Let me summarize:

• logical models that use "rules.".
• process models that use "mechanisms."

For our purpose, to create a temporal learning and thinking model, there are some advantages of using mechanisms and some disadvantages of using rules.

To understand these differences begin with the definition of prediction:

A logical model is a collection of rule based predictions. The rules reflect:

• direct experience of one condition following another,
• indirect experience of connected symbols or,
• logical deduction (logical manipulation of images or symbols.)

In all logical predictions, the first and second condition had physical existence before the existence of the rule. Conditions not in memory can not be part of a rule. A rule-based model can not predict a condition that has not previously existed. It follows that creation of a rule requires hindsight of the conditions contained within it. With deduction, the connection is new, the relation is new, and the prediction is new, however, the composition of the predicted condition was known.

Process models, on the other hand, do not require explicit hindsight of a second condition to predict the condition. Behaviors, never tried in the real world, can be introduced into a process model and the model’s mechanisms can predict a condition that has never previously occurred. That’s how we know if a dog, a raccoon, a pig, or the wind, moves the cheese in the Rube Goldberg cartoon, the cat will be awakened even though one of these forces never moved the cheese.

This difference between a rule based model and a mechanism-based model is critical in our effort to design learning environments for temporal sight. A rule model for temporal learning would require that at least some people had temporal sight. The rules would have had to have been derived from the experiences of people who have gained temporal sight. Since we know that almost no one has temporal sight (at least at the levels I describe) we can not use their learning experiences to derive these rules and to build a rule model. With the option removed, we are left with no other choice than to build a process model that uses mechanisms not rules.

For engineers to make the jump from rule based to mechanism based model building, they had to change their view of knowledge and process. To create a process model of learning and thinking, we now have to make similar changes.

To get beyond the limitations of a rule model, knowledge must be more than the one to one memory of direct experience or related facts, values, and behaviors transmitted by our culture. The example I use to explain this expanded view of knowledge is taken from an imaginary community that wears brimless hats. For them existing knowledge includes:

A mechanism model expands existing knowledge. A mechanism varies the size of the hat – increasing or decreasing the size of the hat is equivalent to adding a hat brim. Now, as with the Rube Goldberg model where if we added elements that never existed like the dog, or raccoon, we gain a new results, in this case a mechanism that increases hat size (adding a brim) produces conditions, relations, behaviors, and values that never previously existed.

For example:

• conditions that have never existed in the past or present are found to be expectations for the future. "His nose is shaded by hat brim."
• relations that have never existed in the past or present are found to be possible constructions. "the hat brim prevents nose sunburn."
• behaviors that have never been taken in the past but if taken in the present are believed to modify the expected future. "Always wear a hat with a brim."
• values (feelings) that:
• did not result from personal experience, or
• were not transmitted by existing culture, and
• did arise from causal manipulation of abstractions.
• "A not sunburned nose feels better than a burned nose."

Thus the concept of trends in variables (in a world where experience only produced states of variables) creates an additional class of knowledge - mechanism based trend knowledge.

Our view of thinking processes, like our view of knowledge, also must be changed as we move from a rule-based model to a mechanism-based model. Familiar sensing, manipulating, storing, or retrieving, processes adequate for gathering and manipulating rules must now include processes for:

• accessing previously invisible parts of existing conditions (for example, "abstract in-process motions")
• converting existing conditions into previously unknown conditions (transformations of in-process motions into conditions that will exist in the future if no other factors preclude them,)
• converting intervening behaviors into changes in these predictions of future conditions,
• creating values that determine desirability of imaged conditions relative to conditions experienced directly. And finally,
• developing meta processes, for acquiring the thinking processes that facilitate mechanistic access and manipulation of information.

These thinking processes have the form of the Rube Goldberg contraption. Still calling it a contraption is a denigration. The Rube’s pictures of systems were works of art. Even non-mechanical people could see with clarity how movement percolated through the system obtaining a result distant in time and space from the initial action.

If a condition exists, it can be the input to a thinking mechanism. The mechanism’s output is a condition that never existed. Since the new and old condition can be connected there can be in memory a new:

• relation not obtained from direct experience of the objects or symbols in the world. And
• an expectation (prediction of a condition) also not based on direct experience of the objects or symbols in the world.

With Rube’s view of content and process, that is with:

• an understanding of the temporal aspects of information,
• the kinds of transformations performed by mechanisms, and
• the a rationale for their connections,

we can build a process model of thinking and learning. We can understand how mechanisms overcome the temporal limitations of rule models.

At a meta level, Rube’s graph of connections among mechanisms may also show how the mechanisms were acquired and connected. This may allow us to see how we developed existing thinking mechanisms that were temporally limited. It may show what changes in curriculum would result in changes in mechanisms and connections that facilitate temporal sight.

To build a process model of thinking and learning we need to create descriptions of:

These are not simple tasks even for systems like bridges for which we have descriptions of the:

To help create these descriptions I use existing dynamic, behavior, and learning models. Each of these groups of models has something special to offer:

• dynamic models give temporal context to our knowledge,
• behavior models relate temporal knowledge to our actions, and
• learning models describe acquisition of our temporal knowledge.

Each of these three groups of models contributes variables, mechanisms and pathways useful in creating three process models of temporal thinking and learning. I build a process model from dynamic models in Chapter 12. I build a second process model from behavior models in Chapter 13. And I build a third process model from learning models in Chapter 14.

Since the resulting three process models are three views of the same thinking process it should come as no surprise that they share some common variables and mechanisms. Thankfully the reader will find some redundancy. When ever possible the same examples were intentionally used in all three chapters. The overlap and commonality of variables and mechanisms makes it easier in Chapter 15 to combine the three process models into one process model of thinking and learning capable of describing pathways for the development of temporal sight.

Don’t rush your personal learning process. Reviewing the concluding diagrams in Chapter 15, before understanding the simple mechanisms that reveal the model’s power will leave you with a temporal model of learning and thinking that is mystical and less compelling.

Keep each idea and model at the common sense level. Nothing should seem mystical. There is no magic required to create an understanding. No special engineering, psychological, or educational training is required. (The engineer, psychologist, and educator is at a special disadvantage because he or she is blinded to some aspects of these process models by his or her rule-based expertise.)

The text is written for the non-expert reader, who, without serious bias, has a better chance to do the integration. The text is written so that the diligent reader will be able to understand each small step with common sense.

While only empirical work will prove a target curriculums’ correctness, hypothetical success foretells that the global application of a curriculum that creates temporal sight could create new generations that do not drive themselves toward war, famine, and pollution.

Chapter 12. Dynamic models & temporal thinking

The dynamic models I studied in engineering were process models of physical systems which, changed over time. And I use these dynamic models to make explicit the:

• components of process models,
• assembly of process models, and
• power of process models to create, what should appear to their creator, as immutable predictions.

It is this power to predict that will allow dynamic process models to describe cognition’s development into temporal sight.

Since process models are a subset of dynamic models, let me first describe dynamic models.

• Dynamic models make explicit the temporal aspects of information processed by systems.
• Dynamic models describe:
• the motion of a system, and
• the control of a system’s motion.
• Dynamic models create scenarios for a system’s
• variables , and
• connections among its variables.
• Dynamic models create these scenarios for systems:
• allowed an unfettered course, and
• where human behaviors change the
• variables or
• connections among variables.
• Dynamic models describe:
• physical systems, where variables have mass, and
• cognitive systems, where variables are abstractions.

Physical and cognitive dynamic models combine into a single model when:

• the physical system impinges on human senses or
• human behavior changes the physical system,

Dynamic aspects of physical systems are more easily seen than dynamic aspects of thinking systems. For example:

• a ball takes time to travel between the catcher and the thrower,
• wind changes a ball’s trajectory after its thrown,
• the catcher moves toward the location of both where the ball was thrown and the continuously changing location where the ball will land with the wind blowing on it.

Dynamic aspects of thinking processes on the contrary are all but hidden in abstractions. However we know they exist given that thinking processes:

• manipulate information over time,
• change their own structure over time,
• initiate and terminate manipulations,
• create predictions for future points in time,
• create meaning for predictions distributed over time, and
• compare different streams of predicted benefits.

With half of the combined system derived from partially hidden abstractions, it is a challenge to create a process model that describes how humans interact with, and learn to interact with their physical system.

However difficult, if the process model can be built, it may be able to tell us why:

• individuals in 1968 didn’t want to wear seat belts, and
• individuals today still unintentionally behave in ways that make war, famine, and pollution for their great grandchildren.

In the car driving system, (the mechanics of the car, the behaviors of the driver, and the thinking that facilitates choice of behaviors) is supported by a huge number of physical relations and social rules. However, they all fit together so intuitively, each driver is almost unaware of this complexity and thus is not overwhelmed by it. On the contrary most of us believe that getting from place to place safely in our car is a very simple exercise.

So it is with process models. As a group of independent ideas the collected structures listed below appear complex. However, they are not confusing if one can make a mental Rube Goldberg picture of them. Then, they fit intuitively together.

