree of abstractness in cognitive control, motor actions, or prediction errors observed anatomical processing gradients in the frontoparietal cortex and in the cerebellum [24–29].

This action control hierarchy might entail different temporal scales associated with lower to higher levels of prediction. Cortical and subcortical regions distant from sensory input (i.e. the transmodal parts of anatomical gradients) display the longest temporal receptive windows [30], indicating a correspondence between processing level (lower to higher), temporal scale (shorter to longer), and anatomical location (evolutionary older to newer). Cognitive performance indeed emerges from low-dimensional trajectories through a functional manifold, where more integrative brain states are associated with longer temporal scales and distributed, transmodal networks [31]. Moreover, the temporal scale of predictions during language comprehension is shorter for shallow syntactic information and longer for contextual, high-level, semantic information and increasing temporal scale is associated with increasingly associative cortical regions [32].

A compelling example of how temporal and spatial hierarchies relate can be found in the human cerebellum, often linked to processing of predictions and prediction errors [6,10,33]. The cerebellum's ability of handling different types of predictions and/or prediction error has been linked to zebrin-positive or zebrin-negative populations of purkinje cells that fire at different rates: zebrin-positive purkinje cells fire at lower rates

than zebrin-negative purkinje cells [34]. Such zebrin-positive and zebrin-negative cells are distributed along an anterior-posterior axis, with more zebrin-positive populations in the posterior cerebellum [35], coinciding with the reported cerebellar processing hierarchy going from sensorimotor in anterior parts to associative in posterior parts [20,29] also expressed through distinct spatiomolecular profiles [36].

Thus, hierarchical duplication and specialization likely allowed the brain to handle increasingly abstract forms of control by predicting the consequences of actions and comparing these with the actual action outcomes. This raises the question of how the brain generates and refines predictions without sensory feedback. Motor learning can occur by imagining practice [37]. To account for error detection and learning conceived as interactions without sensory feedback, one needs to assume some form of internalization of perceptual experience and that internal models can be modified by both bottom-up and top-down signals independently [38]. A link between motor control and high-level cognitive functions was proposed through mental simulation, allowing for forward model tuning through mental practice [7]. Contexts of uncertainty, for example, when learning a novel action or when feedback is absent, might entail running multiple internal models in parallel based on experience with other actions, providing probabilistic estimates of success, much like a prediction error. Other proposals include abstract amodal, in addition to more concrete unimodal, emulators of perceptual states to account for covert actions or imagery in the absence of sensory input [9].

Empirically, covert action imitation used for forward modeling of actions is supported by studies showing motor responses in observers occur before an action is perceived or in agents before movement is executed. Besides engaging the motor cortex [39], covert actions involve activation of cortices representing the sensory consequences of actions [9, 12, 13, 40–43]. Simulating actions either consciously or subconsciously may assist in perception, especially in noisy conditions [44–46]. Moreover, predictive effects in comprehension are impaired when the speech production system is taxed [47–49].

The question arises which advantage would arise from such mental simulations. Tuning of the cortical control architecture could accumulate knowledge on internal noise distributions, thereby improving future performance. This noise is often nonsymmetrically distributed [50]. Entirely mental actions, such as arithmetic, imagination, or spontaneous mental behavior, may require internal models of inference and belief states, involving epistemic goal states and mental processes [51]. Arguably, such internal models would result in a different type of internal feedback, such as an increase or decrease
of levels of certainty. One could imagine that such in-
ternal models of epistemic goals might interact with
those of the environment to enhance the effects of
mental practice of motor actions.

Replacing sensory input from simulation of internal
forward models is incongruent with core concepts of the
PCT and AIF frameworks. What would be the benefit of
mental actions in a PCT or AIF agent and how would
mental actions be defined? Both frameworks propose
generative models that generate internal percepts.
Therefore, they do not rely on a forward model ma-
chinery to simulate sensory input from motor commands.
Instead, each predicted percept provides the context for
lower-level or successive percepts. In that way, the brain
can creatively explore different chains of internal per-
cepts, engaging different layers of the control hierarchy.
In AIF, this would result in sharper priors, that is, in-
creased precision of the internal goals and their transi-
tion probabilities. In PCT, internal simulation is thought
to reorganize the control architecture, which corresponds
to modulations of the expected values of priors, as the
concept of uncertainty is not envisioned in PCT.

