Predictions from Complex Mind Theory: Understanding and confusion

Mind is a complex dynamical system of mental events that happen in the brain. How can we characterize important mental phenomena in this framework, such as understanding or confusion? What prediction can we derive from a theory of the mind founded on complexity science, more specifically as it relates to these mental phenomena?

In this short essay I try to show that complicated, nontrivial mental phenomena of understanding and confusion can be elegantly characterized from the perspective of complexity science. I also make an attempt at pointing to real predictions of the Complex Mind Theory that can be translated into experimental procedures that would eventually help in deepening of our understanding of the mind and the brain.

I have already outlined basics of what I call Complex Mind Theory – a theory of the mind from the perspective of complexity science or complex systems science – on this blog. So I will just briefly bring the most important tenets to attention. Complex Mind Theory is a name for ongoing research endeavors that heavily base their methods, paradigms, vocabulary and formulate their goals and research questions on complex systems science, dynamical systems science and network science (Bassett and Gazzaniga, 2011; Papo et al., 2014; Tognoli and Kelso, 2014a; Tognoli and Kelso, 2014b).

Brain, according to Complex Mind Theory, is a complex system, that is a system composed of many tightly coupled elements, neurons, spread across connected neuronal networks (Sporns, 2010). Self-organization of neuronal networks takes precedence over rigid routes of information processing modules. Neurons and neuronal ensembles interact with each other leading to the emergence of novel behaviors, patterns and forms of dynamics that are very different from those that we observe in single neurons. In the brain many parallel dynamics take place at any given time. These dynamic patterns are patterns of neuronal activity of connected ensembles of neurons (Spivey, 2007). Mind is a system that acts in the way just described.

Phenomena of understanding and confusion

Confusion is a very specific mental phenomenon that we are all familiar with. When we are confused about something we don’t understand we have this peculiar feeling that we call confusion.

What, then, is this confusion, how does it manifest itself in the brain? If we want to understand confusion we should first try to understand understanding.

A glimpse at understanding

Understanding, even if incorrect, pertains to the recognition of the way certain ideas are connected with each other. We understand how parts of the story fit the whole narrative, when we can connect them appropriately. We understand how a cause is linked with an effect, when we can connect the two by a network of other ideas. When we recognize how a premise leads to a conclusion, we understand the inference.

Understanding is not only rational, but also emotional. We can understand why and how a person is feeling about a subject, because we can create similar or analogous connections with ideas and emotions in our own minds.

Understanding is very often embodied and enactive. Understanding usually does not require abstract concepts, abstract relations, purely rational inferences. Most often the process is taking place in contextualized situations in which an agent (cognitively active organism) is performing actions and is in various ways actively engaged with its surrounding. Thus, understanding is tightly coupled with the environment, the agent’s actions and the situation.

Understanding can be abstract. Human minds have remarkable capabilities of making abstractions and generalizations, pinnacle of which we witness in the form of mathematics. Abstract ideas such as “cause”, “relation”, “good”, “infinite” most likely have their genesis in recognizing patterns and patterns of patterns in the world and in one own’s mind. They link to connected concepts and produce understanding when the whole pattern of connected ideas progresses and develops coherently, fitting with each constituent concept, possibly encompassing more of them as dynamics of pattern self-organization happens in the mind.

Understanding, even if incorrect, involves organization of stable pattern in the brain. When a stable pattern of activity, most likely distributed across various connected areas, links already existing patterns associated with language, perceptions, emotions, then a simulation can take place. Linked ideas, memories are being “run” in the brain, that is the pattern evolves in time, it is changing, reorganizing itself. The linked ideas are understood, because they are a part of a whole pattern, they are coherently and cohesively linked together in the dynamics of the evolution of the neuronal pattern. Understanding arises when the mind recognizes such stable, distributed, linked, coherent pattern being formed, when the organism is confronted with a problem that is new to it.

I have previously written about understanding in a previous post: Mind is not an information processor.

Understanding and simulation

Understanding is achieved by means of connecting relevant ideas and simulation (Barsalou, 1999; Barsalou, et al. 2008) of those ideas in a dynamical self-organization of idea pattern that constitute a thought.

Simulation happens on many occasions. When we recall specific things, when we dream, even when we recognize ourselves in the mirror. What differentiates understanding from other occurrences of simulation are two factors: 1) the context in which the mental phenomenon develops, and 2) specifics of the process.

