**Section Headings**

Understanding Data Energy -> Fulfilling Potentials

Mathematics of Energy: Work Ability & System Dependent

Energy: a Construct of Newtonian Dynamics

Kinetic & Potential Data Stream Energy

2 kinds of Mental Energy: Accumulation & Assimilation

Application to 10-Minute Attention Span & Core Concept Lectures

*What is the relationship between Data Based Energy and Attention? Why is this significant? The internal logic of Data Based Energy seems to provide a good model for the internal logic of Attention’s mental energy. A better understanding of DS Energy could lead to a better understanding of mental energy, which could assist us to better fulfill our potentials. *

*Although we don’t know what energy is, we know what it can do. Energy has the ability to change the state of a system. Data Energy has the ability to change the state of Living Algorithm’s (LA) mathematical system. *

*Scientists have found it useful to divide energy into 2 parts, kinetic energy that does work and potential energy that is stored for later use. We have found it useful to employ similar constructs in the LA System, i.e. active (kinetic) and residual (potential) energy. *

*A portion of the entering Data Energy is employed to change the state of the System (active energy). The remainder is stored in the System to be employed at a later date (residual energy). At any moment, the System can be characterized by the percentage of residual energy it contains. *

*According to our Theory of Attention, active energy corresponds with the mental urge to accumulate information or experience, while residual energy corresponds with the mental urge to assimilate these experiences. The LA mathematics reveals how these complementary urges guide and dominate our lives on a day-to-day level. *

* Our Sleep/Awake cycle provides us with a good example. As we accumulate information and experiences (active energy) during the day, the urge to assimilate (residual energy) rises. When the assimilation urge passes a certain threshold, we go to sleep. This downtime from the data accumulation phase provides us with an opportunity to assimilate the day’s experiential harvest. Upon waking, the residual energy is depleted and the assimilation phase transitions into the info accumulation phase of this cycle. *

How can data possess energy? What a strange and intriguing concept!

Energy itself is a mysterious idea. Although difficult to define, we can understand the notion of energy through intuition and experience. In a similar vein, we can sense that information displays some of the same potent properties as energy.

We can understand energy through its effects upon the world. Indeed, scientists have developed many equations that enable us to tap into the power behind wind, water, the sun, fossil fuels, etc. These equations suggest that there is a definite logic associated with physical energy. This logic consists of key components and their significant interactions.

First and foremost, energy has the capability to change the state of a system. For instance, electrical energy can affect the lighting in our homes and heat energy can change the temperature of a room. Because the character of the Data changes the results of a mathematical system, it can be viewed as a type of energy. As we shall see, the internal logic of physical energy is quite similar to the components and the relationships associated with Data Energy.

Logic of Physical energy ≈ Logic of Data Energy

However, there is a huge difference between the two types of energy, i.e. material and mathematical. Physical energy has the ability to exert an influence upon the physical world. In contrast, Data Energy is a purely mathematical construct that exerts an influence exclusively upon a mathematical system. The difference between the two types of energy is like the difference between market trading and simulated trading. One has real world consequences, while the other is a game.

Physical energy -> Physical World

Data Energy -> Mathematical System

What’s the significance? Although Data Energy is purely mathematical, its internal logic seems to provide an excellent model for the mental energy of Attention. In fact, the rhythms of Attention seem to be aligned with the mathematical rhythms of the LA’s System.

Logic of Data Energy ≈ Logic of Attention’s Mental Energy

According to common sense and our Theory of Attention, mental energy has the capacity to exert an effect upon the real world. For instance, we direct our hand and fingers to grasp an apple and then draw it to our mouth to satisfy our hunger. Alternately, we could choose to resist the urge to eat the apple. Most of us believe and act as if we employ our mental energy to direct our body to exert an influence upon material reality. Could it be that our mental energy is converted into biological energy? It certainly seems so.

Mental Energy -> Biological Energy -> Physical World

If our suppositions are true, the mathematics of Data Stream Energy reveals the internal logic of mental energy, which interacts with physical energy to influence reality. If so, understanding the mathematics of Data Energy could enable us to better understand how mental energy influences our lives.