In Figure 1, we illustrate a possible implementation of a
continuous control mechanism scaling with abstractness.
Such a mechanism would be influenced by the precision
of priors and by the signal-to-noise ratio of the task-re-
levant (or controlled) sensory inputs (see panel D). As
such, it can accommodate perception, overt actions, and
mental actions. In the following section, we will focus on
how language production is ideally suited to test this
control scheme.

Language production as a test case for a
continuous model

Language production, involving abstract cognitive pro-
cesses, motor execution, and speech feedback, is ideal
for testing predictive control continuity. If a unified
predictive control mechanism exists, hierarchical scaling
should be observed across language processing levels.
Traditionally, research has focused either on sensor-
imotor speech control or high-level linguistic monitoring
[52]. Much research has applied predictive control to
speech motor control [5,49,53,54]. Proposals for higher-
level control often treat linguistic and sensorimotor as-
pects separately, centering either on comprehension-
based monitoring or internal production dynamics
[55–57]. This separation makes it difficult to explain
how auditory feedback can update conceptual encoding
[58,59]. A few proposals have advanced toward a unified
predictive control account of language. Pickering and
Garrod [60] propose a framework rooted in forward
modeling, where both production and comprehension
involve predictive control. Grandchamp et al. [13] pro-
posed a detailed extension of this model to inner speech,

characterizing internal speech along multiple dimen-
sions. Nevin [15], using PCT, treats both overt produc-
tion and inner speech as hierarchically organized control
processes that minimize discrepancies between internal
reference signals and actual or imagined perceptual
states.

Our framework builds on previous accounts by modeling
the role of priors and prediction error precision across
levels, offering a testable basis for both covert and overt
linguistic control. It formalizes how prediction errors —
internally or externally generated — can propagate
hierarchically. This supports a unified, scalable pre-
dictive control mechanism spanning comprehension,
overt production, and inner speech (Figure 1). The
proposal builds on the idea of dual scaling (see Figure 2),
in which temporal and anatomical hierarchies are
aligned: in a nested architecture, lower-level sensor-
imotor processes are implemented in fast, unimodal
control loops, while higher-level conceptual processes
operate on slower timescales in more integrative, trans-
modal control loops [30–32]. This assumption allows for
specific predictions both in terms of functional brain
topography, timing and temporal scope of the effects
related to predictive control for different linguistic
components. This does not entail a simple isochronic
mapping between the dynamics of overt/covert actions
and the dynamics of their neural representations [61] but
rather a timestep difference in integration windows for
different levels of the hierarchy. At a neural level, the
interactions between the layers of the hierarchy (layered
control loops) will include information modification/
perceptual associations, which likely gives rise to non-
linearity [62]. Prediction errors may cascade either up or
down the hierarchy depending on timing, context, and
the relative precision of predictions and feedback, en-
abling flexible cross-level interaction. Thus, hierarchy in
this context does not imply modular or serial processing.
Rather, language processing involves distributed, par-
allel, and interactive activation across levels, consistent
with general principles of cortical organization [19,22]
and psycholinguistic evidence [63–65]. The dual scaling
along temporal and anatomical gradients and the flexible
adjustment of top-down and bottom-up signals guiding
behavior allows the proposal to accommodate the de-
mands of real-time, context-sensitive language use. This
includes scenarios where fast articulation must be ad-
justed on the fly based on delayed high-level informa-
tion, such as semantic disambiguation or pragmatic
intent, or, in the opposite direction, scenarios where
unexpected low-level sensory feedback leads the
speaker to adjust their internal models or perceptual
goals at higher levels [58,59].

We propose empirical studies to test the hypothesis of
continuous, hierarchical language control. These would
involve perturbing control loops at multiple levels by
<figure><img src="image_5.png" /><figcaption>Figure 1</figcaption></figure>

