03rd September 2021

Book in Focus

Our Self-Organized Brains

A Systemic View of Human and Social Learning

By Osvaldo Agemennoni


Education in the Era of Neuroscience: Do We Use the Available Knowledge?

Humanity has a huge amount of knowledge at its disposal, but, on a social level, we often exhibit unintelligent behaviors. The human capacity to understand the phenomena of nature and to develop new technologies contrasts with our inability to correctly deal with the most important global challenges today (like poverty, hunger, violence, global warming, etc.). This undeniable fact has motivated me to focus my attention on the principles of individual and social learning, in an attempt to understand the topics to which we should pay attention. 

From an educational point of view, there are two different areas of analysis that should be considered: individual development itself (centred on the person) and society’s behavior. Educational systems seek to ensure that individuals and communities can carry out actions in pursuit of the common good. 

In my opinion, there are two concepts related to the educational areas of study (individuals and society) that should be considered seriously and that would allow us to adequately integrate both areas of study: cognitive control in the framework of self-regulated learning in individual development, and self-organization in the behavioral improvement of society. Cognitive control, or executive control, refers to our capacity to modify our behavior in pursuit of future goals. Self-regulated students guide their actions through motivation, strategic planning and metacognition. Self-organization is a dynamic and adaptive process in which systems acquire and maintain the structure by themselves, without external control, but with a certain order. The common good is a social emergent, not an imposition.

Our Self-Organized Brains expands on the following statements:

1. A systemic approach to the human cognition processes provides a framework to address human and social education in an integrated way. The systemic study of the brain has more than a century of history behind it. Its main actors knew that the understanding of the outstanding functional characteristics of the human brain required analytical tools oriented to the study of the dynamic processes involved in its evolutionary development. The beginning of this period of the study of the brain during the twentieth century is quite fascinating. Some of the relevant approaches developed are outlined in the book.

2. To understand the dynamics of the brain, it is necessary to observe how it has been shaped by the process of evolution. We have evolved in a continuously changing world, and, consequently, the nervous system has developed strategies to detect those changes and adapt our behavior to our environment, e. our close surrounding world. Figure 1 schematizes the flow of information in which our brain is involved, from neurons to the world and vice versa. The neurons of the brain are responsible for perceiving the events of the environment and the world in which we live and for performing the actions that make up our daily life. Homeostatic processes regulate physiological variables to guarantee a living condition for our body. Cognitive capabilities are responsible for maintaining or improving our performance. The dynamics involved from homeostasis to cognitive processes have two basic dynamic characteristics: feedback control loops and self-organization. The book offers a friendly introduction to these topics.

3. To fully understand brain functions, it is necessary to know the main features of a complex dynamic system. We know that the brain is a system made up of a complex network of neurons, and that it is responsible for a large number of functions, from maintaining life to developing our human activity par excellence: thinking. We also know that its structure does not clearly reflect its functions, which emerge from the particular interconnections between neurons. In this network of neurons, information flows through electrical pulses, making our brain the most amazing complex dynamic system known on the planet. However, we have paid little attention to how a complex dynamic system should be approached. The first and most important feature associated with a complex dynamic system is its unpredictability. If the dynamics of a brain are complex, try to imagine the increasing complexity of the resulting system by connecting thousands of millions of brains through the internet. As a consequence, we face a highly uncertain environment today. Cognitive control emerges as a high-level brain response to deal with the uncertainty of all the information that reaches our brain from such a complex environment. These topics are described in this book.

4. Self-organization and feedback control are two essential tools in the comprehension of brain dynamics. Let’s imagine a huge tree filled with millions of small lights instead of leaves, as an analogue to our brain and its neurons. The lights turn on and off in different branches following a pattern that depends on the activity. On a night watching our tree full of lights, we could see that the transmission of neuronal activation (lighting of the lights) would resemble the flight of a flock of birds. We can discern that these dynamic movements could not be predicted from the sum of birds, and that there is an emergent phenomenon. These actions of the system depend on its components and structure (the links between elements) and on the context that makes up its environment. Complex systems are organized in a distributed manner without centralized control. Flying flocks of birds do not have external control. Distributed control enables global performance to be achieved in pursuit of a given objective. The system has cohesion, and the causal relationships between its members make it resistant (to a certain extent) to internal or external fluctuations that could disturb its integrity. Such behaviors should be addressed in the framework of self-organized systems.

Fig. 1: Brain-environment closed loop information flow. From homeostasis to cognition.