• Each variable can adopt different values.
• Each variable can have only one value at an instant in time.
• Over time each variable can be continuous or discontinuous.
• When variables describe physical objects then a time interval is required for a change in value.
• When variables describe abstractions then a time interval is not required for a change in value.
• The history of a variable’s values creates a scenario.

• A connection between two variables describes the affect of a change in the first variable on a change in the second.

• A process model is a representation of two or more connected variables.
• In a process model, the value of each variable controls or is controlled by the value of one or more other variables.
• If no connection exists between a variable and other variables within the process model it can not be part of the process model.
• a variable's exclusion from a process model requires that both:
• the variable affects no variables within the model, and
• none of the model’s variables affect it.

• A variable’s value at two points in time creates a scenario.
• Scenarios in memory form the basis of prediction.
• Some scenarios are not based on variable sampling, they are instead created by a mechanism that manipulates information
• When a mechanism connects two variables, the second can not change unless its predecessor changes.
• With mechanism dependence, if a variable changes, dependent variables are forced to change.
• A chain of mechanisms that connects variables creates immutable predictions.
• Sometimes these chains form trees where one variable can manipulate two or more other variables, or two or more variables manipulate one variable.
• Sometimes variables and connections form loops.
• Trees and loops form immutable predictions.

These concepts form the core of the process models that I use to describe thinking and learning as well as its development.

(Note: You might want to dog ear the previous page so you can refer back to it like you did for the page with the definition of terms in Chapter 10.)

Building process models depends on identifying both variables and the mechanisms that connect them together. There are many ways to accomplish this. Below I identify energy flow, mass flow, and information flow as three means for extracting the variables and mechanisms from our universe.

mass is moved from place to place in physical systems. Since the atoms of material are for the most part not created or destroyed, the atoms can be followed as they migrate from variable to variable in the a physical system. This tracing can become a powerful tool to help build the model.

For example, carbon atoms that start in a crude oil reservoir in the ground have many destinations. For example, drilling waste, consumed by tanker as fuel, lost into the sea as pollution, separated into one of the many products of a refinery, consumed in gasoline transport, evaporated while loading into cars, and converted by gas engines into carbon dioxide

As each carbon atom combines with other atoms to make compounds, The atoms of these compounds become threads in a search for other variables. For example as the gas is burned in car engines, the high temperatures create besides carbon dioxide, carbon monoxide, sulfur dioxide, and nitric oxides. Each of these has affects on other system variables and facilitates other predictions.

Similarly, energy can neither be created or destroyed, and thus must exist somewhere in the universe. Tracing energies locations as we traced the carbon atoms to successive locations helps define the variables and connections in process models.

A third way to identify variables and mechanisms of process models is to trace information flows among them.

For example, in the Army information flows are called chain of command. The general makes the plan and tells his majors, who tell their lieutenants who tell their sergeants who tell their men. If the chain of command is broken, it is easy to determine from the army’s communication structure who knows and who doesn’t know. If a lieutenant is killed at the front line, without replacement, everyone knows which sergeants and solders will not receive new orders.

These two examples illuminate a way of working backward and forward from any variable or mechanism. That is, one can ask the questions:

• "To what mechanism is this information passed?"
• "From where does this mechanism receive information?"
• "What causes this variable to change?"
• "What variables will be caused to change if this variable changes?"

Process models contribute to our understanding of thinking and learning in that they:

• "make predictions" and
• "explain how they make them."

These explanations are derived from the process model’s

• variable components,
• connection geometry, and
• system control structure.

Variables describe systems. Predictions are the values of variables at times in the future. However, exact numerical values are not the only useful indicator. Simply knowing the trend of a variable can be enough of a prediction to shape behavior. And if this is true then just knowing that a trend is changing can be enough to shape behavior.

In the next two sections I focus on just a small part of the information associated with variables useful in making predictions:

• variable trends and
• variable limits.

As systems get complex and mechanisms between variables get vague, predictions about variables change from how big and when to:

• "the variable is changing" or
• "the variable is remaining constant,"

and if changing:

• "the variable is increasing" or
• "the variable is decreasing."

The loss of precise mechanisms changes the visual composition of process models. With mechanisms removed we are left with variables (in ovals connected) connected by arrows that show the casual affect of one variable on another.

This simplification, while losing the process model’s ability to predict the exact value of a variable, retains the model’s "trend" predictive capabilities. For the arrow-oval diagram to create a prediction requires only that the arrow indicate the relation between the trend of the variable at the tail of the arrow and the trend of the variable at the head.

An unmarked arrow means the trends of the two variables are in same direction. If the tail variable goes up the head variable goes up. For example

In summary , these process models make predictions without numbers. The model tells the direction of the change in each variable's value relative to changes in another variable.

A connection in a process model implies that a second variable is forced to change or gets a signal to change when its predecessor changes. However, the dependent variable does not always change.

Let’s assume you are a cherry picker. And you can pick 25 cherries a minute. You are carrying a pail where you put the cherries you picked.

Your model predicts the number of cherries in the pail. At the end of one minute you have 25 cherries. At the end of two minutes you have 50. At the end of three minutes you have 75 cherries etc.

Sounds simple enough until the pail gets full. Then "minutes collecting" does not predict the "cherries in pail." The variable "cherries in pail." has a limit and once the limit is exceeded more minutes does not mean more cherries in pail.

There are cases where variables have idiosyncrasies. These cases sometimes lead to the addition of new variables not included in the original process model, "cherries picked and not in pail" and additional predictions (not previously thought of a future conditions resulting from the original system) cherries dropped.

The presence of these newly discovered future conditions change the decision environment, which is created by the process model without the new variables. Thus the efforts to review undiscovered idiosyncrasies within the process model’s existing variables is itself a powerful process for making predictions.

Identification of variable idiosyncrasy is a large research area I introduce it only to reveal some of the predictive powers of process models. Besides the example above, called "overfilling a sink" listed as 1) below, I describe three others.

Some of the predictions created by process models stem from the geometry of the connections among its variables. These shapes include chains, trees, and loops.

Consider a process model that describes the gas in a gas tank and the "gas consumed by a running engine. " The model of the two variables appears below:

This two variable dependency can be expanded into a chain of dependencies. For example, assume that the engine drives a tractor and the tractor is plowing a large field. Each row of the field takes a period of engine running time. Now we have three variables in the chain where none can change unless all change.

If the field is big and would take many vehicle tank refills to plow the whole field and there was a fuel storage tank at the field’s edge and the tractor stopped there to refuel, a fourth element in the chain could be added – the fuel in the storage tank.

If the field was so big that many fills of the fuel in the field storage tank would be required to complete all the rows. Then a central farm tank, that holds fuel only for plowing, would periodically make fuel transfers to the field tank. Then the fuel removed from the farm’s central plowing storage tank could predict the number of rows plowed.

This chain of mechanisms describing fuel flow, can be expanded by tracing back through, the fuel distributors tanks, the refiner’s tanks, the transporter’s ship tanks, the oil well pumper’s tanks and finally the crude oil reserves in the ground. The process model can make predictions about each of these from the amount of field plowed.

Process models can be more than single chains. They can branch like trees.

Much of the dependence and predictions made possible by the process model were made possible by doing a conservation of mass analysis. That is the atoms that were in the fuel reservoir are the same as the atoms in the oil tanker, and finally the atoms in the fuel tank of the tractor.

Buy following these atoms along their journey we could identify additional variables in our system. Notice that in the above chain we went from fuel consumed to rows plowed.

We could have also done a conservation of mass trace and identified a variable called "carbon dioxide put in the air." The carbon atoms in the fuel tank went somewhere.

Thus our chain can branch. Burning fuel in the tractor has two depend variables – plowed rows and carbon dioxide added to the atmosphere, see Figure 12.2-33.

It is easy to see that the tree shapes that show up in process models also have predictive qualities. Several variables can have their changes be dependent on the change of a single variable.

The limitations on the model’s ability to predict in these branches still relate to each connection being a mechanism. The plowed rows exist only if the tractor "caused" the ground to be turned. If the tractor idled in one place, there is no causality. Similarly the chain of variables describing changes in the containers of fuel "predict" each other’s value only if there is a causal (in this case conservation of mass) connection. If it was possible for an atom of carbon to appear and disappear at will, conservation of mass would not hold, causality would be lost, and the chain or tree would not have predictive capabilities.

Summary of chain and tree process models

In process models, what happens when a down stream variable gets its signal to change, changes, and sends out a signal to an upstream variable? The upstream variable changes. This in turn creates an impetus for the down stream variable, the one that just did the telling, to change again.

Once any variable changes in such a circular "loop" all the variables in the loop and all the variables not in the loop but dependent on changes of variables in the loop continue to change. Thus loops, in process models, predict immutable future changes in variables just as they do in chain or tree configurations.

The changes in the values of any two variables in a loop are determined directly by the mechanisms that form the connections. However, the loop geometry, the connections all working together also tell us something about the future states of variables.

