Posner’s Attention Model & the Decay Factor

Posner’s Model of Attention

Trinity Model

Thirty years ago Michael Posner developed a model of attention. As the esteemed Dr. Medina reports in his book brain rules,

“Sometimes jokingly referred to as the trinity model, Michael Posner hypothesized that we pay attention to things because of the existence of three separable, but fully integrated systems in the brain.” (p. 78, brain rules)

3 Networks: Alerting, Orienting, & Executive

What are these “three separable, but fully integrated systems in the brain”? 1) Alerting or Arousal Network, 2) Orienting Network, and 3) Executive Network. Let’s briefly examine the functions of each network in turn.

1) Alerting or Arousal Network:

Medina likened the first system (Posner’s Alerting or Arousal Network) to the function of a security guard – surveillance and alert. This network has two states: Intrinsic Awareness and Phase Alertness. In the first state our attention only monitors general changes in the environment. In the second state our attention focuses upon specific environmental features.

Intrinsic Awareness (general attention) and Phase Alertness (specific attention)

The two phases alternate. In the first phase, general attention is engaged in the task of “monitoring the sensory environment for any unusual activities.” (Intrinsic Awareness).

“If the system detects something unusual, it can sound an alarm heard brain wide.” (p. 79, brain rules)

Once the alarm has been sounded, Intrinsic Awareness becomes Phase Alertness and the brain shifts from general to specific attention.

2) Orienting Network: Organism attends to unusual stimulus

After the specific attention has been triggered, the system orients toward the stimulus (the Orienting Network). All senses are alerted. The head turns toward the unusual input so that both eyes can focus upon the event and the ears can better attend to related sounds.

3) Executive Network: Life engaged

At this point the Executive Network is triggered. The signals are evaluated and a decision is made about what to do next. The question is: Does the non-conforming activity belong to a predator, prey or sexual partner? Once the determination has been made, a decision is made whether to escape, chase, or mate with it – or do nothing at all.

So much experimental verification Posner’s Model taken to be truth.

This trio of networks comprises the Posner Attention Model. This model has achieved widespread acceptance in the scientific community. As Medina reports:

“Posner’s model offered testable predictions about brain function and attention, leading to neurological discoveries that would fill volumes. Hundreds of behavioral characteristics have since been discovered as well.” (p. 79, brain rules)

It seems that multiple experiments have established that Posner's Attention Model exhibits significant patterns of correspondence with both neurological activity and human behavior. Due to the scientific community's widespread acceptance of the Posner's Attention Model, it seems safe to consider it a scientific 'fact'. Although these cross-disciplinary validations have firmly established the theory as a 'fact', cognitive scientists are at a loss to explain the causal mechanism behind the model. Why does it work?

This state of affairs is not unusual in the scientific endeavor. For example, it was centuries before Einstein was able to explain the causal mechanism behind Newton's gravity, i.e. why two planets could attract each other at a distance. (See The Occult Nature of Gravity?) Similarly, it was decades before the scientific community discovered that they could employ Mendel's genetics to explain the causal mechanism behind Darwin's evolution.

Scientific 'fact' frequently precedes an explanatory theory. In fact, when a 'fact' is not linked to a plausible theory, it is frequently called a scientific 'mystery'. For example, scientists consider the 'why' behind sleep to be a 'mystery'. Similarly the 'why' behind Posner's Attention Model is also a 'mystery'. Scientists are not entirely satisfied until they uncover the 'why' behind the 'what'. The remainder of this paper is devoted to providing a plausible reason for 'why' the Posner Attention Model works.

Section Headings


To make sense of the many patterns of correspondence between Information Dynamics and living behavior, we were 'forced' into considering the theory that living systems employ the Living Algorithm to digest information. If true, then organisms could have evolved to take advantage of the many features of the information digestion system. If so, then Life might have evolved biologiocal systems such as Posner's Attention Model in order to employ the Decay Factor as a means of regulating the focus of Attention. Evidence indicates that specific attention requires more mental energy than general attention. If true, then we can save mental energy by consciously diffusing attention and/or relaxing our mind to the revert to the default state of general attention. The striking correspondences lend further credence to the Info-Dynamics theory.

Review: The Living Algorithm & Information Dynamics

The Living Algorithm reveals the rates of change, the derivatives, of any data stream. This generates a mathematical system of Information Dynamics. There are many patterns of correspondence (list) between this mathematical system and many types of scientific 'facts', both neurological and behavioral. Most of these 'facts' are tied to attention and/or sleep-related behavior.

To make sense of these correspondences, we developed the Information Digestion Model. It is based upon certain assumptions, i.e. the Core Postulates. For instance, the many connections between mathematical and living behavior 'forced' us to consider the possibility that living systems digest information via the Living Algorithm. This is certainly plausible as the information digestion process reveals dynamical information patterns that are at the heart of meaning.

