The study, led by Alessandro Scano of Italy’s National Research Council in Milan, does not present new in vivo data. Instead, it delivers something arguably more disruptive: a conceptual and computational critique of how the field interprets its own most successful framework. Scano’s argument begins with an observation that has long been treated as a nuisance. When researchers extract muscle synergies from multichannel electromyography (EMG), the resulting modules differ systematically between individuals and even within the same person across sessions and contexts. Standard practice has been to average these differences away or dismiss them as measurement error. Scano flips that assumption on its head, proposing that a substantial portion of this variability is structured, meaningful, and traceable to three non-motor layers of control that standard synergy extraction methods simply never measure.

The first layer is cognitive. Before any movement is executed, the brain constructs an internal model of the task—what the goal is, which strategy might work, where attention should be allocated. This internal representation is not fixed. Two people given identical instructions may build qualitatively different models of the same task, leading to different motor solutions. In overlearned actions like walking, the cognitive layer recedes into the background. But in strategically demanding situations—imagine a penalty kick where the shooter must read the goalkeeper’s posture, weigh probabilities, and select not just how to kick but which kick to attempt—the cognitive layer becomes the dominant driver of motor output. Experimental evidence shows that such decision-making can alter movement kinematics and timing even when the final motor goal is unchanged, a finding consistent with the view that motor control is fundamentally decision-making.

The second layer is affective, and its fingerprints on muscle activity are among the best documented in psychophysiology. Anxiety and stress reliably increase muscle co-contraction, stiffen movement, and elevate variability, reflecting a shift toward rigid, conservative control strategies. Confident or positive states, by contrast, are associated with more fluid and energetically economical patterns. The infamous phenomenon of choking under pressure—when well-learned skills disintegrate at the worst possible moment—exemplifies this modulation. Neurophysiologically, these effects align with the classical Yerkes–Dodson law: moderate arousal can sharpen performance, while excessive anxiety degrades it. Scano’s framework interprets affective states as gain modulators that adjust the operating regime of the motor system—muscle tone, variability, responsiveness to feedback—without necessarily specifying new motor commands. The same internal task representation can thus produce markedly different muscle activation patterns depending on the performer’s emotional state.

The third layer is experiential, and it may be the most conceptually radical. Scano argues that what researchers identify as muscle synergies may partially reflect a subject’s motor history rather than a universal organizational principle of the nervous system. Athletes trained in different disciplines—throwing sports, racket sports, or non-athletic backgrounds—often exhibit systematically different coordination patterns when performing superficially identical movements, differences that biomechanics alone cannot explain. These stored patterns are embodied priors: stabilized coordination solutions carved out by repeated practice and reused across tasks. In this reading, the motor primitives that the field has treated as hardwired building blocks may be better understood as the fossilized residue of learning—patterns that emerged through experience and were subsequently consolidated into what looks like intrinsic structure.

To make these ideas testable, Scano proposes a quantitative extension of the standard synergy model. In the classical formulation, EMG activity is decomposed into spatial synergies (the matrix W) and temporal activation coefficients (the vector c). The framework hypothesizes that cognitive, affective, and experiential factors primarily modulate the temporal structure of motor commands—the coefficients c—while the spatial modules W remain comparatively stable during transient contextual shifts. This distinction has a neurophysiological rationale: spatial primitives are widely considered to be encoded in spinal and brainstem premotor networks, whereas acute stress or cognitive load characteristically modulates the descending central drive rather than instantly restructuring low-level anatomical circuitry. The proposal introduces an Augmented Reconstruction Index (ARI), which partitions the variance explained by a synergy-based model into a motor component and an additional context-dependent component, quantified by the incremental contribution of contextual variables such as heart rate variability, electrodermal activity, or brief self-reports of anxiety and effort.