Understanding Data Energy -> Understanding Mental Energy

With a better comprehension of mental energy, we can better align with the natural rhythms of Attention. This harmonization assists us to minimize problems and maximize potentials. Despite the purely mathematical nature of this exercise, it seems to have real world applications.

Alignment with Rhythms of Attention -> Maximizing Potentials

In order to understand the relationship between Attention and Data Stream Energy, we must first understand the justification for associating the word ‘energy’ with data. What features do physical energy and data energy share in common?

On the meta-level, mathematics reveals the logic of energy in both realms, i.e. material and numerical. This is not a discussion regarding a philosophy of energy or an experience of energy. These perspectives certainly have their own validity and areas of application. This article only pertains to the logic behind the mathematics of energy.

In this context, there are 3 significant similarities between our 2 realms.

First, energy, as mentioned, is the ability or the potential to do work. This work either changes or maintains the state of the system. For instance, we employ personal energy to both ride a bike and to hold our body upright. In one case, we are moving and in the other, we are standing still. Data Energy also has the ability to do work.

1) Energy = Potential or Ability to do Work of Changing or Maintaining System

There are many kinds of energy that power different kinds of systems. For instance, our bodies employ biological energy; our computers run on electrical energy and our cars use mechanical energy. Living systems seem to possess both biological and mental energy. The LA’s mathematical system runs on Data Energy.

Second, energy always belongs to a system. There is no such thing as free-floating energy. It could be said that energy is system dependent. Electricity operates in an electrical system, such as a refrigerator. Mechanical energy operates on a machine, for instance a bike. Data Energy operates in a numerical system. None of these energy types are waiting around for a system to appear.

2) Energy is System Dependent.

Just as energy comes in many forms, there are many different types of energy systems, for instance material, living or even mathematical, as in this discussion. Data Energy operates upon a mathematical, not a living or material, system. Indeed Data Energy only exists in the Living Algorithm’s mathematical system.

Further, energy is not in a permanent state like mass or location. It does not have any absolute content. Instead it is linked to the context of its system. For example, pushing a boulder up a hill builds potential energy due to the earth’s gravity. However, the amount of potential energy is dependent upon the location of its resting elevation. Let us suppose that our boulder is pushed past the peak into a high valley. Its potential energy is less than if it is on a peak, even though the elevation could be equal. Another example: the potential destructive energy of a wild fire is dependent upon the surrounding fuel.

System’s context determines amount of Energy.

We mentioned that Data Energy and Physical Energy share 3 significant features in common. Both types of energy have the ability to do work and are also system dependent. The third commonality binds them as siblings in the same family.

Both Data Energy and Physical Energy are inextricably linked to Newtonian Dynamics. In both cases, energy is a mathematical construct that is in a distinct relationship with other mathematical constructs. Just as it is not independent of a system, energy is not independent of the mathematics of Newtonian Dynamics.

The Living Algorithm System consists of an interlocking network of data streams. Data Stream Dynamics (DSD) was developed to better understand the operations of this network. The traditional constructs and relationships of Newtonian dynamics provided the inspiration for DSD. Indeed, the inner architecture of constructs of both mathematical systems is nearly identical. (See *DSD Notebook* for details.) In the terms, we’ve been employing: The internal logic of DSD is similar to the internal logic of traditional dynamics. Energy, of course, is a significant component of both Newtonian dynamics and DSD.

Logic of Newtonian Dynamics ≈ Logic of Data Stream Dynamics

While both dynamical systems share a similar logic, they apply to orthogonal realms of existence, i.e. intersecting planes of reality. Traditional Newtonian dynamics does a great job characterizing the relationship between Physical Energy and Material Behavior. This capability enables modern technology.

Newtonian Dynamics: Physical Energy <-> Material Behavior

In parallel fashion, DSD is able to characterize the relationship between Mental Energy and Living Behavior. This knowledge leads to a technology of Attention.

Data Stream Dynamics: Mental Energy <-> Living Behavior

The relationships of both dynamical systems to reality, i.e. material and living, are best understood through the constructs of traditional Newtonian dynamics^{1.}. For instance, they both employ similar constructs in similar relationships, i.e. Work and Power.

