Neuroscience has long focused on understanding brain activity during task performance. As a result, current training methods aim to maximize brain activation during a trained task. However, new evidence shows that this may not be an efficient way to go. Human subjects achieve maximum performance only when the brain network is in a state of high spontaneous interaction and communication between brain regions before training or, in other words, in a state of high "network communication." In this case, minimal effort is required during the task. This requires new learning strategies aimed at inducing higher network communication prior to task execution. The investigators have previously shown that healthy people can learn to increase network communication of motor areas (i.e., the areas that control movement) when they receive real-time feedback on their current activity, which is known as neurofeedback. In neurofeedback, subjects receive continuous feedback about the state of their brain activity in a present moment. Through this feedback, they can learn to change their own brain activity.

The aim of the present study is to validate neurofeedback as a new treatment approach for inducing high network communication at rest (i.e., when participants are not engaged in a task), and to test whether this heightened network communication can enhance visual perception and motor learning.

As the investigators have seen in a pilot study, participants are unable to improve the network communication of visual brain regions (i.e., regions that process light stimuli) through neurofeedback when the feedback is perceived with the eyes. Therefore, the goal of Experiment 1 is to find an alternative form of feedback through which participants can efficiently increase network communication. Specifically, participants will receive neurofeedback in the form of a sound, a vibration on the skin, or both. For neurofeedback as sound, the investigators will use relaxing sounds which have previously been found to enhance network communication. For neurofeedback as vibration, electrical or vibrotactile stimulators will stimulate both hands and feet, as previous research has found a positive effect on brain network communication. The sensory stimulation intensity will be modulated based on the current level of network communication between the target brain area and the rest of the brain. Thus, greater communication leads to reduced feedback intensity. The idea is that subjects learn to maintain states of high communication without sensory feedback. Subjects will receive the instruction to lower the feedback level without indication of any particular strategy of mental imagery, as the investigators have previously observed that no specific mental imagery task is able to enhance alpha-band FC without feedback. Finally, to explore effects on the behavioral level, the investigators will additionally assess visual perception at the beginning and at the end of each session, similarly as in a previous study.

In Experiment 2, the neurofeedback modality from Experiment 1 is adopted to test whether increasing network communication through neurofeedback can lead to improved visuo-motor learning. Visuo-motor learning will be measured with the mirror-drawing task because the investigators have evidence for feasibility from a previous study and because it represents a good model for re-learning as needed in clinics.

In both experiments, participants will undergo magnetic resonance imaging (MRI). This MRI will increase the precision of neurofeedback.

Inclusion Criteria:

• Signed informed consent
• Age at least 18 years old
• Normal or corrected-to-normal vision
• No neurological or psychiatric diseases
• No regular consumption of benzodiazepines or neuroleptics

Exclusion Criteria:

• Any surgical intervention to the brain
• Drug or alcohol abuse
• Presence of non-MRI safe metal in the body

In Experiment 1, participants will receive three different interventions (auditive neurofeedback, tactile neurofeedback, or both) in different sessions. The order of interventions will be counterbalanced across participants.

In Experiment 2, participants will be randomized to one out of three parallel treatment arms (i.e., Group A, B, or C). Randomization, stratified for age and gender, will be generated with a computer random number generator.

In both Experiments, participants will be blinded to the intervention order and group allocation.