A simple observation reinforces this core assumption. Reflexivity is an innate and significant feature of living behavior. The Living Algorithm generates a reflexive mathematical system. (Note: the 'regular' equations of engineering and science are not reflexive.) It seems logical to assume that the best way to characterize Life's reflexivity is via reflexive mathematics. The evidence certainly validates this perspective.

If indeed living systems do indeed digest information via the Living Algorithm, then it seems reasonable to assume that Life would evolve to take advantage of the mathematical features and forms, i.e. the energy patterns, of the digestion process. Again, an abundance of evidence supports this perspective. For instance, the pulse-like nature of human experience, including neural activity, could easily be a method for living systems to take advantage of the Pulse, the most basic energy pattern of Information Dynamics. Also the waking/sleep cycle shows many patterns of correspondence with the Triple Pulse, another basic mathematical form.

As mentioned, there are many correspondences between Information Dynamics and attention. Does this model shed any light on Posner's Attention Model?

Decay Factor determines Contours of Info-Dynamics Landscape

The Decay Factor is a significant mathematical feature of the Living Algorithm. It plays a significant role in the meaning-making process. Let us review the Decay Factor's role and potentials.

Living Algorithm’s Living Average: a smoothed out version of the Raw Data Stream

The Living Algorithm's method of digesting information produces the Living Average of a data stream. If we visualize the Living Average and the Raw Data as lines on a graph, the contours of the Living Average line appear to be a smoothed out version of the Raw Data line. It is as if the Living Algorithm’s digestion process takes the rough edges off the data stream – chopping off the peaks and filling in the valleys of the line.

Decay Factor determines the severity of the Digested Data Stream's Contours.

What happens to our curve when we vary the Living Algorithm’s Decay Factor? Pictured at right are examples of the Living Average, a.k.a. the Decaying Average, with a selection of Decay Factors.

When the Decay Factor is set on 1 (the instantaneous range), the Living Algorithm’s digestive process is turned off and the information flow is not shaped in any way (Graph A at right). No patterns are revealed and the data remains a series of vertical lines without relationship to each other. When the Decay Factor is set on low (the volatile range), the data stream shaping process is minimal. Fewer peaks are cut off and fewer valleys are filled in (Graph B). Conversely, when the Decay Factor is set on high (the sedentary range), the shaping process is more extreme. More peaks are cut off and more valleys are filled in (Graph C). When the Decay Factor is set high enough (the eternal range), the shaping process transforms any data stream, no matter how variable, into a horizontal line; the data stream’s erratic nature is paved over entirely.

Generally speaking, the contours of the digested data stream are less extreme when the Decay Factor is greater. Conversely, the contours of the digested data stream are more extreme when the Decay Factor is smaller.

Decay Factor inversely related to shapeliness of Data Stream Derivatives

The Living Average is just one of the Living Algorithm’s products. The Living Algorithm's digestion process generates all the rates of change (the derivatives) of a data stream, not just the Living Average. The Decay Factor has a significant and similar effect upon each Data Stream Derivative. When the Decay Factor is set on high (the sedentary range), the Living Average curve is flattened out. Similarly, the Living Average's rates of change, the accelerations, are also leveled out. Conversely when the Decay Factor is set on low (the volatile range), the Living Average curve has greater contours. Similarly, the data stream's accelerations (the higher derivatives) also have greater contours.

Bluntly put, the Living Algorithm’s Decay Factor is inversely related to the shapeliness of the contours of the Data Stream Derivatives. Decay Factor up; Shapeliness down. And vice versa. (Note: When the Decay Factor is set at 1, the instantaneous range, there are no rates of change, hence no data stream derivatives.)

Regulating the Decay Factor to move between General & Specific Attention

General & Specific attention of Posner's Arousal Network

Let’s see how living systems could utilize this variable feature of the Living Algorithm’s Decay Factor to move between the 2 alternating phases of Posner's first network, the Alerting or Arousal Network. In the first phase – Intrinsic Awareness, attention focuses upon general features of the environment. In the second phase – Phase Alertness, attention focuses upon specific details. How do these alternating states of attention correspond with the Decay Factor?

Let us suppose that complex living systems have evolved to take advantage of the regulatory features of the Living Algorithm's Decay Factor. Is it possible that Life could utilize the Decay Factor to regulate the level of attention between Intrinsic Awareness and Phase Alertness?

