The Mathematics behind the Xzistor Brain Model

Originally led by a request from Prof. Judea Pearl (@yudapearl) on Twitter, more scientists are now coming forward and asking questions about the mathematics behind the Xzistor Mathematical Model of Mind.

It seems like more and more of them are finding it difficult to fault the model’s underlying principles – the fact that the brain can be viewed as a control system that can be described (principally) using control theory. This approach allows for a powerful way to explain subjective (artificial) Emotions and (artificial) Intelligence. Unlike many of the current incomplete and unimplemented ‘Theory of Mind’ paradigms, this model is evidenced by the convincing emergent behaviours observed in simple simulations and physical robots controlled by Xzistor artificial brains (computer programs).

Skeptical at first, many scientists have now had enough time to think about the fact that any model claiming to emulate the the human brain should start with a very basic system (like a baby brain – as Turing told us!) and then grow to full complexity by learning – storing associations, with these experiences given meaning and nuance through tagging them with homeostatic/allostatic emotions.

This is at heart what the Xzistor brain model is.

So, to respond the Prof. Pearl’s interest and request, and the others who have responded to the YouTube interview with my neuroscientist collaborator Dr. Denise Cook (here), I have decided to offer a meaningful explanation of the mathematics behind the Xzistor model. This is the basic mathematics underpinning the computer programs that ran the early simple human-like simulations and robots – the so-called Xzistor ‘proof-of-concept’ demonstrators.

After doing some reading on Prof. Pearl’s own phenominal career and achievements – see his personal website Judea Pearl here: http://bayes.cs.ucla.edu/jp_home.html – I realised again why he is regarded as one of the founding fathers of AI. I liked the idea that he offered a ‘primer’ to his very advanced inference modelling.

So, I thought I should start with a similar gentle introduction to the mathematics of my model. Then I can go to the full details in mathematical notation after that.

Here is an easy introduction to the mathematics of the Xzistor Mathematical Model of Mind using a Simple Robot Explanation.

I ask only one favour: Do not think the model is too simple to ‘principally’ emulate the human brain. To say it is too simple would be like saying a baby’s brain is too simple to learn and, over time, develop the ability to design jet engines or solve complex arithmetic.

Some important aspects are admittedly a little glossed over in the above slide pack – specifically how truly ‘subjective’ emotions are created by the model. For this it could be helpful to get some wider context around the Xzistor approach to Machine Emotions – here:

The detailed description of the algorithms and the mathematical equations inside them follow here. This was systematically extracted from the rather bulky 500+ page Manifesto of the Xzistor Mathematical Model of Mind including papers, books, the original patent specifications and the actual code used to drive the simulations (C++ and OpenGL) and physical robots (30 000+ lines of Java code including comments!).

I. INTRODUCTION

This Xzistor Mathematical Model of Mind describes a method for modeling the human brain. The functional brain model is substrate-independent and was developed to:

1. Provide a principal understanding of the working of the brain, specifically the mechanisms of cognition and emotion.

2. Serve as a basis for a complete cognitive architecture, providing autonomous agents with innate human-like intelligence and emotions.

The model simplifies and serializes the main neurobiological functions of the brain into a single logic loop containing various algorithms. By means of simplifying assumptions, all functions performed as part of these algorithms can be defined in mathematical terms.

II. HIGH LEVEL LOGIC

At the highest level, the Xzistor Concept uses a very simple logic loop to simulate the brain:

1. SENSING (obtain sensor inputs)

2. PLANNING (translate sensor inputs into behavior commands)

3. BEHAVIOURS (perform behavior commands using effectors)

4. Go back to 1. SENSING

Whereas the human brain has the ability to do parallel processing, it still in most cases goes through the same sequential steps and take time to register a sensory input, compare it with what has been learnt, plan what action would be appropriate, and finally send the effector (motion) comands to the muscles. Tests with Xzistor simulations and physical robots have shown that repeating this logic loop, containing all the required algorithms, at < 0.1 Herz approximates the paralellel processing of the human brain adequately to give rise to smooth human-like behavaiours in agents.

