The neural mechanisms mediating the activation of the motor system during action observation, also known as motor resonance, are of major interest to the field of motor control. It has been proposed that motor resonance develops in infants through Hebbian plasticity of pathways connecting sensory and motor regions that fire simultaneously during imitation or self movement observation. A fundamental problem when testing this theory in adults is that most experimental paradigms involve actions that have been overpracticed throughout life. Here, we directly tested the sensorimotor theory of motor resonance by creating new visuomotor representations using abstract stimuli (motor symbols) and identifying the neural networks recruited through fMRI. We predicted that the network recruited during action observation and execution would overlap with that recruited during observation of new motor symbols. Our results indicate that a network consisting of premotor and posterior parietal cortex, the supplementary motor area, the inferior frontal gyrus and cerebellum was activated both by new motor symbols and by direct observation of the corresponding action. This tight spatial overlap underscores the importance of sensorimotor learning for motor resonance and further indicates that the physical characteristics of the perceived stimulus are irrelevant to the evoked response in the observer.
We used behavioral and functional magnetic resonance imaging (fMRI) methods to probe the cerebral organization of a simple logical deduction process. Subjects were engaged in a motor trial-and-error learning task, in which they had to infer the identity of an unknown 4-key code. The design of the task allowed subjects to base their inferences not only on the feedback they received but also on the internal deductions that it afforded (autoevaluation). fMRI analysis revealed a large bilateral parietal, prefrontal, cingulate, and striatal network that activated suddenly during search periods and collapsed during ensuing periods of sequence repetition. Fine-grained analyses of the temporal dynamics of this search network indicated that it operates according to near-optimal rules that include 1) computation of the difference between expected and obtained rewards and 2) anticipatory deductions that predate the actual reception of positive reward. In summary, the dynamics of effortful mental deduction can be tracked with fMRI and relate to a distributed network engaging prefrontal cortex and its interconnected cortical and subcortical regions.
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