High Decay Factor = General Attention of Posner’s Intrinsic Awareness

We imagine that the default state for the Decay Factor is high, i.e. in the 'sedentary' range. When the Decay Factor is set on high, the contours of the Data Stream Derivatives, i.e. acceleration and such, are minimized. The intensity of the environment is dimmed. Instead of catching every detail, this setting emphasizes the big picture. General patterns emerge, and individual patterns are obscured. With the Decay Factor set on high, Life only experiences the data streams that emerge from the background.

With the high default setting, attention focuses upon the general lay of the land rather than individual features. Instead of becoming entranced and distracted by the ever-changing colors of leaves from the flickering light of the forest, a high Decay Factor turns this information into background noise, as the general amount of change is low. A large Decay Factor maximizes the organism’s ability to survey the general environment. This strategy minimizes individual changes and enhances global changes. This state is similar to the general level of attention to the environment that accompanies Intrinsic Awareness – Posner’s first phase – surveillance or “monitoring the sensory environment for any unusual activities”.

Low Decay Factor = Specific Attention of Posner’s Phase Alertness

If an 'unusual activity' emerges from the background noise, Life could turn the Decay Factor down into the 'volatile' range to get a better look at what was happening. As the Decay Factor becomes smaller, the intensity of individual features is turned up. This state is similar to the specific level of attention to environmental stimuli that accompanies Phase Alertness – Posner’s second phase. There is a trade-off. The heightened intensity of particular events tends to obscure general patterns.

Life employs Decay Factor to regulate Attention

If Posner's Orienting Network determines that this 'unusual activity' is insignificant, the Decay Factor would presumably automatically rise back to the 'sedentary' range, its default position. In this way, living systems could employ the Decay Factor as a regulatory device, like a rheostat, in order to move from the general attention of Intrinsic Awareness to the specific attention of Phase Alertness and back again.

Example: An insignificant yell

For example, a yell might shift attention from a general level of awareness (Intrinsic Awareness) to a more specific level (Phase Alertness). However, once we orient towards the unusual stimulus and realize that it has no significance, our focus reverts to a state of general attention. Living systems could accomplish this transition by turning the Decay Factor down into the 'volatile' range to evaluate the yell, and then allowing it to rise to its default position once the potential threat passes. It is evident that living systems could easily employ the Living Algorithm’s Decay Factor to regulate attention between general and specific levels to better respond to environmental stimuli.

High Decay Factor setting is Energy Efficient

Why would the Decay Factor's default setting be on high, i.e. the sedentary range? Why couldn't organisms easily move from one setting to another? If living systems have really evolved to take advantage of the Living Algorithm's features, what survival advantage would this default position confer?

To make sense of the Sleepiness Phenomenon from the perspective of Information Dynamics, we suggested that mental energy is required to turn the Decay Factor down. After sleeping, we have an abundance of useable mental energy and can turn the Decay Factor up and down to attune to our environment. Sleepiness is an indication that we no longer have any mental energy available to move the Decay Factor down to attune to a specific level of awareness. In this state, we have a hard time reading or at least concentrating on the material. When tired, drivers become a menace on the road, as they no longer have the mental energy to focus attention by lowering the Decay Factor.

When no more mental energy is available for the conscious state, the Decay Factor rises into the 'eternal' range. No patterns are recognized. Attention flat lines and we go to sleep, i.e. enter the state of unconsciousness.

The 'sleepiness' analysis fits in neatly with the speculations of the current paper. If indeed organisms employ the Decay Factor to regulate the level of attention and if it takes mental energy to turn the Decay Factor down, then a default setting in the sedentary range would save mental energy. With the default setting on high, the organism would be able to attend to the general features of the environment and expend the least amount of mental energy. This could be why Intrinsic Awareness, i.e. general attention, with a high Decay Factor is the default position for attention. It saves mental energy.

Living systems that evolved to have a neurological network that could conserve mental energy in this way would tend to survive to pass on their gene pool for two reasons. First their mental energy might be sustainable over longer duration in order to attend to survival needs. Second, this form of conservation could free up mental energy for other pursuits. Instead of expending all available energy attending and responding to environmental stimuli, the organism could employ this energy in a problem solving fashion.

It seems reasonable to suggest that Posner's Attention Model with all its neurological components could have evolved to take advantage of both the focus-adjusting and energy-saving features of the Living Algorithm's Decay Factor.

Information Digestion Model applied to Posner’s 3 Networks

There is yet another synergy between Information Dynamics and Posner's Attention Model. Let's see how the Information Digestion Model could apply to Posner's trio of networks and their phases. There are 3 cognitive networks in Posner’s well-established Attention Model. We've already explored how Life could employ the variable feature of the Living Algorithm’s Decay Factor to shift between general and specific Attention, the two alternating phases of Posner's first network, the Alerting or Arousal Network.

