Writing, buttoning a shirt, picking up a coin or holding a utensil may look effortless, but each action depends on the brain’s ability to regulate force with extraordinary precision. A new study in BMC Neuroscience suggests that learning this kind of control does not simply make the hands faster or stronger. It changes how the brain processes movement, visual information and attention. After practicing a demanding pinch-grip task, healthy young adults became more accurate and consistent while showing stronger changes in alpha- and beta-band brain activity, a smaller neural response linked to novelty and conflict, and a larger response associated with attentional processing. The findings offer a detailed glimpse into the hidden neural work behind dexterous movement—and may eventually help researchers understand how such abilities are rebuilt after neurological injury.
The study, led by Aoki Takahashi and Tatsunori Watanabe at Aomori University of Health and Welfare in Japan, involved 18 healthy right-handed adults with an average age of about 22. Participants used their left thumb and index finger to squeeze a force transducer while watching a computer display. Their goal was to move a white bar into a pair of green target bars and keep it there. The target appeared at one of five force levels, corresponding to 10%, 12.5%, 15%, 17.5% or 20% of each participant’s maximum voluntary pinch force. Because the target changed unpredictably from trial to trial, participants had to identify the required force and adjust their grip rapidly rather than simply repeat a memorized movement.
Each person completed 450 trials, divided into nine blocks of 50 trials. The first two blocks established a pre-learning baseline, five middle blocks served as practice, and the final two blocks were used to measure performance after training. Participants received at least three minutes of rest between blocks to reduce fatigue. During every trial, they first relaxed, then prepared to exert force, matched the visual target during a three-second execution period and finally released their grip when the target bars turned red. This design allowed the researchers to separate several elements of performance: how quickly participants initiated the movement, how long they took to reach the target, how accurately they matched the required force and how steadily they maintained it.
Training produced clear behavioral improvements, although it did not make participants initiate their responses sooner. Reaction time remained almost unchanged, at approximately 214 milliseconds before training and 213 milliseconds afterward. The important changes occurred after movement began. Time to reach the target fell from an average of 709 milliseconds to 605 milliseconds, while velocity toward the target rose from 29.5 to 37.0 newtons per second. Participants also became more accurate: mean force error declined from 0.46% of maximum voluntary force to 0.41%, and force variability dropped from 0.20 to 0.17 newtons. In practical terms, the learners did not necessarily decide to move faster, but once they began moving, they brought their fingers to the correct force level more quickly and held that force with less fluctuation.
To see what changed inside the brain, the researchers recorded electrical activity using electroencephalography, or EEG. Electrodes were positioned over five sites, including central regions associated with sensorimotor processing and frontal and parietal areas commonly used to study cognitive responses. For movement-related activity, the team focused on the central electrodes C3, Cz and C4. They examined event-related spectral perturbations, changes in the strength of brain rhythms that occur around a particular event. The analysis concentrated on alpha activity between 8 and 14 hertz and beta activity between 15 and 30 hertz. During movement, power in these rhythms typically falls relative to a resting baseline, a pattern known as event-related desynchronization, or ERD.
Following practice, both alpha- and beta-band ERD became significantly stronger at all three central electrodes. A stronger ERD means that the relevant rhythm showed a larger reduction in power during the task compared with the pre-movement baseline. These frequencies are often linked to sensorimotor function, although they are not simple one-to-one markers of a single brain process. Alpha-band activity over central areas can reflect sensory processing and the release of inhibition, while beta-band activity is closely associated with motor planning, execution and the updating of movement states. The researchers interpret the enhanced ERD as evidence of greater engagement or more effective coordination within networks responsible for processing visual feedback and producing precise force. The result is notable because earlier studies of force-learning tasks have reported both increases and decreases in beta ERD after training.
That inconsistency may reflect the fact that “motor learning” is not one uniform process. The brain’s response can depend on whether the task uses the hand or the leg, whether participants perform a ramping contraction or hold a steady force, how long they train and how familiar the movement becomes. In the present experiment, participants practiced for more than 1.5 hours in a single session and performed a visually guided pinch task with constantly changing targets. Such a task may require the brain to remain actively engaged even after the basic movement becomes familiar. Rather than becoming completely automatic, the learned performance may have allowed the sensorimotor system to process feedback more powerfully and make finer corrections.