Scientists have found it useful to think of energy as coming in two forms: kinetic and potential. Kinetic energy is used to change or maintain the state of the system, while potential energy is stored in the system to be used at a later time. The relationship between kinetic and potential energy is incredibly important in Newtonian dynamics. As might be imagined, these constructs and their relationship are equally important in the LA’s System.

In other words, the incoming Data Energy is not completely absorbed into the System. Some of the Data Energy does work immediately, while the remainder is stored to do work at a later time. Kinetic (active) energy is the name we give to the incoming Data Energy that does work upon the current Mean Average. Potential (residual) energy is the name we give to the portion of incoming Data Energy that does work on future Mean Averages. As the leftover Residual (potential) Energy is stored in the data stream, it could be called Data Stream Energy.

This residual energy could even be used to characterize the Data Stream. For instance, after the Nth iteration, the Data Stream contains a set percentage (e.g. high, low or none) of stored residual energy. As we shall see in subsequent articles, the notion of Data Stream Energy is very important as an explanatory tool with regards to living behavior.

Now that we have a better understanding of Data Energy, both kinetic and potential, in the LA System, let us see how these concepts play out experientially. (For the mathematical proofs regarding kinetic and potential energy in the Living Algorithm System, check out *Data Energy: Kinetic & Potential – Math Proofs*.)

Evidence of many types implies that Attention employs the LA to digest environmental data streams. With each iteration of the LA process, Attention invests a quantum (chunk) of mental energy in the data stream of choice. If our theories are correct, each quantum of mental energy should be equivalent to a single unit of data energy. Further, Attention’s Mental Energy should behave in similar fashion to Data Energy in the LA System.

As seen, after Data Energy enters the LA System, part of the energy does work (active) and part of the energy is stored in the data stream (residual). This mathematical approach appears to offer fertile conceptual ground. The mathematics behind the model suggests that 2 kinds of energy may be at work when the Conscious Mind, i.e. Attention, digests environmental data streams.

We hypothesize that mental energy has 2 components: information accumulation (active energy) and information assimilation (residual energy). Active mental energy is utilized when the Mind is observing and relating recent data. Residual mental energy is utilized when the Mind assimilates relevant information.

When we are conscious, we are continually engaged in the process of observing and relating information. This process utilizes the active component of mental energy. As the active energy is consumed, the assimilation energy (residual) builds. As long as we are conscious, the residual energy builds faster than it is consumed. In other words, the Conscious Mind, i.e. Attention, can’t keep up. The observing/relating process can only go on for so long. The information digestion system must shut down to completely consume this residual energy.

We speculate that this is the prime function of unconscious sleep. While unconscious, no fresh mental energy enters the system. This gives our unconscious cognition (subconscious processes) time to digest (consume) the residual energy that accumulated during consciousness. The observation/relating process provided food for thought, as it were. Mind needs time to organize and incorporate the information. To accomplish this vital task, Mind shifts modes from Conscious Awareness to Unconscious Sleep. Three experimentally-verified phenomena provide evidence for this interpretation: Sleep Deprivation, Sleep Necessity and Naps. In fact, the patterns of correspondence between the mathematical model and the experimental results even suggested this interpretation.

In analogous fashion, the truck arrives with cases of wine. The deliveryman places them in a holding area (the short-term memory). The employees must then organize the wine by unpacking the boxes and placing the bottles upon the shelves (the long-term memory). The deliveryman employs one type of energy (active/accumulation energy) to unload the boxes. The employees employ a different type of energy (residual/assimilation energy) to unpack and organize the bottles.

On the individual level, downtime (e.g. Sleep, meditation, naps, quietude) provides Mind with time to employ the assimilation (digestion) energy that builds during the accumulation phase. On the general level, the downtime of Sleep provides time to assimilate the all the accumulated residual energy of a single day.

Is there any other evidence for this interpretation of mental energy? Does it apply to any other feature of human behavior?

Psychology has what it calls the 10-minute rule. Evidently, humans can only pay attention to a lecture for about 10-minutes before losing the ability to focus. In brief, we have a 10-minute attention span when absorbing information. Evidently this finding is so widespread and common that psychologists call it a rule rather than a hypothesis.

