- How does Attention/Living Algorithm synergy create Meaning?
- Primary Function of Attention: Filtering out Random Noise
- Data Stream Intensity differentiates Random from Sustained Signal.
- Data Stream Density also differentiates Random from Sustained Signal.
- Attention attunes to Data Stream Accelerations (Intensity & Density) to screen out Random Signals
How does Attention/Living Algorithm synergy create Meaning?
Life, the embodiment of biological systems, is the urge to self-actualize.
In the previous article on the Primacy of Attention, the Author employed a particular train of logic to establish that Life, the embodiment of biological systems, requires the Attention/Living Algorithm synergy to create meaning from the sensory input of raw environmental data. Life, in this context, is the urge for self-actualization, which frequently, though not always, includes the urge for survival. As the urge for self-actualization, Life moves with intention. Life operates on each level of the biological hierarchy from micro to macro. To achieve their multitude of goals, living systems must constantly monitor and then adjust to environmental circumstances. Some claim that this process is always automatic – based upon the stimulus/response metaphor. Others, including the Author, feel that living systems have the capacity for informed choice in certain situations (delineated in future chapters).
Life requires Attention to determine which data streams for the Living Algorithm to digest.
Whichever view is held, Life, as intentionality, needs a bridge that links her with the data streams generated by her sensory organisms. She requires a mathematical mechanism to translate raw data into a meaningful form. The evidence suggests that the Living Algorithm is a plausible candidate for this position. The Author further reasons that Life, as the urge to self-actualize, also requires Attention to automatically sift out relevant from irrelevant information. In this mode, Attention acts as Life’s censor. This essential service weeds out the inessential. This process streamlines Life’s decision-making ability by limiting information overload that can lead to inefficient and inappropriate responses.
To provide ongoing, up-to-date information, the Living Algorithm/Attention synergy must act automatically.
How does Attention provide this service? The Author suggests that Attention acts in concert with the Living Algorithm to weed out the inessential and provide Life with significant information. This digested information allows Life to satisfy her innate urge to fulfill her potentials. The fragility of survival combined with the volatility of environmental conditions requires a certain urgency to the living response. To provide the organism with a timely flow of up-to-date and relevant information that is required for an immediate response, the Attention/Living Algorithm synergy must operate relatively automatically. Accordingly, this part of the process is mechanistic.
Life can tweak Attention, not the Living Algorithm
So where does Life, the urge for self-actualization, exercise her presumed ability to choose? The Living Algorithm, as a presumed part of the biological operating system is fixed – no tampering. However, Attention acts as the go-between with the Living Algorithm. In this capacity, Attention examines sensory input via the Living Algorithm. She then selects which data streams to focus on and which information to pass on. Accordingly, Life employs Attention as an automatic interpretative mechanism. In similar fashion, businesses employ automatic computer algorithms to provide them and their consumers with up-to-date sales information. In this sense, Life can tweak and adjust Attention's automatic mathematical processes in an uncountable number of ways. In a similar fashion, those that mine sales information can tweak and adjust the automatic algorithms to provide the business with the most relevant information that will presumably increase profits. Similarly, the Attention/Living Algorithm synergy provides Life with timely information that can provide assistance in her quest for self-actualization.
Logical Theories require confirming Evidence
Yet, this is all theoretical speculation. It is easy to construct a logically consistent framework. History is filled with great theories that no one remembers or bothers to pass on. Why? Empirical evidence contradicted their logically consistent conclusions. Science only transmits theories that fit the existing evidence.
Life, Attention & the Living Algorithm a meaning-making synergy: based upon definitions.
So far, all we have is the general theory that Life, Attention, and the Living Algorithm form a meaning-making synergy. The reasoning behind this hypothesis is primarily definitional in nature. For instance, we have defined Life as the urge for self-actualization, the Living Algorithm as her mathematical method, and Attention, as Life’s filter. The relationships between these constructs are almost self-evident. Hence our argument is somewhat tautological in the sense that the definition is the argument.
To provide efficacy to the theory must understand Attention’s Mathematical Method.
Before this theory can have any practical efficacy, there are a few questions that must be answered. How does Attention employ the Living Algorithm as a mathematical tool to filter out inessential information? What are the mathematical mechanisms of Attention? How does the Attention/Living Algorithm synergy provide Life with meaningful information? And is there any empirical evidence to support this perspective?
Primary Function of Attention: Filtering out Random Noise
Attention determines whether a Data Stream has potential significance.
There is an infinite amount of imperceptible environmental information that falls outside of Attention's realm. Attention can only be applied to sensory input. The first task of Attention is to somehow examine this sensory input and then determine whether it is merely random noise or a potentially meaningful signal. In the process of recognizing meaning, substance is rendered to a particular flow of data. Attention deems whether a data stream has potential significance to the organism.