With loops the signals

• keep running once they are started,
• can change its message between any two points in time.
• can tell a variable to go up, down, or remain the same.

Given these alternatives in continuing communication,

• some variables within a loop may diverge away from their initial values, eventually attaining the variables extreme limits.
• some variables within a loop may converge toward a single value of the variable.

The bus system is a good example of a divergent loop Figure 12.2-34.

Unfortunately divergent loops have two directions. If ridership falls, then revenue falls and this will cause service to fall... etc. The end of divergence is that service is so poor no one rides the bus. There are no revenues and the buses stop running.

In "convergent" loops " change in a loop’s variable sets off a chain of changes around a loop resulting in the restoration of the variable’s initial value.

For example consider the furnace output in a house and the room temperature in a house.

Notice however that as the outside temperature decreases the furnace will have to be on more of the time. As the outside temperature rises as it does in summer the furnace will be on none at all to maintain the room at or above 72-degree temperature.

We have discussed convergent and divergent loops before when we used the two control electric blanket to show how people failed to predict what the blanket would do under everyday circumstances. The figures from that section are repeated here. The same six variables predict comfort or chaos depending on the order they passed information to one another.

While not calling them process models in previous sections I showed how the six variables connected together in two three variable loops maintained two independent comfortable blanket temperatures for two people under a wide range of room temperatures and individual feelings of chill. I also showed the six variables connected together in a single loop predicted discomfort for both parties.

In this section I will use those diagrams to describe how the loop diagrams, could predict whether a loop is divergent or convergent. I will show a mechanism for determining whether:

• variables stay close to desired values or
• variables migrate continuously away from initial values.

My purpose is not to teach you how to model electric blankets but to show how powerful very simple process model graphs are in making predictions about future conditions. And to help you realize that in the absence of these strong predictions, expectations and behaviors will be different.

The correctly operating two-control electric blanket was shown in Chapter 3 Figure 3.2-50. That figure is the same as Figure 12.2-50 shown below. In Chapter 3, I told a story about how the structure relating the variables in a system determine its behavior. Here, I will provide a process model graph tool that will show how the stability or instability of any loop of variables can be determined. In this way I will be able to show in more detail how process models make immutable predictions.

The person under the blanket did not request more energy on cold nights and less energy on hot nights. They requested a fixed temperature under the blanket. It was the blanket’s controller that requested the energy to maintain that temperature.

The controller acts like thermostat on the wall of your house. If the room temperature is too warm it turns the furnace off and if it is too cold it turns the furnace on.

The set temperature of the furnace thermostat can be changed. For example, from a set temperature of 55 degrees at night to 65 degree set temperature during the daytime. Whatever the set temperature is that is the temperature at which the room is maintained.

Similarly, if the blanket "set" temperature is not what the individual wants he or she dials in a new set temperature, higher or lower for the blanket to maintain.

We can model the blanket with just three variables:

• the set temperature,
• the temperature of the blanket. and
• the desired temperature of sleeper.

These three temperatures communicate-with one another – just as the gasoline atoms moved from one container to the other.

The blanket can be graphed as in the figure below.

• an increase in the first variable requires an increase in the second, or
• a decrease in the first requires a decrease in the second.

Also notice that the sleeper under the blanket either feels an increase in blanket temperature or feels a decrease if the blanket temperature decreases. So connect also is marked with an "S" for same.

The third connection is between what the sleeper feels and the set temperature. If the sleeper feels her temperature is increasing she decreases the set temperature down. If the sleeper feels her temperature decreasing she increases the set temperature. Thus a change in the first variable obtains an "opposite" change in the second variable. Thus the connection between them is marked with an "O."

The net result of these three interactions among variables is that the system is rather well behaved. After the individual raises and lowers the set temperature several times, she finds the temperature she likes and then leaves the set temperature untouched for long periods of time.

This loop, with one opposite control link (marked "O"), displays a tendency to center one of its variables about a fixed value like the set temperature.

To generalize,

You will remember that if the blanket was turned over, so that her "set temperature" controlled his half of the bed and his "set temperature" controlled her half of the bed the system became very badly behaved.

By flipping over the blanket it becomes clear that the two loops of three connected variables become one loop of six connected variables, Figures 12.2-65.

Is there a simple structural difference between the six variable and the two three variable systems? Is there away to tell if a loop will have the nice behavior or the deviant behavior.

From inspection we can see the six variable loop has two opposite links in the same loop instead of one each in the two loops. The two opposite links cancel each other out, and the loop operates like the unstable bus system model. That is as if it had an even number of opposite links the loop.

If we generalize what we have learned any process model with an even number of opposite links, that is 0, 2, 4, 6, etc. will be a system predictably susceptible to all of its variables going to their extremes. That is it will be the same as having all same links. While any loop with an odd number of opposite links 1,3,5,... will predictably maintain some variables in the loop near expected values.

(Note: Connection geometry makes strong predictions. This type of prediction is most important when long time periods between action and outcome make learning by experience too weak to make strong predictions and thus adequately influence behavior which would avoid bad future conditions.)

Not surprising "feedback control" operates effectively at our level of temporal blindness and "feed forward control," an integral part of our elusive temporal sight, is can hardly operate at all. Process models describe why.

For example, we have seen what problems the flipped electric blanket can make. We have seen how process model graphs can predict the consequences when the system is graphed. Next I will electric blanket problem to explain the difference between feedforward and feedback control.

A close inspection shows that the arrow accepting information from the variable "feelings of warmth" and sending it to the "set temperature" is not a simple mechanism. Instead it is a collection of mechanisms. The arrow includes:

• a sensor, which evaluates a condition created by the previous variable,
• a comparator, which compares that condition to a desired condition a behavior conceptualizer, and
• an actuator.

These mechanisms and variables facilitate a wide range of thinking processes. Some of these processes can be recursive. They look at themselves.

A human being can see itself as an element in a system. By inspection of that system’s connections, the human being can conceptualize motions or changes within the system, which have not occurred. These images can contain the potential dangers of untried behaviors. When the image rather than the experience facilitates an avoidance of that behavior the thinking processes can be called feedforward control.

For example, if the graphic of the flipped blanket can be constructed and its instability can be calculated, before the act, then there can be a consciousness in remaking the bed not to perform the behavior of turning the blanket upside down.

This realization of understanding the consequences of a behavior before it has been taken, and using that realization to direct behavior, is a form of system control. However, it is not feedback control because the avoided conditions never existed.

In the next two sections I will present process model graphs that make explicit the information flows for the feedback and feedforward control described in words above.

Begin by focusing on the following subset of aspects of process models.

• Systems have variables.
• Variables have desired levels.
• If the variable gets too big, behavior makes it smaller.
• If a variable is too small behavior makes it bigger.
• If the variable is stuck, behavior gets it moving.
• If a variable is moving and the preference is for it to be still, behavior stops its motion.

In this context the cognitive portion of system control means seeing the need, and creating the behavior to get a system variable to change in the direction of preference.

Remember that mechanisms are rectangles and have conditions as input and output. Ovals are conditions or records of conditions in memory. When a condition becomes overwhelming complex, as is the "state of the world at an instant of time," in the following figures, these large aggregates of conditions have been noted as two character designators not ovals. For example t0 means the condition of the world at a time zero.

In memory "best previous state" is connected to a behavior that have moved past less-than-best-previous-states, (e.g. matching closely t0) close to that target state. In this sense, behavior moves the physical world, at t0, toward a previously experienced target state with behaviors used previously to attain that target state. T0’ represents the modified t0 condition.

If this model were to fully describe our cognitive control over the physical world, consciously chosen behavior would depend on a combination of

• "what already exists in the present" and
• "what happened in the past."

With this description of control, humans can at best reproduce the best of the past. Humans can not improve present conditions, through their conscious choice of behavior beyond the best conditions that existed in the past except through serendipity.

If a form of creativity exists in addition to serendipity, there must be a process model that describes the additional flows of information through the physical and cognitive systems.

My search for this new model begins by reformatting and expanding our view of feedback control. Expand Figure 12.2 - 70a into a series of physical world states, t -3, t -2..., separated by a series of physical world processors as shown in Figure 12.2 - 70d.

• similar states from the past (tps) and
• previous behavior tpb that has moved that state toward Mbps.

Human cognition then sends that behavior in conjunction with t0 to form t0’ as input to the right collection of mechanisms labeled physical world.

• present or past states of the system already in memory,
• behaviors that have been taken in the past, and
• changes that have resulted from those behaviors.

Thus when a behavior is chosen for implementation at t0’, that behavior is depend on information that came from back in time. Thus the control process depicted in Figure 12.2 -70e might truly be given the name feedback control.

This is a much more restricted view of feedback control than the reader might have. However, it is much more useful in describing the feed forward control process used in the creation and use of temporal sight.