A model serving perception, overt actions, and mental actions. Note the cascaded architecture in which every control level along the abstract to concrete axis has its correspondence in a particular sensory feature that is derived from hidden layers (dashed blue lines). Contextual information modulates bottom-up versus top-down information flow (potentially at each level of the system). The arrows' thickness represents the impact of the respective signal. The model behavior is influenced by the precision of priors and by the signal-to-noise ratio (SNR) of the task-relevant (or controlled) sensory inputs (see panel D). In case the prior is imprecise and the sensory input provides high SNR, the model updates the priors (strong bottom-up error signal in red) and thus perceives (panel A and third row in panel D). To disambiguate speech sounds, the system can engage the auditory-proprioceptive internal loop to emulate, for example, place of articulation features. When the prior is precise and an observation is unexpected under the prior, the system can adjust the sensory observation to the prior via overt action (panel B). Precise expectations about the outcome result in dominating top-down predictions (yellow) and reduced updating of expectations based on the sensory input (thin red arrows). Instead, prediction errors are used to update the predictions of lower control levels, finally reaching the muscle. The action results in minimized overall prediction error. Under optimal sensory conditions (first row of panel D), the model is more confident in its prediction error (precision-weighted prediction error) and thus its actions. When the prior is precise and the sensory input's SNR low (acting under uncertainty), the model samples more, acknowledging the uncertainty of its prediction error (last row in panel D). If sensory SNR is low (in the extreme case absent) and the prior imprecise, the model needs to gain more confidence in its desires. This could be achieved by mental actions (panel C) engaging inner loops on higher levels of the system (depending on the granularity of the emulation) involving equally weighted top-down predictions and bottom-up prediction errors that can be tuned independently of sensory input. The bottom-up prediction error is compared against memory templates on the higher hierarchical level. Uncoupling of control loops in the control hierarchy could be implemented by resetting the gain of lowest level error units to zero in case overt sensory feedback is not expected [83,84]. A mental action would thus not create a mismatch between the internal prediction of sensory feedback and the missing sensory input. Mental actions could serve both internal noise estimation and exploring options. The system samples the distribution of potential states and sharpens the priors, thereby defining potential solutions. This process uses contextual cues from higher levels of the control hierarchy or preceding priors, for example, in a sequence of thoughts. Lower-level priors are adapted to conform to higher-level priors (planning) or can emerge spontaneously in complex nonlinear interactions (creativity). Panel D illustrates Bayesian updating of priors (predictions in yellow) based on new sensory evidence (blue signal). The thickness of the red box codes the degree to which the prior is updated.

manipulating feedback and predictions in parallel (Figure 2). Targeting anatomical and/or temporal gradients across hierarchical levels could differentiate between continuous mechanisms and step-like separate accounts.

A first tool for testing the continuity hypothesis are speech error probabilities. The fact that speech errors

are rare, despite the remarkable speech rate, suggests an efficient monitoring mechanism. The nature of speech errors suggest an origin at various levels of the control hierarchy. For instance, saying ‘cat’ instead of ‘dog’ is a semantic speech error, while saying ‘sebra’ instead of ‘zebra’ is an articulatory speech error. The error probability for speech errors originating within these different positions along the hierarchy is not random:
<figure><img src="image_6.png" /><figcaption>Figure 2</figcaption></figure>

Arrow depicting the temporal course of speech planning (upper part, left to right) and speech feedback processing (lower part, right to left). The text in the arrow represents phenomena hypothesized to influence prediction precision (upper part) and prediction error precision (lower part) through the distribution of priors and the quality or availability of feedback. The gradient hierarchical architecture expected for brain anatomy and dynamics is represented through the gradient shift in color whereby red represents the transmodal extreme of anatomical gradients, corresponding to large time constants, and blue represents the unimodal extreme of anatomical gradients, corresponding to small time constants.

speakers produce contextually inappropriate words below chance (message level); they are more likely to produce a speech error if synonyms to the intended target are present in the context (semantic level) [66]; they are more prone to slip into an existing word compared to a nonsense string of sounds (lexical level) [67–69], and phonological substitutions between sounds that share articulatory-phonetic features are more likely than between those that do not share features (articulatory phonetic level) [67,68]. Such patterns suggest that there are different, context dependent, precisions of predictions (strengths in the reference signal) related to language production, with decreased prediction precision (weaker reference signal) in situations more likely to result in an error compared to less error probable situations. This difference in prediction precision (reference signal strength) should be present both for correct utterances and overt errors, as it would be driven by a different distribution of priors. This is illustrated in Figure 1, panel D: situations that are more likely to result in an error will have a more widespread distribution of priors, leading to reduced prediction precision (weakened reference signal). A given level of the planning