High Decay Factor sets up Foreground and Background

 According to Info-Dynamics theory, Attention is attracted to information acceleration as a method of filtering out random data streams. Acceleration indicates that the signal is organized, i.e. not random noise. In the resting state of general attention, i.e. Intrinsic Awareness, a high Decay Factor smoothes out the sensory information. This smoothing process generates a steady environmental backdrop upon which changes can be more readily observed. When an organized signal, such as an intruder, enters the random leaf pattern and breaks into the foreground of Attention, the Decay Factor is lowered to better see the details – the state of specific attention (Phase Alertness).

Experience of Sustained Attention leads to Orienting and Executive Networks

If the signal is consistent and Attention is sustained for a long enough duration, the acceleration generates a Pulse. According to the theory, Life 'experiences' the information when the Pulse is completed. Again experimental evidence supports this perspective on both behavioral and neurological levels. According to recent research, sustained attention is required for 'memory consolidation'. This is why random Internet surfing leaves few memory traces. (See the article Shifting Attention & Information Dynamics.)

Even neurons require timed repetition to change their state. (For more on this topic, see the article The Importance of Repetition for Experience.)

This Experience sets the stage for a change of state. If the organism deems the Experience significant, it triggers Posner’s Orienting Network. The body and senses orient towards the stimulus (the acceleration) to get a better read. Maybe the Decay Factor is turned even lower to pick up particular details at the expense of the general picture (Phase Alertness).

Sustained Attention generates pulses of knowledge about the object or idea of concern. Once enough knowledge has been accumulated, the Executive Network is triggered and Life must make a decision as to what to do, something or nothing.

Life can turn Decay Factor up and down to move from specific to general attention and back again.

Note at any point in this multifaceted process Life can allow the Decay Factor to revert to the default sedentary range to save mental energy and return to the Alerting Network. If Life decides that the general pattern is not significant, mental energy is not employed to turn the Decay Factor down into the volatile range and the organism does not enter Phase Alertness and the subsequent or accompanying Orienting Network. Similarly if the more specific information from the Orienting Network is insignificant, the Executive Network might make the decision to return to the first phase of the Alerting Network (Intrinsic Awareness) by allowing the Decay Factor to return into the sedentary range.

Speculations about the Conservation of Mental Energy

In summary, it is evident that there are many congruencies between Posner's Attention Model and Information Dynamics, both the mathematics and theory. If living systems employ the Living Algorithm to digest information flows, then it is possible that they could evolve to employ the Living Algorithm's Decay Factor to move between general and specific attention (Posner's first network, the Alerting Network).

Why? The evidence suggests that lowering the Decay Factor to enable specific attention costs mental energy. If this is so, then a high Decay Factor setting could be a way of saving mental energy for other endeavors. Energy efficiency certainly provides an evolutionary advantage. Those with more available mental energy would tend to survive more frequently to pass on their gene pool.

While many life forms might have evolved to conserve mental energy in this fashion, we speculate that humans could take conscious advantage of these insights. How could this energy-saving be accomplished?

Is it possible that 'relaxing the mind' and/or 'diffusing attention' could save our mental energy? It seems that 'relaxing the mind' would naturally return us to the default state of general attention. However, we are frequently so mentally aroused that 'relaxing the mind' is insufficient. In this case, it is likely that we could consciously diffuse our attention to bring it to the general state. In terms of our model, raising the Decay Factor so that we aren't so focused upon details would achieve this end. We theorize that meditation was developed to serve the function of relaxing and diffusing our attention. (These speculations are only supported by anecdotal evidence.)

What about Posner's other networks?

When the Living Algorithm digests organized information, one of the mathematical results is a Pulse. According to Info-Dynamics theory, a completed Pulse of sustained Attention is the root of Experience. Moving around Posner's 3 Networks depends upon Life's determination of the relative significance of these Experiences.

The organism begins in the Alerting Network. When something unusual seems significant in terms of fulfilling potentials, then Attention is sustained, which generates an Experience Pulse. Posner’s Orienting Network takes over to focus more closely. Again Attention is sustained to generate knowledge. Once enough relevant knowledge has been gathered about the object or idea of interest, Posner’s Executive Network assumes control. Life then evaluates this knowledge in order to decide whether to do nothing or take action. If the determination is made to do nothing, the living system reverts to the default state of general attention.

It is evident that living systems play an active role in the interaction between Posner’s Attention Model and Information Dynamics. Under the Information Digestion Model, Life employs the Living Algorithm's Decay Factor to regulate between general and specific Attention and also makes the determination whether to act or not. Simply amazing.

For a slightly different perspective, check out Life’s summary of this article. See why she is excited about the Living Algorithm’s mathematical definitions.

We don't know where this investigation is heading next. To find out, check out this article – Mind Intent, the Source of Info Energy.


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