• Human cognition is fed additional information in the form of the mechanisms and connections of the physical world as well as the state of the physical world at t0.
• human cognition performs some additional thought processes.
• it polls the physical world for its mechanisms and connections.
• with the mechanisms and connections it builds a world simulation.
• With trial behaviors creating trial input conditions (shown as t0’s – read t-zero-prime’s) the simulation creates alternative image of t+1’s for each trial t0’.

Consider two examples:

In both examples information exists in memory before a choice of behavior about conditions which have never existed but will exist after the behavior. That is information from the future exists in memory.

Now we can divide the contents of memory at time t0 into that which occurred in the past and that, which occurred in the simulated future. In Figure 12.2-70g, information about the past that is supplied to the behavior selection from memory is depicted as entering from the left. Information about the future that is supplied to the behavior selection from memory is depicted as entering from the right.

It follows that temporal sight is the choice of behavior based on feedforward control.

To the process model bridge designer, choosing a prediction that does not match that made by his or her model, means his or her model is wrong. To correct it requires changing a mechanism or variable so the preferred prediction results form the model. The conflict arises when the process model’s original mechanisms and variables are the ones that seem most correct, and the required changes, to obtain the alternative prediction seems wrong.

This conflict requires some serious inspection. We already know the basics of process models, which connect two values of a variable using a mechanism. The second condition is a function of the first condition.

Now lets look at some alternatives thinking processes that connect to values of a variable. These connections produce relations like:

• "the second condition follows the first in time" or
• "the second condition is equal to the first."

While these relations also:

• can be stored in memory, and
• groups of similar relations can be generalized to rules and stored in memory, and
• these relations and rules can make predictions,

the predictions have limitations. One we have already discovered,

• Rules can not predict conditions that did not previously exist.

Next, I show an indirect limitation:

Relation or rule connections are based on experience – direct or indirect.

In both cases the relation can be learned without having knowledge of its connecting mechanism. These relations create weak predictions, because they are not "always true."

• our teacher could have learned an incorrect relation and we correctly learn what she or he taught us.
• our teacher learned it correctly transmitted it correctly and we learned it incorrectly.
• our experience correctly reported a change in a condition in the physical system but we incorrectly connected this change to a condition-change that did not cause it.

The net result of this long list of ways for an individual to learn an incorrect relation is that the predictions resulting from rules are sometimes incorrect. And since there is no way to verify the correctness of any rule based prediction, all rule based predictions have unknown uncertainty. Our experience with rule based predictions verifies our knowledge of uncertain.

Everyone knows someone that knows someone that survived the fiery crash of an airplane or car. Or the person who was underwater for 20 minutes but was not killed. Or... Predictions that people get killed in crashes or through drowning are based on rules, which are based on direct or indirect experience, and they have exceptions. The exceptions create uncertainty. So many, it seems, like random and unquantifiable uncertainty. This opens the door to let any prediction compete with any other prediction. This reduces the integrity of choosing behavior based on prediction.

When these views of uncertainty, prediction, and behavior also permeates the thinking based on process models, it derails any possibility of attaining the feed forward control required to create temporal sight.

For example, There is a rule that some people will not be injured in a terrible car crash. Given the counter prediction that people are injured in terrible car crashes and a large portion of uncertainty attached to, a decision-maker can conclude that he or she will be covered by the former prediction rather than the later.

However, there are no exceptions or uncertainty when people in a car crash are subject to injury producing forces. Masses require forces to change velocity. A crash is a change in velocity. If these forces are high enough they cause injuries. The mechanistic connections of a process model include:

• the higher the speed, or
• the faster the stop,
• the smaller the contact area
• the higher the forces and
• the greater the injury.

These mechanistic relationships are immutable.

A rule of thumb prediction, for example, "A person with a rabbits foot in his possession will be saved from these injuries," should not compete with a process model’ s predictions. A person should choose to put on a seat belt. When he or she does not, it is due to uncertainty.

The rabbit’s foot prediction, when transmitted with the weight of culture, creates uncertainty about the predictions of injuries created by the process model. Predictions that are based on these less than mechanistic relationships unjustifiably diminish the influence of predictions based on mechanistic connections. The undeserved uncertainty lessens a mechanistic prediction’s influence in choosing behavior.

To give a second example, we know that the fuel, consumed in a car, comes from the gas tank. We know that the amount of fuel burned is the amount absent from the tank. We know that if we burn all of the fuel in the tank the car will stop running. These connections are not based on experience, fantasy, or expert advice. The predictions are underlain by the mechanisms that reflect conservation of mass. Predictions between amount of fuel remaining in the tank and a running engine are not fuzzy or debatable. They are immutable. It is inappropriate to attach uncertainty to these predictions.

Counter predictions, like:

are fantasy. To let random allocated uncertainty be facilitated by a prediction based on non mechanistically built relations derails rationality.

In summary, the strength of a process model’s predictions is based on the following set of constraints.

• In process models if something is known about the change in one of its variables, something is known about changes in all of the others.
• The relationship between model produced trends and real world trends are built at personal level. The process model is initially built on the model creator’s personal view of the variables and connections he or she believes exist in the physical world. Any model that:
• includes items that the model creator believes to be untrue or
• leaves out items that the model creator believes to be true,

• If the creator of a process model does not believe (or like) the immutable predictions, he or she can change the predictions only by removing or adding connections or variables to the model.

Within this set of constraints, a person’s process models creates immutable predictions.

When this prediction is disbelieved or ignored in choosing behavior it suggests that there exists some other prediction which appears stronger. It suggests that the constraints of process modeling do not carry the weight of their rational structure.

Process model structure allows us to:

• generalize what information and thinking create immutable predictions.
• why we unknowingly adopt so many incorrect predictions, and
• why, in our present state of mental development, few predictions have high certainty.

Immutable predictions make behavior selection easier, however complex systems are difficult to process model. They contain many variables with many connecting mechanisms. Faced with having to make decisions that interact with systems that seem too difficult to model, humankind has found a quicker, yet less accurate predictive process. It uses non-mechanistic relationships to create predictions.

These, in their least dangerous form are called correlations. Correlation is what results from the scientific method. The scientific method identifies a relationship between variables using strict rules:

• to acquire information from experience (experiments) and
• to perform data analysis to create relations.

Unfortunately these safeguards do not prevent the scientific method from discovering incorrect predictions. The net result is that non-mechanistic modeling creates uncertainty about its predictions and unjustly uncertainty of all predictions including those created by process models.

Let me give an example of how correlational information creates uncertain predictions.

Smoking causes lung cancer – Or is it coffee?

No one really knows exactly what makes lung cells become cancerous. We do not know the cause. There are many examples of people who are heavy smokers who have contracted the disease and died. There are also some heavy smokers, who have not contracted the disease.

So this leaves the door open as to "if" smoking causes lung cancer. "Smoking causes lung cancer" is not as perfect a prediction as "If the engine is running the gas tank is being emptied."

The studies that have been performed to answer, "What causes lung cancer?" look at groups of people who are the same in every way except some smoke and some do not. A group could be all people who lived in New York City their whole lives and died at age 70 in the year 1975. The researchers recorded who in the group died of lung cancer and how much each person (independent of the cause of death) smoked.

The measurement of smoking is "pack years." A person who smoked a pack a day for 20 years was given a score or 20. A person who smoked a pack a day for 40 years was given a score of 40. A person who smoked two packs a day for 20 years was also given a score of 40.

The researchers divided the people who died in 1975 in New York into groups. All the people who had smoking scores between 50 and 45 were in a group. All the people with scores between 45 and 40 were in a group, down to a group that had scores between 5 and 0.

For each group they calculated the percentage of the group that died of lung cancer. And they plotted a graph with the percentage death on the vertical axis and the group’s average smoking score on the horizontal axis. (See graph below). Of little surprise to no one, the points created a rising line to the right. The groups that smoked more had higher percentages die of lung cancer.

Let me give another example to demonstrate the vulnerability of this means of making predictions. A second group of researchers measured amount of coffee each of these New Yorkers drank. They came up with the number of "cups of coffee/day/ years" instead of "packs/day /years" for each person. Then they divided the coffee drinkers up into groups as they did the smokers. They calculated the percent in each group of coffee drinkers that died of lung cancer. When they plotted coffee consumed vs. percent in group died of lung cancer they found the new line, rising to the right, that showed the more coffee drunk the higher the chances of dying of lung cancer.

While we want to believe the prediction," the more smoking we do the more the lung cancer we will have," we don’t want to believe the prediction, "the more coffee we drink the more lung cancer we will have." However, from the data and the analysis both relationships and both predictions are equally strong.

In the creation of correlation, we gather the data, plot it and see if "the more of this quantity means more of that quantity." However, correlational data is not mechanistic. It is not like the gas tank emptying when the car is moving. A percentage of the heavy smokers don’t die from lung cancer. The uncertainty weakens the prediction. Maybe the uncertainty is deserved. It is easy to see why the heavy coffee drinkers wheel that argument. They don’t want to give up or feel guilty about drinking coffee. Other than the correlation, there is no reason to believe that if people drink less coffee they will have less lung cancer.