architecture might thus rely stronger on prediction error in case of low prediction precision depending on the context. Contrasting conditions with different prediction precision (reference signal strength) should result in observable neural traces, also when no overt speech error occurs, as the higher, pre-articulatory levels of language (just as imagination or inner speech) also rely on predictions (reference signals). Thus, well-documented biases in the probability of producing an overt error can be used to indirectly manipulate prediction precision (reference signal strength). Spatially and temporally sensitive neuroimaging techniques can be used to assess the neural traces of prediction precision both during planning and articulation stages of language production. Hence, error probability can serve as a window into the inner workings of action control at different levels of abstraction. Evidence concerning speech error-related phenomena at different levels of processing comes from separate studies but is suggestive of a gradient scaling with abstractness [70,71]. For example, lesions in the anterior cerebellum produce dysarthria, suggesting the anterior cerebellum performs auditory-motor coordinate-transformation [72] and the more posterior parts
of the cerebellum are involved in phonological and
lexical speech error processing [69,70] and semantic
predictions [73,74]. This seems consistent with a cere-
bellar modality specific to transmodal gradient. Re-
garding temporal scaling, Dorokhova et al. [68] reported
dissociations in the time course of electro-
encephalography (EEG) responses to lexical and ar-
ticulatory-phonetic prediction error manipulations
during speech planning with earlier effects for lexical
(higher hierarchical level) than articulatory-phonetic
(lower hierarchical level) manipulations. However, ana-
tomical and temporal gradients need to be explored di-
rectly and concomitantly by assessing the effects of
manipulations of prediction or prediction error at dif-
ferent hierarchical levels of language processing in the
same participants using spatially and temporally sensi-
tive techniques providing (intracranial electro-
encephalography or magnetic electroencephalography).

A complementary means of testing the continuity be-
tween motor and cognitive control is through distortions
in the feedback, targeting levels of processing of dif-
fering abstraction (articulation to semantics). Distorting
acoustic properties of the auditory feedback associated
with self-produced speech leads to increased auditory
responses [42,49,75–78] and behavioral adaptations. Si-
milar sensorimotor adaptations have been related to a
comparable control hierarchy in the song-bird brain
[79,80]. However, more abstract hierarchical levels are
more easily investigated in humans [77,78,81,82]. Per-
turbed sensory feedback is thought to generate a pre-
diction error because it does not match the prediction
stemming from speech planning steps. This entails de-
creased reafference cancellation leading to an update of
the predictions. Reafference cancellation in the auditory
cortex can be modulated by arbitrary, learned properties
such as the distance between two vowel categories [81],
indicating that both lower and higher levels of predic-
tions can be modulated by sensory input. Sensory
feedback can influence not only perception but also the
construction of sense [58,59]. For example, in a Stroop
task, participants’ auditory feedback was altered so that
it corresponded to a contextually plausible but se-
mantically different word (e.g. participants said
‘green’ but heard ‘gray’ when the word ‘gray’ was
printed in green ink). Participants often accepted dis-
torted utterances as self-generated, indicating that sen-
sory feedback can alter top-down predictions, affecting
how meaning is constructed and attributed to oneself.
Conversely, conscious monitoring of the source of
feedback during speech modulates sensory feedback
control [82]. Exploring these interactions in relation to
the continuity hypothesis requires applying para-
metrically manipulated feedback perturbations across
multiple levels (phonetics to semantics) within partici-
pants, assessing behavioral compensation (updating of
predictions) and neural attenuation responses (Figure 2).

Before concluding, it should be noted that error patterns
and auditory feedback are of course nonexhaustive
means to tap into prediction precision and prediction
error strength. Nevertheless, they provide a means of
varying prediction precision and prediction error
strength in a homogeneous way at many different levels
of processing, both on correct trials and error trials.

Conclusion

We reviewed evidence supporting hierarchical pre-
dictive control, scaling with degree of abstraction,
spanning from motor execution to abstract reasoning.
Neurobiological and evolutionary data suggest that pre-
dictive mechanisms underlying motor control have been
extended to facilitate cognitive processes, including
language production. We proposed a predictive control
scheme integrating a continuous, hierarchical me-
chanism, resulting in spatial and temporal predictions
that could be tested through the manipulation of priors,
targeting prediction precision, and feedback, targeting
prediction error strength, in language production. Future
work should directly probe this hierarchy in predictive
control to bridge movement and cognition.

Funding

This work was carried out at the Institute of Convergence
ILCB and the Cooperative Brain Imaging Center
Frankfurt. ER was supported by the ILCB (ANR-16-
CONV-0002), benefiting from support from the French
government (France 2030), managed by the French
National Agency for Research (ANR) and the Excellence
Initiative of Aix-Marseille University (A*MIDEX) and
CAK by the president of Goethe University.

Author Contributions

Both authors contributed to literature search, con-
ceptualization, and writing of the manuscript.

Declaration of Competing Interest

The authors declare that they have no known competing
financial interests or personal relationships that could
have appeared to influence the work reported in this
paper.

Acknowledgements

We wish to thank Johannes Kasper for helpful comments.

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