The Understanding Capacity and Information Dynamics in the Human Brain
Abstract
:1. Preview
- (1)
- Self-organization in the neuronal substrate;
- (2)
- Information production; and
- (3)
- Optimization of the organism–environment interaction.
2. Introduction
2.1. The Physical and the Mental
- (a)
- Material (physical) objects = ;
- (b)
- Mental objects = ;
- (c)
- Relationships between the material and the mental objects, responsible for making the former accessible to the latter (i.e., intelligible) .
2.1.1. Physical Objects
2.1.2. Mental Objects
2.1.3. Requirements for Intelligibility
- The physical world is intelligible because it has a degree of order and consistency;
- Processes in the brain serve to apprehend that order and apply the results in regulating behavior;
- Efficient regulation is predicated on the availability of reversible operations performed on distinct (segregated), (quasi)stable, and flexible informational structures (mental objects).
2.2. Evolution of Regulatory Mechanisms
- (A)
- Primitive organisms. Regulation is confined to the boundary surface (e.g., opening or closing surface channels to allow or block access to the organism’s internals).
- (B)
- Animals. Regulation expands to the immediate surrounds and manifests in a range of behaviors extending from simple reactions (e.g., sea slugs extending or withdrawing their gills) to complex predatory or foraging behaviors in higher animals. In all cases, acting on conditions external to the surface involves establishing direct contact with the surface (reaching, grabbing, clawing, biting, etc.).
- (C)
- Humans. Regulation expands to outside conditions separated from the organism’s boundary surface by indefinitely large intervals. As formulated by Bertrand Russell: “…the essential practical function of “consciousness” and “thought” is that they enable us to act with reference to what is distant in time and space, although it is not at present stimulating our senses” [12].
2.3. Principles of Understanding
- (1)
- Self-organization feeds on energy. More precisely, “the flow of energy through a system acts to organize that system” [13]. Entropy H of an open system connected to an energy source and energy sink is determined by entropy of the system and cumulative entropy of the source and the sink , H = +. According to the second law, + 0. Energy flow from the source to the sink leads to increasing entropy in the source-sink subsystem, > 0. The only demand on entropy change in placed by the second law is that . Accordingly, entropy decreases in are permitted, under the condition that system is open and serves as a conduit for energy flow [13].
- (2)
- Self-organization takes place in open systems driven away from equilibrium (“dissipative systems”) [14,15], and proceeds through phase transitions accompanied by entropy reduction and symmetry changes [16,17]. The rate of entropy generation declines as systems relax toward steady states [18]. Changes of symmetry manifest, for example, in the formation of Benard cells when molecular mechanisms of heat transfer are replaced with convective heat transfer. In general, symmetry breakings accompany transitions from disorganized movements of individual (micro) units to collective movement of ensembles comprised of multiple units (as, for example, during transitions from laminar to turbulent flow in liquids [19,20]).
- (3)
- Information absorption entails entropy reduction and extraction of free energy [21,22,23,24]. The notion dates back to the realization that measurements yielding information dI about a physical system cause entropy increase inside that system [21]. Reciprocally, absorbing information dI reduces entropy dH in the receiver, accompanied by extraction of free energy dF and conversion of heat into work dW. Roughly, the argument is as follows [24].
- (1)
- Understanding is a product of self-organization in the neuronal substrate, involving self-directed construction and manipulation of mental models.
- (2)
- Models are composed of quasi-stable neuronal groupings (packets).
- (3)
- Mental modeling involves work and is predicated on supplying free energy in the amounts sufficient for performing that work. The human brain regulates extraction of free energy from the environment and diverts a part of it towards the work of mental modeling.
- (4)
- Modeling produces information. Absorbing information from the environment equates to negentropy extraction, and reducing entropy as a result of internal information production equates to negentropy generation.
- (5)
- The process is self-catalytic, in the sense that modeling stimulates information seeking on the outside (significant information) that facilitates increasing order on the inside, via formation of new models, and expanding and unifying the already formed models.
- (6)
- Mental models function as synergistic complexes, focusing energy delivery and obtaining large amounts of work at low energy costs (high-cost attentive changes in any component produce mutually coordinated, low-cost changes throughout the model).
- (7)
- Modeling yields a quantum leap in regulatory efficiency by improving energy extraction from the outside (better predictions and robust response construction in unforeseen circumstances) and reducing unproductive energy expenditures inside the system.
3. Theory of Understanding: How the Intelligible World Arises from Sensory Flux
3.1. Neuronal Packets and Their Role in Understanding
3.1.1. Neuronal Packets: The Building Blocks of Understanding
3.1.2. Perception and Recognition
3.1.3. Apprehending Behavior
3.1.4. Rudimentary Understanding
- (1)
- Objects are in the immediate proximity of the animal (within the sensory-motor feedback loop);
- (2)
- Objects have familiar properties and are proximal in space and time, that is, have been co-occurring in the animal’s past history (accordingly, the corresponding packets occupy proximal positions in a small neighborhood in the packet network);
- (3)
- Manipulations are within the envelope of instinctive (genetically determined) responses (e.g., reaching, pulling, dragging, grabbing, etc.).