Many of our most scared scientific beliefs (experiment based beliefs) are dependent on correlational analysis. So many of these correlational beliefs have made incorrect predictions (that is they measure cup of coffee instead of packs of cigarettes, that all predictions, including those derived from mechanistic models seem uncertain.

Uncertainty has a devastating the effect of any prediction on decisions. Decisions with high present costs and larger predicted future benefits are the most susceptible to this uncertainty. That is, you have to believe your prediction is very good to give up something good today to get a bigger good in the future.

However, the reversal of humankind's trends toward war, famine, and pollution depends on making these types of decisions. Non mechanistic (e.g. correlational) predictions increase rather than decrease the uncertainty. That is why we have to understand the difference between process model and correlation produced predictions and allocate different certainty to ea.

Our global problems can be described by either a process model or multiple examples of correlation. Predictions, resulting form correlation analysis, are often in conflict with each other. One study says there is no increase in one variable as the other increases and the other says there is such an increase. However, when we let these correlation uncertainties take credibility away from process model predictions, then, our behaviors do not reflect the available temporal information.

In closing, each of us does not want to take personal responsibility for perpetrating war, famine, and pollution on our great-great grandchildren. A process model says six billion of us share that responsibility. The correlational analyses are divided. The uncertainty of these latter analyses let each of us take the same isolated stands of the heavy smoker. We firmly cling to a belief that we are the 1% whose behaviors, while looking like everyone else's are not contributing to the common march toward our ugly future.

The motivation for using correlation instead of process modeling was that the complexity of systems made it difficult to make explicit each unique variable and each connecting mechanism.

For example, the human body has millions if not billions of variables and connecting mechanisms. Taking a medicine directly changes millions of these variables and mechanistic connections trigger changes in millions more. The medical researcher side steps this complexity by measuring the medicine added and the affect on the target anatomy. She or he then plots the data to obtain the correlation. She also plots the correlation of the medicine against other parts of the anatomy that she hopes are not affected. It is a crude science at best but it may be the only possible way to proceed with medical research.

It would be better if medical researchers had a process model of the human body. Lacking such a model leaves the door open to untrue predictions like "coffee drinking causes cancer." Worse, the trial and error development of new treatments causes tragic side effects because there was no way to predict what the drug might do besides effect the target anatomy.

However, medical researchers too often confine themselves to believing that systems are too complex to allow any mechanistic predictions and that experimentation with correlational analysis is the only path to understanding. Some tragedies might be avoided using an aggregate form of process modeling. In this form, aggregated elements and their mechanism connections can still make strong immutable predictions. For example, if no water enters the body, and water leaves the body, dehydration occurs. No amount of additional complexity will counter this prediction. Contrary beliefs, for example, that we can not know for sure that dehydration is taking place until we test, are counter productive.

The world’s war, famine, and pollution problems are examples where process models, using aggregations of many variables, still maintain their predictive strength in the face of great complexity.

The most important strength of process model predictions is the reduction in uncertainty inherent in their design facilitated by using mechanism rather correlational connections between variables.

The second strength of process model predictions is their change in strength over time. Process models produce predictions that gain strength over time while predictions from correlational models grow more uncertain over time. This follows from the fact that correlational connections depend on historical information which ages as predictions extend father into the future. Conversely mechanistic predictions become more robust as the time period lengthens. Running the engine empties the tank. As the engine runs longer, the exhausting of the fuel in the tank becomes ever more probable."

The third strength of process model predictions derives from the need of the modeler to be explicit on which variables and mechanisms are included and excluded. We have seen that when many variables and mechanisms are aggregated as they are in correlational analyses, then factitious predictions result. For example, increased coffee intake increases the chance of death by lung cancer.

Fourth, process models create predictions, which are continuous. If the engine does not run the fuel remaining in the tank does not change. With correlation if we stop smoking it will not stop the lung cancer you have already contracted from killing you.

In this chapter I used dynamic models to introduce process models. Process models illuminate several temporal capacities of, thinking, learning, and behavior selection.

• Process models make predictions that differ from predictions created by other thinking processes.
• During behavior selection, process model predictions are in competition with predictions created by other thinking processes.
• Uncertainty attached to predictions determines which behavior we choose.
• Past experience with making and using predictions has led us to undervalue the immutable predictions made by process models.
• Process models differentiate control over behavior into feedback and feed forward control. This differentiation is central in understanding why certain forms of information, specifically present trends and future conditions, are absent or undervalued in our thinking.
• Process models, facilitate behavior selections that achieve future conditions that were not even goals of the decision-maker before the process model was placed in operation. For example, for 6 billion decision-makers, the choice of number of children was never connected to the individual’s contribution to the war, famine, and pollution in which their great grand children would have to live. As a result certain procreative behaviors were not considered contributors to results almost no one wants.

Chapter 13. Behavior models & temporal thinking

Economic models predict that behaviors stem from weighing the recognized and valued benefits and costs resulting from an act, against the recognized and valued benefits and costs that accrue from not acting (or acting alternatively.) The key words are

Unrecognized or miss-valued benefits and costs distort the choice of behavior.

In this chapter, I will focus on distortions in:

• an individual has a value which makes him or her averse to a future condition however, temporal blindness prevents the individual from predicting that condition, or
• the future condition is predicted, however, the aversion is limited to present conditions. Temporal blindness prevents the extension of that aversion to future condition."

• search for a condition’s causing behavior, or
• find a new behavior to prevent the condition, or
• to extend values that cover an existing or immediate condition to values that cover the same condition in the future.

In this chapter I show, using process models, that temporal blindness induces differences in "knowing" and "valuing." As a result, a temporally blind person makes different decisions from a temporally sighted person. These differences result because he or she:

• perceives different conditions,
• has different expectations for how behavior affects conditions, or
• has different values for conditions.

The goal is to create a process model that:

• predicts which behaviors an individual will choose given:
• present conditions,
• future conditions, and
• preferences for conditions.
• shows how information is acquired and manipulated to create:
• perception of present and future conditions,
• predictions of the affects of a behavior on conditions, and
• relative worth of conditions.

By comparing this process model with more familiar behavior models we can realize their limitations and understand our choices of behavior. For example, we will understand why spiritual, cultural, rational, physiological, autonomic, genetic, and social biologic, models produce behaviors that create the future conditions no one wants.

I will show that their limited performance relates to the temporally limited images created by direct experience of worldly objects or symbols. For example, I will show that using this information to choose behavior is often like using only the information view from the rear view mirror to drive a car.

Lets begin building the process model of behavior selection by describing, in broad-brush strokes, the information flows, and processes that transform information into behavior. Then, complete the model using progressively more temporal definitions of information and process.

The process model in Figure 13.1 - 10 describes the behavior selection environment. The part that lies below a bold horizontal line contains elements within the physiology of a human decision-maker. The upper part, external to that physiology, contains the billions of variables and processes in the "external world."

Behaviors, physiological activity, cross the line upward. Changes in the external world cross the line downward as physiological sensations.

The fluid of the model is information. Information, flows, is transformed, collects, and is compared. This information is in compliance with the rules presented in the dynamic process model chapter. That is, the information can be physical or abstract. It consists of variables and derivatives that describe changes in those variables.

Groups of variables form a condition. Variables range from the meta physical of, "How much god loves me." to the physical, like, The number of staples in my stapler." From the indirect like "how many dollars are in my bank account." to the physiological "How much carbon dioxide my body produced in the last five minutes."

The six conditions in Figure 13.1 - 10 are described below.

The "existing" condition is the individual’s registration of the "external world." The variables in the condition are not limited to direct recording of sensations. The acquisition processes can also create registrations of variables through mental manipulations of these sensations and residues of previous sensations and manipulations. These include the individual’s perceptions of quantities of variables and positions of objects at the present instant, their trends, and expectations of their future quantities and positions.

The "desired" condition is the individual’s preferred state of "existing" condition. These include not only the preferred quantities of variables and positions of objects, but also their preferred trends and a "preferred-future-scenario." The same group of acquisition processes that created the "existing" condition creates the "desired" condition.

The previous two conditions feed into a "comparison" process. This process produces a set of differences between an existing and desired condition. If the differences are small, (small as defined by a meta class desired condition) the process induces no change in the previous behavior patterns, which allows or maintains them. If the difference is large, the differences are collected into a problem condition.

The "problem" condition induces a search for alternative behaviors that will change the existing condition and make it closer to the desired condition. This could be a wish that an outside force (e.g. god, the parent, or the government) changes the existing condition. It might also mean a search for an alternative personal behavior that could be executed.

An alternative behavior triggers a fourth application of acquisition processes. These processes attach to each behavior a "resulting" condition. The range of acquisition processes provides for a range of "resulting" conditions. Some of these are little more than fantasy and others are the result of strict causality. In strict causality the resulting condition depends on a tracing of motions in the "existing" condition and then a tracing of these changes as they are affected by the behavior.

The winning "resulting condition" is compared with a "desired condition" to obtain a difference. If the difference and the cost are small the alternative behavior is implemented in the external world. As in the previous comparison, large and small is based on a second set of meta desired conditions.

