• Galiya M. Markova, Institute of Biophysics Siberian Branch of RAS (Krasnoyarsk, Russia), School of fundamental biology and biotechnology (Krasnoyarsk, Russia)
• Sergey I. Bartsev, Institute of Biophysics Siberian Branch of RAS (Krasnoyarsk, Russia), School of fundamental biology and biotechnology (Krasnoyarsk, Russia)

Survival in a changing environment is a task that requires identifying and remembering the most significant regularities of the environment and acting with them in mind. The ability of an organism (or an agent imitating an organism) to form and use internal representations of the external environment is called reflection in a broad sense. The article is devoted to identifying the connection between the predictability of events in the environment and the emergence of a universal skill of reflection in agents in this environment. As agents, we used heuristic model objects – simple recurrent neural networks, the primary training of which was carried out within the limit world models. These models were implemented as a set of tasks (in order of increasing predictability): responding to a random sequence of stimuli, reflexive game, responding to a set of fixed sequences, and responding to a single fixed sequence. The emergence of the universal skill of reflection after the primary training on each of these tasks was assessed by the success of the trained model objects in test tasks. The maximum Jacobian eigenvalue of the weight matrix and the type of the neural activity trajectory after the initiating single stimulus were regarded too. Based on this set of characteristics, we found out that world models with a predetermined periodicity of events (stimuli) contribute to the greatest extent to the formation of the universal reflection skill. On the contrary, in unpredictable environment conditions the emergence of internal representations is baffling. These results lead to the better understanding of reflection in a broad sense and simplify the choice of environmental conditions for further research of this phenomenon.

reflection, simple recurrent neural network, reflexive game, responding to sequences of stimuli, limit world model

2026-06-05