Life requires Attention to 'experience' a Data Stream.
Although our sensory mechanisms continually process environmental input, Life requires Attention (conscious or subconscious) to 'experience' a data stream. Without the organism's Attention, Life will not 'experience' the data stream. If Life does not 'experience' the data stream, it has no meaning to the organism. Without meaning, the data stream has no significance. In other words, Life does not make adjustments to data streams that she does not 'experience'. Attention, either conscious or subconscious, is required to impart significance (or substance) to a data stream. Lacking Attention, Matter does not have this ability. Possessing Attention, Life is able to 'experience' a data stream.
How does Attention determine significance?
How does Attention differentiate noise from meaning – when to attend to a signal and when not to? Each of our senses has evolved to attune to a particular type of environmental information. For instance, our eyes are able to perceive a specific range of the electromagnetic spectrum, while our ears pick up auditory signals. Our senses turn the environmental information into digestible data streams. The task of Attention is to determine whether the sensory data streams have potential meaning or are just background noise. In the constant barrage of sensory input, how do we determine what to pay attention to? What are the consequences of this attention? In other words, at what point does Attention pass a data stream on to Life, so that she can 'experience' the information behind the signal?
Example: How do forest creatures sift out lush background noise from meaningful signal?
For instance, an incredible array of sensory perceptions - rustling leaves, soft breezes, and light flickering through the branches – bombards the senses of forest creatures. How does Attention filter out this complex background noise from information that could be meaningful to the organism – for instance, information regarding food, predator or sexual partner? The accurate analysis of the potential significance of these data streams is crucial to survival. Why?
Determining potential significance enables an adjustment (a response to the stimulus).
This analysis determines a potential response. The basic choice is whether to do nothing in terms of a random signal or to do something in response to a meaningful signal. First, Attention determines whether the sensory data streams have potential meaningful. Then Life makes a decision as to a potential response – for instance, whether to attempt to eat the source of the data stream, mate with it or run for the hills. Identifying food, partners or danger is based upon the ability to ignore (screen out) random signals and to recognize and focus upon a particular signal (or data stream). Currently, we are only interested in how an organism distinguishes a random from a sustained or organized signal. Once we have established a potential filter then we can tackle the consequences.
Data Stream Intensity differentiates Random from Sustained Signal.
If the first task is to differentiate noise from meaning, what mathematical filter could be used? Wouldn’t there be a different filtering technique for each type of input? Wouldn’t the organism develop a unique filter for each circumstance? Or is it possible that the organism could employ the simplicity of one filtering method to cover multiple situations?
How does Attention employ Living Algorithm to screen out noise?
We have suggested that the Living Algorithm is Attention's mathematical tool. If so, how does Attention employ the Living Algorithm to sift chaos from meaning? What automatic mathematical process could be employed to differentiate meaning from noise?
Living Algorithm provides sufficient information to filter out random signals.
The Living Algorithm’s algorithm provides all the relevant information that is required to differentiate a random from an organized signal. One of the results of the Living Algorithm's digestive process is the Directional, the data stream’s acceleration. For ease of discussion, we will call the Directional a measure of the relative intensity of a data stream. Focusing Attention upon the data stream’s intensity enables Life to differentiate a random from a sustained, or organized, signal. Let us see how this might occur.
Graph: Triple Pulse vs. Random
Let us look at a graph that contrasts the relative intensity of a highly organized data stream, the Triple Pulse, with a random data stream. It is visually apparent that the organized signal (the red background) generates an orderly pulse that rises far above the chaotic spurts generated by the random signal (the blue foreground). The intensity of a random signal rarely rises above 0.1 (10%), and if so only briefly. In contrast, the intensity of the sustained signal quadruples this value, almost reaching 0.4 (40%).
Setting a threshold of intensity – To attend or not to attend
When a signal rises above 10% in intensity, it is highly probable that the signal is organized and needs to be attended to. In fact, the higher the data stream’s intensity, the less likely that it is random. If Attention’s filter threshold is set at 10%, this would allow the organism to differentiate a random from a sustained signal. When the intensity is above 10%, the signal has potential meaning. When it is below 10%, it is just noise. Attention could apply this threshold to determine which data streams to filter out and which to pass on to Life.
Data Stream Density also differentiates Random from Sustained Signal.