The diagram below shows the five functional algorithms of the Xzistor brain model and their linking. These algorithms are performed left to right – and repeated – in a constant loop:

1. SENSING ALGORITHM

A Sense translates a physical condition or variable (V) in the environment or body into a corresponding representation (S) in the brain:

Translation Functions for Senses

The translation functions above refers to any means whereby a sensed environmental variable (V) is changed into a representation (S) that the instantiation of the Xzistor brain model can process. An example would be an optic sensor that takes a sensed optic state (V₁) and translate it via a video camera processor (X_s1) into an array of RGB values (S₁) that a digital computer program can use to perform numerical calculations on. For different technologies, different means can be used to achieve this translation function. The only requirement for the model is that there will only ever exist one representation (S) for every unique incoming variable (V) – to a resolution appropriate for the application.

2. DRIVE ALGORITHM

The Drive Algorithm was derived from the bioregulatory processes in the body and brain, most of which reside in the sensory, endocrine and autonomic nervous systems. These mechanisms attempt to maintain homeostasis/allostasis of body states by regulating one or more Control Variables.

A Drive is in most cases part of a negative feedback closed loop control system which alerts the body and brain when a Control Variable is moving out of range. It generates the error signal which indicates to what extent the Control Variable is out of range and thereby indicating the level of threat it poses to the system.

In later descriptions of the model, Drives are also referred to as Urgency To Restore (UTR) mechanisms and a distinction is drawn between two types of Drives, namely Body UTRs and Brain UTRs. Body UTRs are Drives that perform homeostatic regulation of a Control Variable (CV) located in the body, while Brian UTRs are Drives that perform homeostatic regulation of states recalled from the Association Database (memory). This aspect will be further explained in the discussion on the Association Algorithm. A Drive translates a Control Variable (CV) error signal into a corresponding representation in the brain, and can be expressed in mathematical terms as follows:

Translation Functions for Drives

The translation functions above refers to any means whereby a sensed/measured Control Variable (CV) is changed into a representation that the instantiation of the Xzistor brain model can process. An example would be a digital thermal sensor inside or outside the body of the virtual/physical agent that takes a Control Variable (CV₁) reading – tempearure in this case – and translates it into a digital error signal (ES_d1) between the value 0 and 1. The error signal will depend on how far the reading departs from the setpoint value. The translation function (X_d1) will then use the Control Variable (for identification) and Error Signal (for status) to create the representation of the Drive (D₁) that the brain model can process. This is an example of a Body UTR Drive (or just a Body UTR). The Error Signal (between 0 and 1) will allow this Drive to communicate with the brain model the ‘level of urgency’ with which it should be restored to maintain a safe external/internal temperature. This can then be compared with the ‘urgency’ (or UTR value) of other Drives, to determine what actions should be prioritised. The only requirement for the model is that there will only ever exist one Drive representation for every unique incoming Control Variable / Error Signal combination (to a resolution appropriate for the application).

By way of another example, we can consider the simple negative feedback closed loop control system aimed at homeostasis of blood borne water (H₂0) – see figure below. In this case the Drive representation in the brain could be a numerical value and the brain could be an Xzistor model computer program (executing the logic loop).

As the H₂0 Drive increases in time, it reaches a Detection Threshold (DT) – also referred to as the activation level – where the signal, which is already identifiable by the brain model, will become a contender to drive action selection. The H₂0 Drive will continue to increase as the blood borne H₂0 is depleted. The rising part of the curve we will refer to as the Deprivation (DEP) Regime. This regime can be expressed in mathematical terms as:

The brain creates a dedicated Deprivation Emotion (DE) representation which is somatosensory in nature, meaning it will consciously be ‘felt’ by the brain as if located in part(s) of the body (in humans this state could typically be generated in the insula, where competitive adjudication is performed – comparing activation versus inhibition levels of active Drives – to aid the thalamus in action selection). It will later be explained, as part of the Association Algorithm, why the Deprivation Emotion state DE will have to compete with the Deprivation Emotion states of other Drives based on Drive strength or ‘urgency’ to be prioritsied by the executive part of the brain model and why it will be viewed by the model as ‘negative’ as it is turned into an avoidance state through operant learning:

With the ingestion of water, the Drive curve will slope downward. The vertex point where the curve changes direction is of prime importance to the brain model and we will refer to it as the Satiation (SAT) point or Satiation (SAT) Event, and the declining part of the curve as the Satiation (SAT) Regime. This regime can be expressed in mathematical terms as:

The SAT point is where the brain detects that something in the environment or body is causing the H₂0 Drive to decrease (or be Satiated), and it needs to store all the information about – and leading up to this event – for future use. In the section on the Association Algorithm we will discuss the storage of ‘Associations’. Every Drive, whether it is a Body UTR Drive or Brain UTR Drive, is assumed to have a value between 0 and 1, where 0 indicates complete homeostasis, and 1 indicates the maximal departure from homeostasis (the most critical or aversive condition).

While DEP is simply the value of the Drive between 0 and 1, SAT is the derivative state given by the rate at which the Drive decreases (or the Drive curve slopes downward). In the case of SAT, 0 will indicate no decrease in Drive:

And 1 will indicate an instantaneous drop to 0:

For basic computer program implementations of the model the value of DE can be multiplied by -1 and set between 0 and -1 (i.e. always negative and dependent on the value of D).

The larger the decrease in Drive, or the steeper the downward slope of the Drive curve, the higher the value of the SAT will be. In the human brain SAT is a key condition for bioregulatory and cognitive control, and the model uses it as the basis for Reinforcement Learning (RL) and ‘Reward-based Backpropagation’ – these important mechanisms are discussed later. The prime objective of the brain in terms of the model always remain to constantly minimize DEP and maximize SAT.

The brain creates a dedicated Satiation Emotion (SE) representation which is somatosensory in nature, meaning it will consciously be ‘felt’ by the brain as if located in parts of the body (this state could typically be generated in the insula and presented to the thalamus for action selection). It will later be explained, as part of the Association Algorithm, why the Satiation Emotion state SE will also have to compete with the Satiation Emotion states of other Drives based on rate of Drive strength reduction or ‘recovery’ to be prioritsied by the executive part of the brain (e.g. thalamus) and why it will be viewed by the brain as ‘positive’ as it is turned into a pursual (or approach) state through operant learning:

For a basic computer program implementations of the model, the value of the Satiation Emotion (SE) can be turned into an absolute value and limited between 0 and 1 (i.e. always positive and dependent on the rate at which the Drive recovers from its Deprivation regime).

Multiple Drives

The model caters for many Drives being active simultaneously in the body and brain. The figure below shows three different Drives active at the same time:

Each Drive will measure its own Error Signal (ES) based on its own Control Variable (CV). We can define the Total Drive (D_tot) as the sum of the three Drives (i.e. D₁, D₂ and the Prime Drive) as indicated on the Drive versus time graph in the figure above.

We will refer to the strongest single Drive (highest value between 0 and 1) as the Prime Drive. The total Deprivation suffered between time T₁ and T₂ will be given by the area under the D_tot curve between T₁ and T₂ when plotted against time and can be calculated as follows:

In the graph above we can see that the SAT (rate of reduction) achieved by D_tot is less than that of the Prime Drive because only one of the three Drives is Satiated while the others are still increasing, meaning they are in Deprivation. Sometimes when there are many Drives to consider it is convenient to normalize the D_tot to a value between 0 and 1 (and multiply by -1), where 0 will indicate no Deprivation and -1 will indicate the maximum total Deprivation the agent is capable of suffering based on the total number of Drives. To normalize D_tot we do the following:

Interdependency of Drives

The model accommodates the interdependency of Drives. In the graphic above, for instance, the Control Variable of Drive 1 could have been influenced by the Control Variable of Drive 2. This type of coupling or dependency often exists between biological Drives and some Drives extensively use other Drives to increase the total Deprivation (DEP) value of D_tot to collectively make it a stronger avoidance state. Some Drives also make use of Reflexes that influence the Control Variables of other Drives e.g. the Fight-or-Flight Reflex. The F-o-F Reflex is a complex mechanism which, in the human brain, directly activates the Control Variables of numerous Drives simultaneously to rapidly escalate D_tot using the endocrine system and autonomic nervous system mechanisms. Many Drives use the F-o-F Reflex to increase DEP – some at low levels (mild stress) and some at a higher level (severe shock). Pain is modeled as a Drive using body mapped pain receptors detecting excessive pressure, temperature, shear force and tissue damage as Control Variables, inextricably coupled with the autonomic F-o-F Reflex. Some bio-regularity processes do not have detectable representation in the brain (detectable by the executive structures of the brain e.g. thalamus, basal ganglia, etc.). These ‘quiet’ control systems are not deemed Drives by the model, but some do influence Drives (e.g. blood volume (quiet) can influence Thirst levels (a detectable Drive).

3. REFLEX ALGORITHM

A Reflex is triggered by a Sensory State (S) or a Drive (D), resulting in a representation in the brain, interpretable as a preprogrammed set of motion commands. When triggered by a Sensory state, we have:

When triggered by a Drive state, we have:

The Reflexes triggered during Deprivation are normally different from those triggered during Satiation. We will group Reflexes into four types:

For the Deprivation (DEP) condition:

1.Involuntary Deprivation Reflex (triggered when the Drive is in Deprivation) e.g. shivering when cold.

2.Learn-modifiable Deprivation Reflex (triggered when the Drive is in Deprivation) e.g. crying when hungry.

And for the Satiation (SAT) condition:

3.Involuntary Satiation Reflex (triggered when the Drive is in Satiation) e.g. prostate contractions.

4.Learn-modifiable Satiation Reflex (triggered when the Drive is in Satiation) e.g. suckling of an enfant.

4. ASSOCIATION ALGORITHM

The Association Algorithm uses the representations generated by the other algorithms to store and later re-evoke Associations. Association-storing is achieved by ‘linking’ and ‘storing’ representations which are all present at the same time in the brain.

The representations present in the brain at time = t will be stored in the Association Database as a mal-tuple A_t of the form:

Association Forming and Updating

With every cycle of the Xzistor logic loop, an Association (A_i) is stored by linking all of the above representations and storing them as a combined entry into the Association Database. For simple digital applications like virtual agent simulations or small robots controlled by an Xzistor computer program, typcially 10 Associations can be stored/recovered per second which will result in smooth movements when in future the Motion Algorithm fetches these Associaitons and executes the learned effector Motions (M) at 10 updates per second (discussed in more detail below).

The Association Algorithm contributes to modelling many important brain functions and effects like learning, daydreaming, sleep dreaming, recalling, recognizing, context, effector motions and problem-solving. How these are achieved by the brain model will be described in Section 8.1. LINKING THE 5 BASIC FUNCTIONAL ALGORITHMS.

Recognition

With every cycle of the Xzistor logic loop, a check is also performed to see if current incoming representation sets (containing e.g. S_i, D_i, etc.) have a match in the Association Database (see Anchor States below). A match would mean that the current incoming Association has been ‘recognised’ as part of an existing Association which the model can now use as a source of information (e.g. re-evoking Emotions around currently perceived objects or perhaps access prior learning around effector Motions to avoid/approach these objects). The information from a ‘recognised’ Association will be used and synthesised (combined) with the incoming representations at various points during Xzistor logic loop, to create an updated version of the Association that will be stored back into the Association Database.