Firstly, understanding happens when the cognitive agent actively seeks to create a model of something (e.g. seeks to know why and how something happened). The agent had not have a ready pattern of thought when it confronted the problem. The process of self-organization of ideas in the process of thinking was then initiated. Simulation mostly pertains to situations, when the problem is known and what is left to do is mostly running the known thoughts.

Secondly, simulation refers to the process of unfolding of thought patterns, whereas understanding emphasizes self-organization of ideas into patterns in such a way as to fit those ideas together in a coherent manner, that is in a way that these ideas all fit together.

Experimental separation of understanding and simulation would not be an easy task, as proper understanding requires lasting simulation of thoughts and ideas. But it should nonetheless be possible, because induction of self-organization of previously not related or only loosely related concept into a coherent whole is qualitatively different than mere running of the thinking process of ideas with known connections.

Confusion as a disruption of understanding

When I try hard to understand a particular math problem again and again and I fail each time arriving at wrong answers or nothing at all I can say that I am confused, I don’t understand what’s it all about. When I’m looking for my keys that I’ve just seen on the table and they’re not there, I’m confused – I don’t understand why they shouldn’t be there, I don’t know how they disappeared. When you’re explaining to me your argument and I cannot connect the dots I am confused – I have no idea how all this paints a picture that you’re trying to convey.

Confusion arises when an agent tries to understand a solution, a problem, a situation but fails to do so. When trying to grasp the floating ideas and thoughts into a coherent ensemble, an agent recognizes that it cannot fit them all together: the elements seem to be such that they cannot be linked, or the linked ideas to not lead to the running of the simulation that would unfold thinking with relevant concepts. It may also be the case that some elements are inaccessible, for example due to inability to recall concepts and relations between them from memory, or even due to brain lesion. Confusion is thus a disruption of the process of self-organization of a consistent ensemble of thoughts, a disruption of a process of understanding.

Predictions concerning confusion based on Complex Mind Theory

Any good scientific theory should make verifiable predictions about range of phenomena that it describes and explains. Such should be the case with respect to Complex Mind Theory. If the theory predicts that a phenomenon arises in particular context, then we should test these predictions by making experiments to see if such and such events actually do take place on such and such conditions. If the results are positive, then we gain confidence in the theory in question and we can build on on theory, making new predictions and explanations. If the results are negative, then the theory can be modified, fixed or discarded.

What can we expect from Complex Mind Theory with regards to understanding and confusion?

1. Relative separation of understanding and simulation. These two phenomena are tightly coupled, but their nature is slightly different.
– Thus it should be possible to experimentally evoke understanding with very little simulation, that is understanding that does not require elaborate running of thoughts.
– It should also be possible to induce simulation of thoughts that does not lead to high levels of understanding. That is, the mere thinking may on many occasions be associated with low level of understanding in the form of familiarity: a familiar or linearly proceeding scene or narrative will be understood by subjects, but will not evoke strong feelings of understanding.

2. Linear progression of a task, scene, narrative should lead to understanding much more easily, with no sudden burst of understanding. The understanding of a given situation will be achieved by means of adding up and building upon the concepts and thoughts through simulation. The sudden moments of understanding, when the understanding is achieved in an unexpected manner, in a “aha!” style, should be very rare.

3. Out of order presentation of a situation should lead to sudden understanding or “aha!” moments. Nonlinear, or out of order, presentation of a narrative or a story will connect concepts and thoughts in largely disconnected chunks. When the last or the crucial element of the story is presented, these large chunks will suddenly connect with each other and they will also undergo major reorganization of patterns. Linear simulation will have much less weight on the effect.


Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200–209. doi:10.1016/j.tics.2011.03.006.

Barsalou, L. W. (1999). Perceptual symbol systems. The Behavioral and brain sciences, 22(4).

Barsalou, L., Santos, A., Simmons, W., and Wilson, C. (2008). Language and simulation in conceptual processing. In De Vega, M., Glenberg, A. M., and Graesser, A. C., editors, Symbols, embodiment, and meaning, pages 245–283. Oxford University Press.

Papo, D., Buldú, J. M., Boccaletti, S., & Bullmore, E. T. (2014). Complex network theory and the brain. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1653), 20130520. doi:10.1098/rstb.2013.0520.

Sporns, O. (2010). Networks of the Brain. MIT Press.

Spivey, M. (2007). The continuity of mind. Oxford University Press, USA.

Tognoli, E., & Kelso J. A. (2014). Enlarging the scope: grasping brain complexity. Frontiers in Systems Neuroscience 8:122. doi: 10.3389/fnsys.2014.00122

Tognoli, E., & Kelso, J. A. (2014). The metastable brain. Neuron, 81(1), 35-48.


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