The study also examined event-related potentials, or ERPs—brief voltage changes in the EEG that occur in response to a stimulus. Researchers focused on the N2 and P3 components after the appearance of the visual force bar. The N2, a negative-going wave that appears relatively early, is often associated with detecting novelty, mismatch or conflict and evaluating whether incoming information requires a response. After training, the N2 became smaller. One interpretation is that repeated exposure made the visual display more familiar, reducing the need for intensive stimulus evaluation. The brain may have become faster at recognizing the task-relevant visual information, producing a less pronounced response to a stimulus that was no longer novel.
The P3 component showed the opposite pattern: its amplitude increased after learning. P3 activity is commonly associated with the allocation of attention and the updating of information relevant to a task. At first glance, the result seems counterintuitive. If practice makes a task easier, why would a marker of attentional processing grow stronger? The researchers suggest that the answer lies in the task’s variable targets. Participants had to inspect the display on every trial, identify where the target was positioned and determine how much force to apply. Practice may have improved the efficiency of visual recognition while simultaneously enabling participants to devote more attention to the difficult part of the task: producing a rapid, accurate response to an unpredictable force requirement. In this interpretation, a smaller N2 reflects familiarity, while a larger P3 reflects more effective or more focused attention.
Together, the findings portray motor learning as a dynamic reorganization rather than a simple reduction in brain activity. The improved performance was accompanied by stronger sensorimotor ERD, suggesting more pronounced engagement of movement-related networks; reduced N2 activity, consistent with easier recognition of repeated visual information; and enhanced P3 activity, indicating sustained or increased attentional investment in a complex control problem. The pattern challenges the popular idea that skilled performance always requires less neural activity. Depending on the stage and structure of learning, expertise may instead involve stronger recruitment of sensory and motor systems, particularly when precision and rapid correction remain essential.
The implications extend beyond laboratory demonstrations of finger dexterity. Fine force control is frequently impaired after stroke and other neurological conditions that disrupt cortical areas or the pathways connecting them to the limbs. Rehabilitation programs often aim to restore the ability to regulate force, but the neural mechanisms supporting that recovery remain incompletely understood. EEG markers such as ERD, N2 and P3 could eventually help clinicians track how patients respond to training, although the present study is far too small and limited to establish a clinical tool. The researchers studied only healthy young adults, used a small set of electrodes and assessed learning with the same task used during practice. They also measured brain activity before and after training, not continuously across the five practice blocks, leaving the precise sequence of neural changes unknown. Future studies will need larger and more diverse samples, high-density EEG or brain imaging, longer follow-up periods and transfer tasks that test whether improvements carry over to everyday movements. Even with those limitations, the study reveals that the brain’s mastery of a tiny, controlled pinch is anything but tiny: it involves a coordinated dance between visual recognition, sensory feedback, motor preparation and attention, all refined through practice.
Subject of Research: Neural mechanisms underlying the acquisition of fine finger force control
Article Title: Neural substrates associated with the acquisition of fine finger force control
Article References: Takahashi A, Ishizaka R, Minami K, et al. Neural substrates associated with the acquisition of fine finger force control. BMC Neuroscience. 2026;27:5.
Image Credits: AI Generated
DOI: 10.1186/s12868-025-00986-0
Keywords: Fine motor control, force control, pinch grip, motor learning, EEG, event-related spectral perturbation, alpha-band ERD, beta-band ERD, N2, P3, visuomotor processing, attention
Tags: alpha and beta brain oscillationsattention and movement regulationbrain activity during dexterous movementsfinger force regulationfunctional brain imaging in motor tasksneural mechanisms of fine motor controlneural plasticity after neurological injuryneurophysiological markers of motor learningneurorehabilitation and recoveryprecision grip learningsensorimotor integrationvoluntary movement control