3.1.5. Cognitive Revolution: Emergence of Human Understanding
- (1)
- Coordinating packet vectors across unlimited spans in the packet network; and
- (2)
- Conducting such coordinations without motor-sensory feedback (i.e., while withdrawing from sensory inflows and suppressing overt motor activities).
- (1)
- If packet corresponds to a currently perceived object A, establishing coordination allows one to attribute causes of A’s behavior to some object B, which is not amenable to perception;
- (2)
- suggests the use of object B and deployment of coordination in order to produce some desired changes in the behavior of object A;
- (3)
- allows prediction of changes in B following changes in A;
- (4)
- Exercising coordination simulating interaction between ;
- (5)
- A coordinated pair becomes a functional unit → that can be coordinated with other units ( ) → , and so on;
- (6)
- Establishing coordinations equates to production of information, yielding reduction of entropy, and a growing degree of order in the system. Accordingly, information can be sought that facilitates entropy reduction, via self-directed construction, expansion, and integration of models;
- (7)
- Establishing coordination is experienced as attaining understanding, or grasping the meaning of behavior variations in A and B;
- (8)
- Understanding enables explanation.
- (1)
- Understanding entails entropy reduction, i.e., the resolution of uncertainty or expected surprise. Establishing relations (e.g., switch controls bulb) amounts to forming dependencies between packets, which is accompanied by producing information and reducing entropy,
- (2)
- Understanding entails generalization, i.e., an increase in the marginal likelihood of internal models following a reduction in model complexity. Grasping a relation in a particular process enables transferring it to a variety of other processes different from the original one (e.g., having comprehended that switch controls bulb, the person can figure out how to handle desk lamps, floor lamps, fans, or other devices operated by switches, etc.). As formulated by Piaget:“…the subject must, in order to understand the process, be able to construct in thought an indefinite series ….and to treat the series he has actually observed as just one sector of that unlimited range of possibilities”.[6] (p. 222)
- (3)
- “Understanding brings out reason in things” [6] (p. 222), and thus enables explanations (“the bulb turned on because this switch controls it and it was turned up”).
- (4)
- Most importantly, understanding makes it possible to overcome the inertia of prior learning, and thus enables coping with disruptive changes and unprecedented conditions. Technically, intrinsic to modeling is the possibility of constructing, in thought, various packet groupings until a composition emerges fitting the situation at hand, and thus allowing explanation and prediction.
- (1)
- Intelligence derives from biophysical mechanisms allowing self-directed construction of mental models, establishing coordinated activities in neuronal packets residing in different domains in the packet network. Cognitive functions enabled by the mechanism range from figuring out methods for handling physical objects in order to achieve some desired objectives, to formulating scientific theories defining coordination between abstract variables.
- (2)
- Modeling entails entropy reduction. As a result, the process motivates extracting and producing information that is conducive to further entropy reduction, and thus has intrinsic worth to the system. Accounting for internal information production modifies Equation (1).
- (3)
- Along with maximizing intrinsic significance, modeling serves to maximize extrinsic value (utility) by supporting “mental simulation”, thus reducing prediction errors (minimizing variational free energy [11]). To underscore: In feedback-controlled coordinations, information has no intrinsic worth independent of the external conditions it signifies. Decoupling from feedback gives rise to intrinsic worth commensurate with the degree of entropy reduction the information obtains. The pursuit of intrinsic worth involves re-organizing and unifying mental models and seeking information that is subjectively significant, that is, conducive to further entropy reduction. Intrinsic worth motivates cognitive effort in search of understanding.
- (4)
- The overall functional organization of the regulatory system is hierarchical, as shown below.
Synaptic wiring at the bottom gives rise to informational hierarchy, where each upper level is produced by operations on the lower one. The hierarchy extends upward indefinitely (models comprising models comprising models...).
- (5)
- The world is intelligible because its representation is constructed by the same mechanism that is employed in the attempts to understand it. Intelligibility does not equate to ready understanding, it only implies that understanding can be reached eventually with effort.
