This thesis investigates the neural and behavioral mechanisms underlying covert motor processes, building on a well-established body of research on motor imagery, motor preparation, and action observation. Traditional methods have primarily relied on the 1 muscle × 1 action (1m1a) approach, in which transcranial magnetic stimulation (TMS) is applied to the motor cortex and motor-evoked potentials (MEPs) are recorded from a single target muscle. While informative, this method reduces complex motor representations to isolated joints displacements, neglects intermuscular interactions, and overlooks single-trial variability. To overcome these limitations, the thesis introduces the MultiMEP approach, which records MEPs simultaneously from multiple muscles and applies decoding techniques inspired by multivoxel pattern analysis (MVPA). This method captures distributed patterns of corticospinal activity, providing a more ecologically valid and multidimensional perspective on covert motor processes. The experimental studies discussed in this thesis, demonstrate the effectiveness of MultiMEP in decoding different ecological actions during both motor imagery and motor preparation tasks, with accuracies significantly above chance. Eventually, the thesis also examines the effect of visuomotor training on action observation which at the behavioral level is reflected in automatic imitation, particularly its modulation through counter-imitative training. Results challenge standard interpretations of sensorimotor contingency accounts, suggesting greater flexibility in how experience shapes motor representations. Together, these findings highlight both methodological and theoretical contributions for understanding, through other perspectives, motor cognition.
EXPLORING COVERT MOTOR PROCESSES: BEHAVIORAL INSIGHTS AND INNOVATIVE APPLICATIONS OF TRANSCRANIAL MAGNETIC STIMULATION / F. Genovese ; supervisori: C. Sinigaglia, G. Barchiesi ; coordinatore: A. Sereni. - Dipartimento di Filosofia "Piero Martinetti" - Sala Piero Martinetti - Via Festa del Perdono 7, 20161, Milano. Dipartimento di Filosofia Piero Martinetti, 2026 Jan 26. 38. ciclo, Anno Accademico 2024/2025.
EXPLORING COVERT MOTOR PROCESSES: BEHAVIORAL INSIGHTS AND INNOVATIVE APPLICATIONS OF TRANSCRANIAL MAGNETIC STIMULATION
F. Genovese
2026
Abstract
This thesis investigates the neural and behavioral mechanisms underlying covert motor processes, building on a well-established body of research on motor imagery, motor preparation, and action observation. Traditional methods have primarily relied on the 1 muscle × 1 action (1m1a) approach, in which transcranial magnetic stimulation (TMS) is applied to the motor cortex and motor-evoked potentials (MEPs) are recorded from a single target muscle. While informative, this method reduces complex motor representations to isolated joints displacements, neglects intermuscular interactions, and overlooks single-trial variability. To overcome these limitations, the thesis introduces the MultiMEP approach, which records MEPs simultaneously from multiple muscles and applies decoding techniques inspired by multivoxel pattern analysis (MVPA). This method captures distributed patterns of corticospinal activity, providing a more ecologically valid and multidimensional perspective on covert motor processes. The experimental studies discussed in this thesis, demonstrate the effectiveness of MultiMEP in decoding different ecological actions during both motor imagery and motor preparation tasks, with accuracies significantly above chance. Eventually, the thesis also examines the effect of visuomotor training on action observation which at the behavioral level is reflected in automatic imitation, particularly its modulation through counter-imitative training. Results challenge standard interpretations of sensorimotor contingency accounts, suggesting greater flexibility in how experience shapes motor representations. Together, these findings highlight both methodological and theoretical contributions for understanding, through other perspectives, motor cognition.| File | Dimensione | Formato | |
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