The computational proof-of-concept is where the argument becomes genuinely alarming for anyone who has published synergy analyses. Scano simulated an eight-muscle upper-limb reaching task driven by three ground-truth spatial synergies, then added a simulated internal state—a tonic contextual variable with a fixed value of 0.6 and a sensitivity vector introducing module-specific shifts—to mimic the sustained baseline EMG elevation, heightened sympathetic tone, and generalized low-level co-contraction documented in real subjects under psychological pressure or cognitive load. When standard unconstrained non-negative matrix factorization (NMF) was applied to this contextualized data, it achieved a marginally higher overall reconstruction (R² = 0.9989) than the constrained ARI approach (R² = 0.9983). But it achieved this mathematical edge by distorting the extracted spatial synergies to absorb the contextual offset—a phenomenon Scano terms structural drift. The algorithm misinterpreted context-driven tonic changes as a fundamental reorganization of the underlying motor coordination strategy, even though the true spatial modules had never changed.

The implications extend beyond simulation. In real-world human data, where typical reconstruction quality hovers around 90 percent rather than the near-perfect levels of the toy model, the failure to account for context may force unconstrained decomposition methods to artificially extract an additional, supernumerary synergy to explain variance that actually reflects a stress-induced tonic offset. Scano calls this coefficient pollution: a mathematical artifact that merges genuine biological motor structure with context-dependent modulation, destroying the neurophysiological interpretability of the decomposition. A sudden shift in cognitive load modulates central drive, but it does not instantaneously create a new anatomical primitive. The conventional workaround—simply increasing the number of extracted synergies—lacks physiological justification for transient contextual modulations and risks publishing phantom synergy reorganizations that are actually stress signatures in disguise.

The framework generates five falsifiable predictions that can be tested with standard motor control paradigms augmented with minimal assessments. Experimentally induced stress should systematically alter temporal activation patterns while leaving spatial synergies stable, manifesting as an increase in the context-dependent component of explained variance. Cognitive load under dual-task conditions should similarly inflate context-dependent variance. Experts and novices performing the same task may show comparable motor reconstruction quality but differ systematically in the structure of their latent contextual factors, suggesting that differences traditionally attributed to motor primitives may instead reflect embodied priors. As tasks become familiar through practice, the contextual contribution should progressively shrink. And standard decomposition methods should continue to misattribute contextual offsets to spurious structural changes—a prediction the simulation already demonstrates.

The practical consequences ripple outward from basic research into rehabilitation and human–robot interaction. In clinical settings, altered muscle synergies are typically attributed to neural damage or biomechanical constraint, but the framework highlights that anxiety, motivation, cognitive deficits, and prior experience can also shape patient-specific coordination patterns—meaning that variability currently misread as impairment may partly reflect treatable affective or cognitive states. Rehabilitation protocols could be enhanced by targeting task understanding and emotional regulation alongside motor execution, and Scano notes that in robotic applications, an exoskeleton control system equipped with the ARI framework would avoid confusing user stress with a change in motor intention. The author is careful to acknowledge limitations: the linear coupling between contextual states and temporal activations is a first-order approximation, real psychological factors almost certainly combine non-linearly, and the framework captures instantaneous dynamics rather than longitudinal plasticity. But the core message stands with unusual clarity for a theoretical paper. Muscle synergies are not fixed building blocks of movement; they are context-dependent coordination patterns emerging from an integrated cognitive–motor system. The variability that decades of synergy analysis have treated as noise may be the richest signal in the entire dataset—the measurable trace of a mind shaping every movement it commands.

Subject of Research: From motor primitives to context-dependent coordination: a system neuroscience approach integrating cognitive, affective, and experiential factors into muscle synergy analysis

Article Title: From motor primitives to context-dependent coordination: a system neuroscience approach integrating cognitive, affective, and experiential factors into muscle synergy analysis

Article References: Scano, A. (2026). From motor primitives to context-dependent coordination: a system neuroscience approach integrating cognitive, affective, and experiential factors into muscle synergy analysis. Frontiers in Systems Neuroscience, 20, Article 1838867. https://doi.org/10.3389/fnsys.2026.1838867

Image Credits: AI Generated

DOI: 10.3389/fnsys.2026.1838867

Keywords: motor, primitives, context-dependent, coordination, system, neuroscience, approach, integrating, cognitive, affective, experiential, factors