If the difference is found to be too large, then either

• a new round of alternative behaviors are created. And
• a new competition creates new "resulting conditions"

The process model in Figure 13.1 - 10, while grossly oversimplifying the human decision environment, provides a skeleton which will help us understand the temporal aspects of thinking and learning.

The geometric structure of the process model in Figure 13.1 -10 shows parallel and serial information flows. Parallel flows will show us that many alternative conditions simultaneously compete for dominance and thus use in a behavior selection environment. Serial flows will show us that behaviors are taken one at a time. Parallel and serial flow geometry are starting points to describe temporal limitations in our thinking and learning.

When we think of models that explain why we choose the behaviors we do, we commonly think of spiritual, cultural, rational, physiological, autonomic, genetic, and social biologic models. Each model produces a behavior for a real world condition. Each model contributes an image of a resulting condition. And each does it simultaneously even thought only one of the proposed behaviors and can be taken at an instant of time and only one future condition can result.

Consider the case where a male approaches several females. The genetic model predicts he will sexually engage all of them to ensure the survival of his genes. While his cultural model predicts "he must limit sexual activities to his spouse." However, the models are not running inside his brain. Parallel acquisition processes are running inside his brain. They are producing competing images for the

• existing condition,
• desired condition, and
• resulting condition.

If the processes that are creating the images corresponding to the genetic behavior are stronger than those creating images corresponding to cultural behavior, then cultural penalties appear smaller at the instant of behavior than the, possibly subconscious image, of passing on ones genes.

This parallel acquisition geometry is one of two, which I will use to detail the temporal limitations in human thinking and learning. The second failure is revealed by serial information flows.

The second geometric structure revealed in Figure 13.1 -10 is that behaviors must be taken one at a time. After a behavior is taken the external world changes. Inputs to the existing and the desired condition change and the next behavior selection environment is not exactly like the previous. As a result, each successive behavior is dependent on all of the conditions and thus all of the behaviors that came before it. Each successive choice of behavior depends on the ability of an acquisition process to:

• acquired differences in the external world that followed the behavior,
• isolate those differences created by the behavior from those that were in-process in the external world before the behavior, and
• maintain records of serial behaviors and the resulting changes attributable to each.

This temporal view of the acquisition processes focuses our attention on their longitudinal capabilities. By longitudinal I mean, an acquisition process that chooses one behavior to address an immediate condition and then is used again to choose a second behavior to address a subsequent condition. The question is, "Does the process used in the first decision have a view of the second decision environment."

For example, to what degree does the acquisition process make apparent, at the time of the first decision, that the first behavior will create the external condition which is the basis for its second application. Depending on the quality of the acquisition process, its view of the dependence can be strong or weak. Depending on the strength of this reflective view, the second condition plays a strong or weak role in choosing the first behavior.

For example, if one is thirsty, then one drinks liquids. If one has a full bladder one urinates. If the connection between drinking and urinated is weak then the decision to drink is not effected by the discomfort of holding one’s bladder. If the connections are strong, it will.

A second example of strength and weakness in acquisition processes emerges when in addition to the first behavior creating the condition addressed by the second, the second behavior creates the condition that is addressed by the first. The serial flows of information form loops within the behavior selection environment. The question is "Does the acquisition process discover the loops?" Certainly those processes that discover the loops will make a different series of behavior choices than those that have no discovery. Those that discover the loops only after several cycles around the loop make different behavior choices than those that make the discovery with no cycles around the loop.

For example, consider the case where the first behavior "making peace" makes peace between two groups by redistributing scarce resources. Peace allows individuals to focus on improving their lives. This means increasing their personal demand for resources. Unfortunately these resources were only redistributed by the peace-making behavior. They were not increased. Under these circumstances successful increasing one’s own consumption recreates the uneven distribution. Which remakes the war and re-requires the peace making behaviors of redistribution. We have uncovered a circular series of behaviors that have a life of their own.

The difference between a strong and weak acquisition process is that the resulting :

As we found with parallel structures, serial structures determine the quality of behavior selection. Different behaviors result when different acquisition skills are present. The remaining sections of this chapter will view in detail the acquisition processes and their operational effects on which behaviors are chosen.

Figure 13.1 -10 implies that:

• each parallel pathway creates a different condition for the same input information.
• at any instant; only one produced condition, could be used in the comparison that follows.

This implies that, at that instant, the values and preferences, produced by the other six pathways (if they are not identical to those in the winning condition) are considered inferior and are not brought to comparison.

Choosing one condition over another means they must be compared and ranked. To compare two things requires dimensions. The temporal singularity of these dimensions is central in determining a condition’s strength and weakness.

While the dimensions of a condition are allowed to range over time, at the instant of comparison, numerical values are fixed. Objects can not be at two places at the same time - variables can not have two sizes.

This singularity extends to both the value of a variable and that variable’s temporal aspects. For example, an object must assume one of three trend states: getting "closer," "farther" or "staying the same distance" from some reference. A variable must be getting "bigger," "smaller," or "staying the same size."

Singularity in condition also extends to "desired " conditions. Each must contain a singular preference for each object’s position and each variable’s numerical value. The desired condition also includes preferences for temporal terms.

Singularity also extends to the difference between two conditions. A single hierarchy must exist for the set of deviations between two conditions. How else would one know which of the existing deviations must be dealt with first.

For each instant, only one value and trend describe the variables within a condition of:

• what is,
• what is desired, or
• what is expected after taking a behavior.

When doing comparisons between the existing and desired conditions, or between the existing and resulting conditions, at the instant of comparison only one condition can exist.

If each condition shapes behavior differently than its rejected competitors, then to understand how temporal blindness weakens each of these acquisition pathways and shapes behavior selection, we will have to make each pathway more explicit.

Conditions are created using acquisition pathways. Each pathway has mechanisms, which transform information and create conditions. Each mechanism accepts input information and emits output information. Each sensing mechanism may not be sensitive to the temporal aspects available in the information. Each transforming mechanism may not use the temporal information, which is correctly sensed. If the resulting condition is partial or weak it will compete poorly. If the condition that would be dominant is not brought to comparison with it true full strength, the chosen behavior may not correctly reflect temporal aspects of the external world.

In the next sections I introduce the sets of mechanisms used in acquiring conditions. I begin with mechanisms used to create existing conditions and then use them as a base to describe additional mechanisms required in creating, desired conditions, alternative behaviors, and resulting conditions.

Describing mechanisms that create an individual’s descriptive view of the world (existing condition,) not a preferential view of the world (desired condition), has been one of the most fundamental issues of both philosophy and psychology. Tens of thousands of books and papers have been written to describe how an individual comes to know this view. Only an infinitesimal sliver of this view can be addressed in the next pages. This sliver focuses on temporal aspects of information and mechanism.

To focus on temporal aspects of conditions we need to eliminate from the discussion aspects, which commonly distort behavior but are not temporal. The most common of these distortions is omission. That is when "existing conditions;" omit objects with position and size that exist in the external world. A second common distortion is inclusion of objects that do not exist in the external world.

However, even when an "existing condition" is a perfect snapshot of the external world, that is, when:

• it includes every object
• no extraneous objects; and
• it describes all accurate positions and sizes of all objects
• at the time a behavior is selected;

this perfect description does not tell anything about the objects in the future.

For example, a snapshot of a car does not tell if the car is traveling backward or forward. How fast it is traveling. If it is changing its speed (as it would if it was rolling up or down hill). If it is changing its acceleration, (as it would if the hill was getting steeper or flatter.)... If it is changing its rate of acceleration, (as it would if the rate at which the hill was getting steeper or flatter, increased or decreased...etc. The "etc." means associated with any variable (in this case car position), is an infinite series of change descriptors.

The snapshot does not describe this family of temporal terms. The snapshot tells nothing about where the car will be in a second, a minute, a day, or a year - even if no behavior is taken. When combined with positions and temporal terms of other objects, e.g. trees, guard rails, and other cars, the snapshot tells nothing about if the car will be in an accident and what kind of injuries will occur.

Different behaviors result for the same "external world" when "existing conditions" and "desired conditions"

• are just snapshots or
• snapshots and temporal terms.

Take, for example, the case where the "existing conditions" of a car and the "desired conditions" of a car are identical snapshots. Then, no alternative behaviors are selected. On the other hand, consider the case where the existing conditions include temporal terms that predict the car is on course to impact a tree. Then there is a difference between the existing and the desired condition. And the difference demands behavior to stop or alter the course of the car.

In this temporal view, "conditions" are more than snapshots. They are even more than a series of snapshots. Conditions include a family of "change terms" for each object and variable within that snapshot.

Engineers call this temporal view of conditions a "state space." A state space is the values of variables and the locations of objects – each with its associated family of change terms.

