The chaotic nature of temper in humans: a long short-term memory recurrent neural network model.
In mathematics and physics, chaos theory deals with the behavior of certain nonlinear dynamical systems that under certain conditions exhibit a phenomenon known as chaos, which is characterised by a sensitivity to initial conditions. Mathematicians paradoxically call such states of order chaos and distinguish them from randomness. New models for describing and predicting different aspects of behavior are being created which once seemed unpredictable. This is done by focusing on the overall patterns of behavior, showing how stable or unstable they are and identifying the circumstances that make them change. In this paper, we indicate why human temper and mood changes have a chaotic nature. Then, we develop a chaotic model based on a long short-term memory recurrent neural network with irregular embeddings derived by the gamma test to model temper tantrum. We finally use a feedback delay controller to stabilize its chaotic behavior, because it is a plausible method for stabilizing biological neural systems. A lot of aspects of this model are analogous to the human counterpart. The model might suggest, for example, that if a particular person had stronger ego defenses, and attended a little less vigilantly to the external world, he or she might find a stable attractor amidst of a broader landscape of chaotic attractors. A therapist or self-control would be analogous to the delayed feedback controller, by specifically encouraging those changes, he might help the person reach a stable behavior. Finally, some comments are proposed to facilitate the normal behavior.
Zifan
Ali
A
Neuromuscular Systems Laboratory, Department of Biomedical Engineering, Amirkabir University of Technology, Somayyeh, Hafez, Tehran 15875-4413, Iran. arshiaprado2002@yahoo.com
Gharibzadeh
Shahriar
S
D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013694:Temperament