5. The dynamics involved in the cognitive cycle allow us to clearly appreciate the learning mechanisms developed by our brain. It is well-known that response-contingent stimulation during infancy improves learning ability. This fact is true across all life stages. The sense of agency—also called the sense of control—is the feeling of becoming aware of actions and their consequences. The sense of agency plays a crucial role in the development of the causal structure of the environment in infants, as well as in the development of learning capacities throughout our lives. Research has also demonstrated its relationship to the development of coordinated action with other people. We need to feel that our actions have some consequences for our environment to be motivated to improve it. The cognitive cycle of Figure 2 allows us to deeply appreciate our dynamic interaction with our environment. We perceive our environment through our senses (sight, hearing, etc.). With this information, and that stored in our memory, we construct our reality. In the frame of that reality, we can predict a course of action. The perception-prediction-action process, in which our brain is constantly involved, allows it to develop causal mental models of the environment, categorize its different elements, etc., e. record useful information in our memories related to our particular environment. Learning processes will be clearly appreciated in this framework.


 Fig. 2: Information flow involved in the cognitive cycle.

6. The comparative study of human and artificial learning mechanisms allows a deep understanding of them. Given the parallelism between the brain and the models (artificial neural networks) that try to emulate it, the adjustment mechanisms of the models’ parameters are generally conceived of as learning processes. These artificial mechanisms used to adjust the parameters are based on physical, biological or behavioral evidence. Nevertheless, all of them fall into one of the following three learning paradigms: supervised, unsupervised and reinforced. Analyzing the artificial learning mechanisms clarifies the understanding of our learning processes. These topics are introduced in a friendly way in this book.

7. In the complex world today, self-regulated learning seems to be the most appropriate approach. Self-regulated learning refers to a person’s ability to carry out strategic motivational and guided actions (planning, monitoring and evaluating personal progress) during the learning process. The self-regulated learning process allows students to fully understand that what is essential is not the information they are acquiring. Rather, it is the meaning they can give to the experience that they and others have, as well as the opportunity to build a harmonious and articulated conglomerate of capacities. The main aspects of self-regulated learning processes are clearly presented in the book.

8. There are a few necessary conditions for a self-organized society to develop. Researchers have studied the conditions that should be met so that, even when individuals pursue their interests, they act in favor of the interests of society as a whole, regardless of any intention to do so. The emergence of self-organizing societies during the evolution of life on Earth, from self-producing molecular processes, protocells, prokaryotic cells, eukaryotic cells, multicellular organisms, and animal societies, to human communities, corporations, states, etc., shows similar patterns and a few necessary conditions. The book presents these conditions are presented and addresses some challenges of today’s society (like the impact of AI and the dynamics of scientific systems).

Final considerations

Neuroeducation is a new area of study that groups together researchers in cognitive neuroscience, educational psychology, educational technology, education theory and other disciplines, and explores the relationships between biological processes and education. Neurosciences are a set of new disciplines that have had a tremendous evolution in recent decades due to the development of different new technologies in brain images and signal processing, among others. However, it should be remembered that the first steps in the development of a new discipline are usually dominated by a reductionist approach to concentrate the efforts on a few elements in order to achieve a conclusion of causes and effects with enough supporting pieces of evidence to be accepted by the scientific community.

Reductionism is usually a necessary approach in order to understand many different phenomena of a large complex system. However, it is important to put the knowledge achieved in that way within the context of the whole system in order to fully grasp the large scale relationships, i.e. to view the results with an emergent approach to view the main dynamics of the system altogether. Let us think about the way a painter paints over a large canvas. Not only do they need to get closer to the canvas in order to focus on the details (the reductionist perspective), but they also need to step away from the paint to see it from a distance and to appreciate it as a whole (to see the emergences of the paint). This natural behavior of a painter is not so common in all human areas, particularly in different scientific disciplines and educational systems.

The areas of study in neuroscience range from those focusing on the neuronal cell to those focusing their attention on modelling and analyzing the highest level of the brain functions. Particularly, and due to the immature condition of this relatively new discipline, in the middle of this range it is possible to appreciate a gap. A gap often makes interdisciplinary communication difficult. This book was written to contribute to the reduction of such a gap.


Osvaldo Agamennoni has a Bachelor’s degree in Electronics and a PhD in Control Science, both from Universidad Nacional del Sur (National University of the South), Argentina. He also obtained postdoctoral experience at the University of Sydney (1992-1994), working on an artificial intelligence project. His research focuses on areas such as nonlinear dynamics and control, neural networks, machine learning, and neuroscience. For the last 15 years, he has been working on the evaluation of mild cognitive impairment through the modelling of eye movements. He is the co-founder of View Mind (https://viewmind.com.ar/en/), a start-up dedicated to keeping track of executive, attentional and memory functions, and related brain activities, through 10-minute, non-invasive, standardize automatized evaluation.


Our Self-Organized Brains: A Systemic View of Human and Social Learning is available now in Hardback at a 25% discount. Enter the code PROMO25 at the checkout to redeem. 

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