Braniff de la Torre-Valdovinos

Voluntary movement arises from a sequence of neural processes that involve planning, preparation, and execution within distributed cortical networks. The readiness potential, a slow negative brain signal preceding self-initiated actions, represents a sensitive indicator of motor preparation. However, it remains unclear how this signal reflects concurrent variations in mechanical and temporal demands. In this study, twenty-eight healthy participants performed self-paced elbow flexions under nine combinations of mechanical load and movement duration while brain electrical activity, muscle activity, and movement kinematics were simultaneously recorded. Linear mixed-effects analyses revealed that the amplitude of the readiness potential increased progressively with greater mechanical load, indicating that cortical readiness scales with the intensity of preparatory effort. In contrast, longer movement durations produced smaller amplitudes, suggesting that extended temporal windows reduce the efficiency of preparatory synchronization. No significant interaction between load and duration was observed, supporting the idea of partially independent neural mechanisms for effort and timing. These findings identify the readiness potential as a neural marker integrating the energetic and temporal dimensions of voluntary movement and provide a basis for understanding how cortical readiness dynamically optimizes human motor performance.

Voluntary movement arises from a sequence of neural processes that involve planning, preparation, and execution within distributed cortical networks. The readiness potential, a slow negative brain signal preceding self-initiated actions, represents a sensitive indicator of motor preparation. However, it remains unclear how this signal reflects concurrent variations in mechanical and temporal demands. In this study, twenty-eight healthy participants performed self-paced elbow flexions under nine combinations of mechanical load and movement duration while brain electrical activity, muscle activity, and movement kinematics were simultaneously recorded. Linear mixed-effects analyses revealed that the amplitude of the readiness potential increased progressively with greater mechanical load, indicating that cortical readiness scales with the intensity of preparatory effort. In contrast, longer movement durations produced smaller amplitudes, suggesting that extended temporal windows reduce the efficiency of preparatory synchronization. No significant interaction between load and duration was observed, supporting the idea of partially independent neural mechanisms for effort and timing. These findings identify the readiness potential as a neural marker integrating the energetic and temporal dimensions of voluntary movement and provide a basis for understanding how cortical readiness dynamically optimizes human motor performance.

Behavior is not a mere sequence of responses to stimuli but the dynamic expression of internal processes such as planning, prediction, valuation, and inference. These functions arise from distributed and metabolically costly neural systems and are best understood by considering behavior and neural activity together. This article presents a narrative and conceptual review of the neuroscience of behavior, integrating biological, environmental, and computational perspectives. We synthesize evidence from motor control, neural population dynamics, predictive processing, and spontaneous behavior, showing that behavior cannot be explained without the neural systems that generate it, and that neural activity gains meaning only through detailed behavioral models. Neural dynamics correlate with latent variables, such as intention and prediction error, that structure adaptive action across timescales. Recent advances in behavioral analysis, dimensionality reduction, and computational modeling enable the analysis of neural and behavioral data with comparable complexity, revealing shared computational architectures that link population activity with the organization of action. Our methodology involved a targeted literature search in PubMed and Web of Science (1919–2025), supplemented by seminal earlier works. By combining mechanistic and functional analysis, we outline a unified framework that explains how brains, bodies, and environments together generate flexible, adaptive behavior.