smoothly makes his/her work better and better doing less and less errors. It brings him/her persistently closer and closer to the stable zero point. The zero point means in our context that the person stops to do errors.

![0_image_0.png](0_image_0.png)

Fig. 6. A theoretical phase portrait of self-learning type (Gavrikov, Khlebopros, 2009) for the case when X stands for number of errors. X˙ denotes the change in error number with time, N stands for an initial number of errors.

The analysis of the data received shows a more complicated picture that a theoretical phase portrait (Fig. 6). The real phase portraits for solvers are shown in Fig. 7. In the figure, the flow of time is marked by small arrows. It is obvious that no monotonous flow from many errors to zero errors is observed. The dynamics of task solving resembles rather spiral oscillations with a definite trend to lower errors. The spiral trajectory for the participants "K" and "B" takes place both in the beginning and closing phase of solving. The phase portrait for the participant "M" has only one cycle.