3.2. Supportive Experimental Findings
3.2.1. Neuronal Packets—Are They “Real”?
3.2.2. Mental Modeling: From Fitting Sticks to Landing on the Moon
“There is, in fact, a very appreciable difference between the two types of co-ordination, the first having a material and causal character because it involves a co-ordination of movements, and the second being implicative. The co-ordinations of actions … must proceed by systematic steps, thus ensuring continual accommodation to the present and the conservation of the past, but impeding inferences as to the future, distant spaces, and possible developments. By contrast, mental co-ordination succeeds in combining all the multifarious data and successive data into an overall, simultaneous picture, which vastly multiplies the powers of spacio-temporal extension, and of deducing possible developments”.[6] (p. 219)
“In large complexes, each of which hangs together as a … functional or dynamic unit. Such a complex, an interrelated knot of pieces … is to be considered as a unit of perception and significance”.[48]
“consists essentially of taking stock of the spatial, functional, and dynamic relations among the perceived parts, so that they can be combined into one whole”.[48]
3.2.3. Language
“We can picture Merge’s output as a kind of triangle—the two arguments of Merge form the two legs of the triangle’s “base,” and the label sits on “top” of the triangle”.[50] (p. 114])
“Language evolved as an instrument of internal thought, with externalization as a secondary process”.[50] (p. 74)
4. Aspects of Human Cognition
4.1. Landscape Navigation in Norm and Pathology
4.2. Cognitive Effort, Value Attribution, and Consciousness
4.3. Assimilation and Accommodation
4.4. Architecture for Coordination
4.5. From Self-Organization to Self-Realization
- (1)
- Genetics determines a person’s intellectual pursuits in the course of the life time and the ability to realize such pursuits within some range of condition variations;
- (2)
- Absence of the requisite conditions can arrest self-realization and cause frustration.
5. Summary and Discussion
5.1. Discussion: How Neurons Make Us Smart
“Observe that a high level of mechanization can be achieved in executing the algorithm, without any evidence of understanding, and a high level of understanding can be achieved at a stage where the algorithm still has to be followed from an externally stored recipe”.[104]
“Abduction … is an inferential step … including preference for any one hypothesis over others which would equally explain the facts, so long as this preference is not based upon any previous knowledge bearing upon the truth of the hypothesis, nor on any testing of any of the hypotheses, after having admitted them on probation”.[105]
5.2. Clarifications and Definitions
5.2.1. The Brain Operates as a Resource Allocation System with Self-Adaptive Capabilities
5.2.2. Attention
“An attentional mechanism helps sets of the relevant neurons to fire in a coherent semi-oscillatory way … so that a temporary global unity is imposed on neurons in many different parts of the brain”.[123]
5.2.3. Motivation
5.2.4. Understanding in Humans and Animals
5.2.5. Neuronal Substrate of Relations
5.2.6. Relations as Objects
5.2.7. Thinking
5.2.8. Dynamics of Thinking
5.2.9. Learning with and without Understanding
5.2.10. Meaning and value
5.2.11. Neuroenergetics
5.2.12. Gnostron
5.3. Further Research—A Fork in the Road
“Without offending against the principle of entropy in the physical sense … all organic creation achieves something that runs exactly counter to the purely probabilistic process in the inorganic realm. The organic world is constantly involved in a process of conversion from the more probable to the generally more improbable by continuously giving rise to higher, more complex sates of harmony from lower, simpler grades of organization”.[147]
- Converting excessive heat into work;
- Biasing ATP hydrolysis towards accelerating release of Gibbs free energy and inhibiting release of metabolic heat;
- Reducing Landauer’s cost of information processing (by regulating access in the landscape).
(please recollect that understanding was presumed to play a marginal role in problems solving, if any [104], as seen Section 5.1.) Neuropsychological theory of understanding has a dual objective of: (a) analyzing performance benefits conferred by the understanding capacity, and (b) elucidating the underlying neuronal mechanisms, aiming at representing them within a unified functional architecture (architecture for understanding) (e.g., contingent on further analysis, the architecture might account for recent findings indicating that processing of plausible and implausible data engages different pathways in the brain [160]). The theory needs to be broad enough to allow comprehensive analysis of the role played by understanding in different manifestations of intelligence (“multiple intelligences” [161]).“Unified theories of cognition are single sets of mechanisms that cover all of cognition—problem solving, decision making, routine action, memory, learning, skill, perception, motor activity, language, motivation, emotion, imagining, dreaming, daydreaming, etc. Cognition must be taken broadly, to include perception and motor activity”.[159]
5.4. Digest
Acknowledgments
Conflicts of Interest
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Yufik, Y.M. The Understanding Capacity and Information Dynamics in the Human Brain. Entropy 2019, 21, 308. https://doi.org/10.3390/e21030308
Yufik YM. The Understanding Capacity and Information Dynamics in the Human Brain. Entropy. 2019; 21(3):308. https://doi.org/10.3390/e21030308
Chicago/Turabian StyleYufik, Yan M. 2019. "The Understanding Capacity and Information Dynamics in the Human Brain" Entropy 21, no. 3: 308. https://doi.org/10.3390/e21030308
APA StyleYufik, Y. M. (2019). The Understanding Capacity and Information Dynamics in the Human Brain. Entropy, 21(3), 308. https://doi.org/10.3390/e21030308