In mathematics, these change terms are called derivatives. In layperson’s terms, a derivative is the difference between two measurements of an object's location, divided by the elapsed time between the two measurements. For example, velocity is the derivative of an object’s location. It is the speed of an object at a location. It could be calculated by taking the number of feet traveled from the initial location during a time period divided by the number of seconds elapsed during the travel. (e.g. Ft/sec.) This velocity is actually the average speed of the object during the time period. However as this time period gets very small, the calculation becomes the velocity of the object at the starting position of the interval.

To summarize the engineers’ view:

• Derivatives and the initial values of variables or positions of objects represent the external world.
• Time drives the representation to create a scenario if no further behaviors are implemented.
• Behavior alters this scenario by altering the representation’s derivatives.
• A future instance of the scenario is the values of variables and positions of objects at the time of behavior, operated on by the behaviorally modified derivatives over time.

Each of us is not so naive that we select behavior by choosing between two snapshot images of a moving world. We don’t learn to steer cars by looking just at pictures of parked and crashed cars.

Neither are most of us so sophisticated that our behaviors are based on state spaces. We don’t consciously use very many members of the large families of derivatives associated with objects and variables. For example, while we do use the maximum deceleration force, "how icy the road is" to determine the maximum speed we drive the car, we do not use yaw rate to determine steering wheel behaviors to recover from skids. We do not use the almost invisible or abstractly visualized societal movements toward war, famine, and pollution to guide our procreative behaviors.

Using something better than snapshots but less than state spaces, we create an imperfect scenario to be used as the condition in selection of behavior. Sometimes the scenario is adequate and our behavior achieves our goals and sometimes not.

Less than perfect scenarios have a profound affect on

• our choice of behavior, and
• our choice of scenario building tools.

The uncertainty created by our experience with incorrect predictions encourages our disbelief in all scenarios. Even when a scenario is based on first principles (or laws of nature) we don’t take its predictions as immutable. On the other hand, we have to chose behavior, and with the certainty of causally created scenarios greatly diminished, mythical scenarios, those supported by cultural approval become equal competitors. Furthermore, when mythical scenarios are the ones culturally accepted, there is real inertia for an individual to create and believe a counter scenario. In such a society the learning to build scenarios that challenge societal beliefs is both not encouraged and un-rewarded.

The uncertainty of any scenario increases with the complexity of the decision environment. One aspect of complexity "time" is particularly troublesome to us. The slower the detectable motions in the present, and or the farther forward in time the results, the more uncertain the future elements of a scenario appear to us and the more we discount them. Unfortunately this has a profound affect on our choice of behavior to control them.

Our task in making a new temporal thinking and learning model is to understand the pathways we currently use to acquire, build, and compare the scenarios we use as our conditions - specifically the temporal limitations in these pathways.

The "imperfect scenarios" that we use to shape our behaviors are not snapshots or state spaces. However, calling them scenarios is not an adequate term either. Scenario is only a story about variables as they move forward in time. To create and select behaviors that implement feedforward control we need a term, which conveys the causal reasons the variables move as, they do. We need a term that conveys the Rube Goldberg magic along with the story. This term I call "image."

Image, behavior, and social outcome were terms presented by Kenneth Boulding in his 1956 book Image. Boulding defined the temporal aspects of image, which makes them useful in my process model of thinking and learning

Kenneth Boulding outlined ten dimensions of image; (The formatting below is added.)

In addition to these definitions, Boulding’s view of how an image comes to exist and how it gets continually modified is useful in understanding my process model of learning and thinking. For example, he defines the meaning of a message as "the change it produces in his image." He sees value scales determining both

• the relative worth of parts of the world image and
• the effects of any messages he receives on any parts of his world image.

Boulding, as do I, believed, image governs behavior.

In Figure 13.2 - 10 I show the differences among the six classes using physiological sensation, memory, level of consciousness of the inputs and outputs, and the transformations they perform. The six classes form a continuum of "condition - creation" that ranges from:

Some human behaviors appear spontaneous. The information that creates the condition that begets the behavior appears from an unknown source. In review, after their execution, these behaviors are attributed to serendipity, intuition, extra sensory perception, or a deity’s advice. The apparent randomness names the pathway class.

Konrad Lorez(?) suggests that human instinct included directives in procreation, hierarchy, and territory. Freud[1933], Dollard and Miller [1950] describe many social behaviors they believe have subconscious origins. Wilson [1975] suggests that humankind’s destiny, is part of a genetic code. They all believe the genetic code produces information that drives our behavior. This class of pathways is given the name "genetic."

Our hearts keep beating, our diaphragm muscles keep our lungs exchanging air. We don’t have to consciously think about keeping our pours sweating, and stomach churning. This class of pathways is given the name "autonomic."

Skinner [l974], Rotter [1954], 1972], and Bandura[1977], suggest that behavior is dependent on information drawn from direct personal experience. Skinner [l953] believed that the individual consciously chooses to repeat the behavior that provides the best previously experienced reward. This class of pathways is called "Behavior based cause and effect." A more common name might be "trial and error."

[Mischel 81] Rotter[l972] and Bandura[1971] suggest that individuals choose behavior based on expectation, however, in their view the reward does not have to have been a direct result of their own behavior. The expectation can be learned from observation of a system they have not perturbed. The social learning theorists believe that an individual can learn to expect an outcome of a behavior by watching, listening to, or reading information created by other people. This class of pathways is called "Transmission based cause and effect." A more common name is cultural transmission.

Expectation, (image sequence, specifically future image), can be created based on partial state space information and causal relations. That is the image sequence does not previously have to be created by behavior based, or transmission based "cause and effect" pathways. Instead the future image is created (inferred) using a simulation of the physical world.

Boulding derives future image from relational image. He writes:

A future image is a present mental representation of future conditions.

However, all future images are not functions of preceding events. For example, later events in an often-told fairy tale are future images of the initial events. However, there is nothing within the earlier events that predict the outcome of the tale. The portion of future images which are important to this discussion are those whose future existence are predictable from preceding conditions without the person (who makes the prediction) actually having previously experienced them. That is, the "end of the story" can be predicted from the first parts without having heard the story told before.

These predictions, based on cause and effect, can be formed based only on initial conditions and elapsed time. Thus, for us the important future images are the ones where present images where mentally transformed by existing in-process motions or behaviors that modified the in-process motions. For us, the important effect of these mentally manufactured future images, is that they add to the behavior selection process a competing alternative condition that otherwise would have been absent.

Other effects of mentally manufactured future image beyond shaping the existing conditions, include:

• creating desired conditions
• performing comparisons,
• creating a concept of too small, too large
• identifying alternative behaviors, and
• creating additional sets of resulting conditions.

Using the "image" contained within the mind of the human being, Kenneth Boulding completes his description of the universe. His universe has two countervailing forces. The first is the second law of thermodynamics which dictates that all things tend toward the most disorganized state a state with an evenly distributed gray continuum of things that has no more potential for disorganization. The second force creates or maintains pockets of order inside this general trend toward chaos.

He classifies and ranks seven levels of organizational forces. He names these levels static structures, clockworks, thermostats, biologic, botanic, zoologic, and human. At one extreme of his organizational force continuum, he sees static structures as passive resistance to disorder. At the other extreme, he sees human beings as the most powerful active forces creating new pockets of order.

According to Boulding, the human being is the most powerful organizational force because her image, her means of guiding behavior, is extensible.

While Boulding mentions many dimensions of image expansion and describes science as the subculture of the extending process, his most powerful contribution for our process model of human thought is his belief that a human being's image is extensible, not only in spatial breadth of the present, but in temporal depth into the future.

Boulding believed that humankind's image can contain knowledge of future conditions that are shaped by the choices of his own present behavior. That is, some alternative future images are extensions of knowledge that result when alternative behaviors are mentally preformed on present conditions.

Boulding’s theory underpins the feedforward capacity of a process model. That is a future image resulting from a present condition extended into the future can be the motivation to identify and implement a behavior.

From Boulding's descriptions, images are the conditions within the process model of behavior selection. The pathways within these models acquire, store, recall, manipulate, and compare images. The creating of these images I call learning. The learning of "pathways that create images" is learning to learning.

The important subclass of images that relate most directly to the temporal mechanisms of our thinking I call "future images" (FI). The learning pathways that create future images I call "future imaging skills" (FIS). The learning of these future-imaging skills is one definition for the acquisition of temporal sight. The part of temporal sight on which I focus deals with the mental abstractions of motions too slow or too fast to be sensed and manipulated directly by physiology. Again this is getting ahead of my story but it does establish the vector.

Figure 13.1 - 10 shows parallel acquisition pathways producing competing conditions. The temporal part of these conditions, at least with respect to feed forward control of the physical system, is the future image. Next I describe:

• the means by which acquisition pathways create future image, and
• how these means determine if the future image is the kind that implements feedforward control.

The random, genetic, and autonomic pathways produce subconscious images that influence behavior. These images do contain a sub or unconscious truncated future image. For example, a truncated future image could contain a connection between the secession of an internal dissonance and behavior. These future images are called truncated because they carry with them no view of the change in the future states of the external world that also result from the behavior.

