The brain has learned to handle multitasking through training

Multitasking is considered ineffective: when a person has to perform several actions at the same time, the brain encounters interference, and it is more difficult for it to allocate resources between tasks. However, a new study has shown that this cognitive barrier does not necessarily remain permanent. With regular practice, the brain is able to rearrange the work of neural networks so that different tasks interfere with each other less. About how the brain learns to cope with several tasks at the same time, why practice is important for this, and what the discovery can tell about the work of the nervous system — in the material of Izvestia.

At first, the brain gets confused, and then it separates the tasks.

The results of the study by the City University of Hong Kong and the Chinese University of Hong Kong are published in the journal Neuron. Scientists have studied what happens in the brain when trying to perform two tasks simultaneously and how this reaction changes after training. For the experiment, mice were used and the work of the secondary motor cortex was observed while performing two tasks simultaneously. In the beginning, different groups of neurons competed for information processing, which caused interference. In other words, it was difficult for the brain to simultaneously maintain several independent streams of activity.

However, after regular training, the situation has changed. The secondary motor cortex began to actively attract specialized neurons for individual tasks, and representations of these tasks in the brain began to separate. As a result, the mutual interference decreased, and the overall effectiveness of the two actions increased.

Yun Winho, professor, head of the Department of Neuroscience at the City University of Hong Kong and head of the study, compared what is happening with the road system of a large city: if all vehicles try to drive along several of the same roads, traffic jams occur.

The brain has found its "conductor": scientists have discovered a gene that controls stress

Why Multitasking is getting Easier

The results show that the brain is able to dynamically redistribute neural resources depending on experience. At first, performing multiple tasks simultaneously creates a cognitive bottleneck: different neural populations compete for the same resources. But as practice progresses, the brain rebuilds their work, enhancing the specialization of individual neurons.

Thus, it's not just that a person is "getting used" to multitasking. The study points to a specific mechanism by which the organization of information processing is changing. The representations of different tasks are gradually becoming more separate, so they interfere less with each other.

To test the universality of the discovered principle, the researchers also used recurrent neural networks. Computer models were able to reproduce the same mechanisms that were observed in the brain, and also showed that such an organization can accelerate learning to perform two tasks. The study was conducted on a mouse model, so its results cannot be directly transferred to humans. However, the work provides insight into how the plasticity of the brain makes it possible to optimize the performance of complex actions through experience and training.

Scientists have discovered a hidden mechanism for repairing brain cells

From training to rehabilitation

The researchers believe that the data obtained may be important not only for understanding multitasking. The revealed mechanism shows how the brain is able to rebuild neural connections and distribute functions between specialized groups of cells. This expands the understanding of neuroplasticity, the ability of the nervous system to adapt to experience.

Overall, the work demonstrates that the brain's ability to simultaneously process multiple tasks may be more flexible than is commonly believed. What initially looks like a severe limitation of the cognitive system, with sufficient practice, can change due to the restructuring of neural networks.