What’s the solution? Dr. Medina argues that shifting core concepts every ten minutes gives the brain time to digest information:

"The most common communication mistakes? Relating too much information, with not enough time devoted to connecting the dots. Lots of force feeding, very little digestion. This does nothing for the nourishment of the listeners, whose learning is often sacrificed in the name of expediency." (Medina, *brain rules*, p. 88)

Dr. Medina's hypothesis regarding the mechanisms behind the 10-minute rule is very suggestive. He speculates that this ingestion/digestion metaphor applies to the 10-minute rule. He suggests that the audience is filled up with information after 10 minutes and needs time to digest the input. He further proposes aiding the digestion process by overt attention to ‘connecting the dots’ between his core 10-minute concepts. Dr. Medina’s noted lecture style appears to explicitly embrace an appreciation for a digestion process that produces a potent pulse of attention. Intriguingly, our mathematical model for information digestion has the potential to further explicate Dr. Medina's digestion metaphor.

Let's apply our analysis first to the 10-minute rule and then to Medina’s core concept approach. At the beginning of the presentation of a core concept, the residual energy associated with assimilation is non-existent. This assimilation energy grows during the 10-minute presentation. Perhaps this growing assimilation energy triggers a complementary urge to organize the accumulated information. Mind has taken in enough material on that particular topic and needs time to organize it. Put another way, the growing urge to assimilate competes with and then overwhelms the fading urge to accumulate. Mind can’t invest any more mental energy in this core concept and begins to fade.

Could the dual nature of mental energy be the mechanism behind the 10-minute rule and even our Attention span? It certainly seems plausible. How about the core concept approach?

After becoming filled up (satiated) with one core concept, Mind shifts the focus of Attention to another core concept. Mental energy is invested in this new area of focused information. Some of the mental energy (the active energy) is employed to accumulate information. (Could this accumulation change our brain's neural networks? Certainly plausible.)

The unused (residual) mental energy builds with each iteration (repetition) of the LA process. As the residual energy (the urge to organize) grows, it eventually overwhelms the active energy (the urge to accumulate). Mind leaves this particular urge to organize in storage and shifts Attention to yet another core concept.

The graph on the right exhibits the growth of residual energy over 120 trials (iterations). As mentioned, residual energy is associated with the urge to assimilate. Until it is utilized, this chunk of assimilation energy remains intact.

Shifting core concepts refreshes our Attention span. Yet each time that Mind shifts core concepts, the urge to integrate/assimilate the prior core concept is left behind. It remains as a pulse of residual mental energy (an unfulfilled urge to assimilate/integrate). Waiting patiently for its turn, this pulse of assimilation energy will not be forgotten.

Our entire waking day could be viewed as a series of core concepts. In order to refresh Attention with its limited span, Mind regularly shifts focus from one experience to another throughout the day. With each experience, the active portion of Attention’s mental energy is consumed, while residual energy accrues. Eventually the buildup of these urges to assimilate (organize) passes a threshold. At this point, shifting Attention to another experience (another modality) does not work any more. The urge to integrate the day’s experiences is too great.

The only solution is for Conscious Attention to shut down completely and cross the threshold into Unconscious Sleep. Without any fresh mental energy entering the system, Mind has the opportunity to fulfill the multiple assimilation urges that have amassed during the day. Downtime provides the necessary time to digest (integrate and relate) this accumulated information into our neural networks.

Just like the cycle of food consumption and digestion, this cycle of information accumulation and integration occurs as long as we are alive.

In conclusion, it seems that Data Stream Energy provides a good model for Attention’s mental energy in regards to Sleep, Psychology’s 10-minute rule, and Dr. Medina’s Core Concept Approach to lectures. Does this striking correspondence lend credence to the notion that mental energy obeys a different set of rules than does physical energy? Could these rhythms of Attention be determined by the operation of Data Energy in the LA System? Is it possible that this knowledge could assist us in our quest for self-actualization?

^{1.} For a more in depth discussion see the treatise: Data Stream Dynamics, Lehman 2010