Reconstruction of a neural network and alteration of the operators’ strategies during the recognition of facial images

Abstract

Functional magnetic-resonance imaging was used to study the dynamics of information processing concerning a face in the neural networks of the brain. It is shown that large-scale neural networks of the operators’ brains were reconstructed during the second half of the study. Thus, when an instruction conflicts with the contents of the image so that it is impossible to find the correct solution, the operators transfer to a less energy-intensive behavioral strategy—they simulate the solution of the problem. When the assigned task is carried out under specified conditions, so that it is easy to distinguish the rotation of the head, the subject quickly learns to solve the problem, develops the knack, and adopts an automatic-motion regime—another neural network is activated, called the brain’s default-mode network. It is shown that the medial prefrontal cortex plays a role in developing and altering the strategy of the operator’s activity. The reconstruction of large-scale neural networks is probably caused by an unconscious mechanism that simplifies purposeful activity. The results agree well with the well-known principle of least action.