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Abstract <p>Understanding the relationship between energy expenditure, cognitive activity, and performance is a key problem in psychophysiology. A series of psychophysiological studies have shown that solving cognitive tasks is accompanied by changes in the systemic characteristics of EEG signals, such as fractal dimension and entropy. These changes are associated with both the success of cognitive actions and their energy expenditure. In the latest study, EEG was recorded from 68 participants solving a task in which they compared graphs and tables. They determined which of three tables corresponded to the numerical data presented on the graph. EEG signals were analyzed using the Higuchi fractal dimension algorithm. The results showed a short-term increase and then a gradual decrease in EEG fractal dimension (F(4, 7656) = 36.594, <i>p</i> &lt; 0.0001). The decrease in fractal dimension was accompanied by a decrease in heart rate. We modeled this effect in the dynamics of neural network training. The model was trained to classify two input values ranging from –π/2 to π/2. The target function was a sinusoid, followed by classification into two states: 0 or 1. At each training step, the activations of all neurons in the network were summed and the fractal dimension was calculated, briefly increasing and then gradually decreasing.</p>

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Energy and Entropy in Solving Cognitive Problems and Their Reflection in the Total Electrical Activity of the Brain

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • I. A. Gorbunov,
  • S. V. Morozova

摘要

Abstract

Understanding the relationship between energy expenditure, cognitive activity, and performance is a key problem in psychophysiology. A series of psychophysiological studies have shown that solving cognitive tasks is accompanied by changes in the systemic characteristics of EEG signals, such as fractal dimension and entropy. These changes are associated with both the success of cognitive actions and their energy expenditure. In the latest study, EEG was recorded from 68 participants solving a task in which they compared graphs and tables. They determined which of three tables corresponded to the numerical data presented on the graph. EEG signals were analyzed using the Higuchi fractal dimension algorithm. The results showed a short-term increase and then a gradual decrease in EEG fractal dimension (F(4, 7656) = 36.594, p < 0.0001). The decrease in fractal dimension was accompanied by a decrease in heart rate. We modeled this effect in the dynamics of neural network training. The model was trained to classify two input values ranging from –π/2 to π/2. The target function was a sinusoid, followed by classification into two states: 0 or 1. At each training step, the activations of all neurons in the network were summed and the fractal dimension was calculated, briefly increasing and then gradually decreasing.