Data Stream Density: Random 0%, Sustained rises to 100%
There is yet another way in which the information provided by the Living Algorithm could be employed to differentiate a random from a meaningful signal. In a prior article, the Author developed the notion of Energy Density in a data stream. Energy Density is a function of the simple ratio of the Deviation to the Range of a data stream. The Deviation is another of the Living Algorithm's measures, The Density of a Random Data Stream hovers around 0%, while the Density of a sustained data stream, such as the highly organized Attention Pulse sequence, approaches 100%. The red line at right is a graphic representation of the Energy Density of the Pulse of Attention. The blue line represents the Energy Density of a random data stream. Note that it never rises above 5% Density and instead hovers around 0%.
Data Stream Density: a second random/meaning filter
As such, the simple ratio (Deviation/Range) that determines Energy Density also provides an easy method to differentiate a random from an organized signal. The higher the Data Stream Density, the less likely it is to be random noise. Conversely, the lower the Density the more likely it is random. The ability to differentiate noise from potential meaning is the first essential step towards the crucial capacity for pattern recognition. Attention could employ this technique as an additional filter to determine which data streams to pass on to Life.
Two filters for cross-checking: Intensity and Density
An organism's Attention could easily employ the first technique (Data Stream Intensity) as a detection device and the second (Data Stream Density) to confirm the assessment of the signal. When the data stream's intensity rises above 10%, the Attention passes the signal on to the organism. If the data stream's density is also rising, this is a further indication that the incoming information could be meaningful. In contrast, a random data stream's density does not rise consistently, but hovers instead around 0%. The two methods of differentiating a random from a meaningful signal could be employed as a form of cross checking. Living systems constantly employ this type of redundancy to affirm their notions of reality.
Attention attunes to Data Stream Accelerations (Intensity & Density) to screen out Random Signals
What do intensity and density consist of mathematically? The Deviation and the Directional are employed to compute the intensity and density of a data stream. Both are measures of the data stream's acceleration. One acceleration is a scalar (a quantity); the other is a vector (a directed quantity). Accordingly, Attention must be attuned to the acceleration of a data stream in order to differentiate a random from a sustained signal. The Living Algorithm provides this information. When the intensity and density of a data stream continue to rise, Attention automatically passes on the information to Life. Cross checking redundancy from other sources, Life decides whether or not to 'experience' the data stream.
Attention ignores Data Stream Velocity, as no difference between Random & Ordered.
In contrast, there is no notable difference between the velocity of a random data stream and the velocity of an organized data stream. Remember, the Living Algorithm's Living Average represents the data stream’s velocity. Attuning to a data stream's velocity would not allow Attention to filter out a random signal from a significant one. Accordingly, to provide a filtering mechanism, Attention must be attuned to a data stream's acceleration, not its velocity.
Attention attracted to Data Stream Acceleration, which leads to Data Stream Power.
This contrast provides one reason why Attention could be innately attracted to a data stream's acceleration and ignores its velocity. Our discussion of Data Stream Dynamics provides another impelling reason for Attention's preferences. Data Stream Acceleration leads to Data Stream Force, Work and Power. We have even suggested that when a Data Stream operates at peak capacity that it has the ability to interact with the material world via the accumulation of info energy density.
Example: a Squirrel digests the Dark & Light Patterns of the Forest.
Let us look at a concrete example of how this filtering process could be employed. A squirrel's eyes are attuned to the dark and light patterns of the forest. According to our model, the squirrel's sensory mechanism picks up non-stop environmental information and turns it into a data stream. To accomplish this task, the squirrel’s visual network takes readings from the stream of information at regular intervals.
Single threshold creates binary data stream
Let us also suppose, for ease of analysis, that the data stream is binary, consisting only of 1s and 0s. This assumption is not unreasonable. The squirrel's cognitive mechanisms could set a single balance point based upon experience. This threshold would determine whether the increments of the incoming information stream are valued at 1 or 0. Environmental information that is above the threshold is recorded as a 1, while any value below the threshold is recorded as a 0.
When both Intensity & Density Rise, Attention passes Living Algorithm's Info to Life
As long as the forest light is flickering from slight breezes, the data stream of 1s and 0s is random. However, if an object, whether it be predator, food, or sexual partner, blocks the light coming to the eyes, the random binary string turns into a continuous string of ones. As long as the light flickers and the data stream is random, the data stream's acceleration also flickers. Both the intensity and the density of the random data stream remain low. As long as these measures remain low, Attention does not pass on visual information to the squirrel. However, as soon as an object continuously blocks or reflects the sun’s light, the data stream signal is sustained and the acceleration rises. As the acceleration rises, the intensity and density of the data stream also begin to rise. When the threshold is triggered, Attention passes on this preliminary information to the squirrel. If the signal is sustained long enough, the squirrel actually 'experiences' the information of the data stream. After 'experiencing' this information, the squirrel then must decide what to do.
What does it mean to ‘experience’ a data stream? We certainly don’t know. To find out, read the next article in the stream – The Importance of Repetition for Experience.