If all the competing images, in any decision environment are unconscious or subconscious, that is they all have truncated future images, then behavior selection can be considered non-conscious. That is when the behavior springs forth, the individual can not recognize a preceding conscious process.

If these truncated future images compete in a behavior selection environment with less truncated (conscious) future images produced by behavior based, transmission based , and inference based pathways, (from the left side of Figure 13.2 - 10.) the behavior selection process is conscious.

This is true even if the chosen behavior reflects the dominance of a truncated future image. For example, under normal circumstances a person would choose life over death. However, a man kills his wife’s lover even though society will take his life in the gas chamber for his action. This distortion in his behavior selection has a logical explanation. At the time he discovers the lover, his image of the gas chamber taking his life is small and weak. While the competing subconscious image (of another man supplanting his genes) (his genetic rage) is large and clear.

Boulding offers an explanation for why satisfying a genetic rage dominates an expectation of certain death. His explanation uses a description of the physiology of the human eye as an analogy for the range of pathway’s that produce future image. His analogy separates image into conscious, unconscious, and subconscious.

While the human eye has a wide field of view, only a small part of this view, that part that falls on the retina’s fovea is in sharp focus. To see other parts of the view in sharp focus the eye must be moved so that that part of the image falls on the fovea area.

Like the eye, the mind can not hold in conscious view all parts of a mental image. Like the eye the mind has a scanning process that brings the unconscious into the conscious. The subconscious portion of the image is that part of the image which is not made available by the scanning process (p53.)

In this view, the strengths and weaknesses of any scanning process become powerful determinants in the behavior selection environment.

Boulding believes, "how the scanning process works," is one of the great unsolved mysteries. The mind may not know what it is trying to bring into consciousness from the unconscious until it sees it. The mind’s scanner may not know what to recall before it recalls it. "We have a curious capacity; for giving ourselves examinations. We know how to write the questions that we have answers for." p.53.

In the proposed process model the six classes of pathways are like six scanning processes. The temporal aspects of these pathways describe our time blindness. Their temporal mechanisms are important in creating our temporal sight. To establish feedforward control requires the use of the left most pathway in Figure 13.2 – 10 - inferred future image.

In Figure 13.2 -10, the autonomic, genetic and random pathways do not appear with indications of future image. The behavior based and transmission-based pathways have future image indications but the future images they produce are not the kind that can implement levels of feed forward control. Only the future image created by an inferred cause and effect pathway can facilitate the feed forward control for which we seek understanding.

Consider some cases in nature where it appears that future image plays a role in behavior selection when in fact it does not.

When Wooldridge describes the difference between thought and instinct, he argues that instinct has no future image. He feels much of the very intricate actions of animals that we attribute to conscious thought are only stored behavior patterns that were part of the subconscious mind at birth. What we see and sometimes judge as conscious behavior is the playing of these complex programs. To prove his point, Wooldridge provides four examples that show a disconnection of the environmental stimuli, which triggered the program and its function. The four examples come from the genetic preprogramming section of The Machinery of the Brain.

First, a male moth released into a cage, where a female in heat and her scent glands (not her vagina) have been separated and attached to opposite ends of the small cage, heads for the scent glands rather than the female moth's vagina and proceeds to deposit his sperm totally ignoring the function of his action.

Second, the Thermometer bird of the Solomon Islands incubates its eggs, by burying them in the warm beach sand. The small chick hatches by pecking through the shell. It then starts a climbing motion that brings it to the surface of the sand. Not only is it difficult to get out of the shell and, though buried alive in the dark, climb up through the loose sand, but when the chick breaks into the sun light, the sand is searing hot. The chick jumps to its feet, opens its eyes, determines the location of the nearest shade and runs for cover before it is broiled. It could be asked, in the face of all the chick's actions, why it isn't at the top of the earth's cognitive order? The answer is obvious. The chick is not thinking. It is just running programs genetically implanted in its brain. In the chick's case, each program is a one-shot. It can only be run once and then it is destroyed. If the chick is captured during the running program and re-buried in the loose sand, the chick does not revert to the climbing motion but continues its running motion. The climbing motion program is gone.

Third, the Sphex wasp has a very intricate process for laying its eggs. She lays her eggs in a burrow. Then she stings a cricket in such a way that it is paralyzed rather then killed. The cricket lives and is preserved by its own bodily functions until the grubs hatch and use it for food. After the wasp drags the paralyzed cricket to the door of the burrow. She then enters the burrow to check to see that the eggs have not been disturbed. If they are OK, she returns to the cricket and drags it into the burrow. The checking of the burrow gives the illusion of thought. What destroys the illusion is that if the cricket is moved several inches while the wasp is checking the burrow, the wasp will not drag the cricket into the burrow, but only to the door. Again she will go inside to check if everything is all right. Moving the cricket away from the door several inches will trigger the same drag and recheck behavior tens of times. Thus, it is not the function of the behavior that controls it. It is not a desire to ensure the eggs are all right that causes the wasp to recheck the burrow. If the wasp had a conscious brain it would already know it had just checked the eggs. The recheck behavior is instead triggered by the dragging of the cricket near the burrow.

Fourth Wooldridge describes the octopus that builds a wall of stones to hide behind so that it can jump over and get its prey. Given clear plastic blocks as the only building material the octopus will build a wall of them and hide behind it even though it has an eye and can see through the wall that is suppose to be hiding it.

These examples predict that simple instinctual beavers in animals are not the feedforward control for which we are searching.

Next let me give an example of much higher order behavior in humans where instinct

Human thought requires input , process, and output. Human action requires stimulus, computation, and actuation. Each of the three can be either conscious or unconscious. The combinations of consciousness describe the utilities of future image in behavior.

Consider the act of "increasing the heart’s rate to be able to perform a future act," like, flight from danger.

The information to increase the heart’s rate reflects conscious sensation of external world conditions.

However, the individual has no conscious role in the computation, which creates the information that make, the heart rate increase.

Thus the individual can not have before the fact, a future image of "being able to flee faster or farther" if a heart beats faster before flight. No one makes a conscious plan to raise his or her heart rate, seconds before the game commences.

I suggested that the conscious alternatives are in competition with the unconscious ones. That conscious alternatives have conscious future images. And that, the presence or absence, the strength or weakness of the future image determines how well a conscious alternative competes with its competitors in the behavior selection environment.

I have discovered the existence, if not the exact content of a scanning process that separates the conscious from the unconscious and the unconscious from the subconscious.

The abilities and limitations of this scanning process determine the boundaries among the three. Images, believed to be impossible to extract from the unconscious or the subconscious, may be invisible due to under used or underdeveloped aspects of the scanning process.

Many of these aspects may be temporal. Investigations into temporal limitations in this scanning process will help us extend our view of our temporal blindness. They will help us define the mechanisms that implement temporal sight.

There are many conditions that we see. There are many conditions we partially see. The conditions we don’t see are our blind spots in our decision making. The conditions we don’t see are either unchanging or changing. I call them "static" and "in-process" conditions. While missing either form of information is equally disruptive in rational decision-making I will focus on the "in process" conditions because they are most disruptive in the production of future images.

Let me provide an example of blindness to "in-process" conditions.

The answers respectively are yes, no, yes, YES!!! The surprise answer to question number 4 is the start of our investigation.

You would probably not have been surprised at that answer if had I asked questions three and four in their temporal form.

• Before a meal is your hunger increasing or decreasing?
• After a meal is your hunger increasing or decreasing?

These questions eliminate the static answers (not hungry or hungry) and force the answer to be temporal.

However, I did not ask the questions in that restricted way. And in its loose form you allowed yourself to transform it into a static form of hungry and not hungry and then chose ""not hungry. It is the same mistake we made in transforming the car skid control problem from its in-process form "rotating" or "not rotating" into its static from "rotated" or "not rotated" and why we missed our chance to came up with an easy robust way to control skids. So what can we learn from these examples, useful in creating a temporal learning model?

Consider how this blindness to the fact that all living people are:

might effect behavior. The motivation "to get food" derived from the static condition "hungry" is different before and after a meal, while the motivation "to get food" from the "in-process" condition "getting hungry" remains the same before and after eating.

If the static condition governs behavior, then, after a meal, lie down, siesta. Hunger will be the cue to get up and start looking for food. If the in-process condition governs behavior, after the meal go back to work to ensure future meals.

Its a good thing that the "going back to work immediately after a meal" behavior is governed by several factors other than temporal sensitivity to the in-process condition. (Remember, most of us did answer "NO" to the fourth question above.)

There are many reasons to go back to work independent of temporal insight. They fall in two groups:

In Figure 13.1 -10, we see a "resulting condition" follows from an image of behavior operating on the problem condition. The detail of this process, Figure 13.2 -10 shows that as many as six different process types can be working to create a resulting condition for each behavior alternative.

Because the comparison process either results in behavior or a continued search for behavior each resulting condition must be addressed serially. By "serial" I mean Ë the resulting conditions in a search, are each compared to a desired condition in some order. When a "a resulting condition is found acceptable,