Anchor States

As we have seen, any number of the representations (from 1 to many) forming part of an Association A_i can, when checked against the Association Database, re-evoke an exisisting Association and make the information contained in its different representations available to the brain model for use. For modeling purposes it is however convenient to fix the number of input representations that, collectively, will re-evoke such an existing Association. This subset of representations which we need to be present at the same time, in order to ‘unlock’ or re-evoke an Association (A_i) from the Association Database, we will call the Anchor State (AS), and it will be a subset of the representations comprising an Association:

Typically, a good trade-off between accuracy and processing overhead can be achieved by choosing the Anchor Sate (AS) to only contain the main Drives and the main Sensory States, i.e:

Impact Factor

Associations will have a specific strength or ‘Impact Factor’ when stored. In the model the Impact Factor IF is a function of three parameters:

1. EIF, Emotional Intensity Factor i.e. the highest of the two absolute values (|D_tot| or |S_tot|) representing emotional salience.
2. ET, the elapsed time since the Association was last recalled (re-evoked).
3. RR, the number of times re-evoked or ‘reinforcement repetitions’.

Impact Factor  = f (IF, ET, RR)

The IF provides a way to rank Associations by how important they are to the system (in finding Satiation). Associations with high Impact Factors will be stored near the top of the Association Database and first accessed when experience (learning) is seeked during problem-solving (discussed later).

Forgetting

Less impactful Associations are not forgotten but stored in a manner whereby they will only be accessed when search criteria are very specific and their is adequate time for the model to Thread through the Association Database (Threading is discussed later and will explain how the Impact Factor of an Association can aid in providing ‘context’ around a real or recalled object or concept).

This provides the system with a long term and short term memory.

Emotions re-evoked as part of recognising Associations

As a general rule, when an Associaton is recognised, all of its representations become available as information that can be used during that cycle by the brain model to help determine the most appropriate next behaviour. This does not mean that Emotions are re-evoked as if from their Drive representations, only that their information can be used without regenerating the pseudo-somatosensory representation (the actual feeling). Just by recognising a food source when not hungry will not create a hunger Emotion in humans, and actual Pain cannot be created by simply looking at a cactus that had caused Pain in the past.

There is however one important exception to this rule.

The Xzistor model uses the way it defines Senses, Drives, Emotions and Associations to model the autonomic nervous system (ANS) which in humans comprise the Sympathetic Nervous System (SNS) and Parasympathetic Nervous Systems (PNS). Simply put, the SNS causes stress e.g. the Fight or Flight (FoF) Response and the PNS counters that with a state of relaxation or calm. Except for the preprogrammed Reflex reactions that trigger the FoF i.e. the SNS, we find that in the human brain activation of the thirst centre, hunger centre and pain centre, etc. also triggers the SNS causing a stress state in concert with these Drive states. As all these Drives increase in strenght, the SNS response also increases in strength, and as these Drives decrease in strenght, the SNS response also decreases in strength. In humans, this SNS response is transferred via the hypothalamus and adrenal glands to the gut (vagus nerve) and then projected to the brain via the brainstem as a visceral somatosensory body state to areas like the insula where it will create pseudo-somatosensory Emotion representations. Because the SNS and PNS become activated in concert with all other Drives (i.e. SNS activation during the Deprivation phase and PNS activation during the Satiation phase), it becomes another type of Drive we will simply call the Stress Drive.

But this Stress Drive has a unique feature in that its Emotions can be re-generated by merely observing or thinking about an object that had been encountered in the past. So as an Association is recognised, the Stress Drive and Stress Emotion representations, stored at the moment the Association was formed, will be regenerated as actual Emotions (unlike Hunger, Thirst, Pain, etc.). In effect the representation of the Stress Emotion in the brain as part of an Association, will act as a Control Viariable (CV) to trigger the Stress Drive and the Stress Emotions – this can be a Deprivation Emotion (bad) or a Satiation Emotion (good).

So now, for every cycle, the Stress Drive and associated Emotions are firstly generated by the net effect of all the other Drives in the system as they are activated, but also influenced by the recognition of a Stress Drive and Stress Emotion representations in an existing Association. Because there is only one autonomic nervous system, all the above effects will work in on the same Stress Drive system and create a consolidated Stress Emotion.

When the Xzistor brain model must determine the next behaviour, it will weigh up the strengths (between 0 and 1) for all the Emotions, including the consolidated Stress Emotion activated by both the other Drives and Stress Emotions from the recognised Associations – and act on the strongest.

To descern between Drives that are triggered by Control Variables in the environment and body, and those triggered by stored Emotion representations in recognised Association – we will refer to two types of Drives: Body UTRs and Brain UTRs.

Body Urgency To Restore (UTR) mechanims are Drives with Control Variables in the environment and body, and Brain Urgency To Restore (UTR) mechanims with Control Variables in the recognised Association.

For more complex instantiations of the Xzistor brain model, both the Stress Drive and the Nausea Drive (to a lesser degree) can be combined to create a Stress Drive and Disgust Drive that can be instantly recognised (regenerated) as objects are recognised in the environemnt (the academic literature provides evidence of the presence of both stress and nausea representations in the insula). The Emotion representations that also provide information on the strengths of these Brain UTRs (between 0 and 1) are often stronger than the other Body UTRs and will drive behaviour of the system – meaning the system is acting out of stress or disgust, and not immediate homeostatic needs.

An interesting behaviour that can be generated based on the above is where the agent now just ‘thinks’ about a painful experience, and performs a learned behaviour to avoid getting into the painful situation again – even without experiencing any Pain. This is referred to by the Xzistor brain model as allostatic Emotions (based on Brain UTR Drives) versus the homeostatic Emotions (based on Body UTR Drives).

These mechanisms supprted by the Association Algorithm contribute to a fully implementable brain model that explains and demonstrates many of the more elusive brain phenomena like recognition, acting out of stress, acting to seek stress relief, acting out of disgust, acting to avoid disgust, acting on the strongest Emotion (whether originating from the body or brain), preference, fear of Hunger, fear of Thirst, fear of Pain, fear of Cold, fear of Fatigue, etc. – also learning, planning and problem-solving based on these Emotion states originating in the body and brain. These effects will be explained in more dteail in Section 8.1. LINKING THE 5 BASIC FUNCTIONAL ALGORITHMS.

4. MOTION ALGORITHM

The Motion Algorithm will translate any of the following into effector motions (actions):

1.A Reflex input

2.A recognised Phobia (where the Association was preprogrammed)

3.A recognised Association (where the Association originated from learning)

4.Motion commands forced on te system by an external party (e.g. robot tutor)

LINKING THE 5 BASIC FUNCTIONAL ALGORITHMS

The Linking Algorithm performs the integration of all the algorithmic elements that control the interfaces between the above 5 functional algorithms, and is effectively the executive part of the brain model (comparable to many of the functions performed by the thalamus in the human brain). What information is passed between these functional algorithms for every logic loop cycle is crucial to how the Xzistor model provides an agent with human-like functions and effects like sensing, subjective emotions (like feeling hunger, thirst, pain, cold, warm, fatigue, anger, pain, fear, stress, etc.), learning, language development, dreaming (day dreaming and sleep dreaming), thinking (including contextualisation and problem-solving), coordinated goal-driven motions, etc.

The full logic loop will be discussed next. It assumes an implementation comprising compiled computer code e.g. C++, Java, Swift, etc. driving a physical robot, but could equally apply to equivalent neural network (hardware) systems – or a combination of both (the functional model is therefore means agnostic). To change this into a simulation, the physical elements of the robot and the environment are simply replaced by virtual models.

2. INITIALIZE – Initialize all variables and arrays (e.g. Association Database).

3. TUTOR OVERRIDE – Open tutor control interface of the agent. This allows the tutor to guide the agent during initial learning. Typically, the tutor will take over control of the robot effectors (e.g. motors) and demonstrate a Motion (M) a few times to show the agent how to solve a problem like opening a food source (simulated) when the Hunger Drive has gone high.

4. MAIN LOOP – The main loop is entered which is repeated until the tutor interrupts the program, or power to the system is cut.

5. READ SENSORS – Based on the latest incoming sensory Variables (V_i), the sensory representations (S_i) are generated by the Sensing Algorithm, e.g. video, tactile(touch), audio, color, temperatures, shock, accelerometers, etc. For a simple computer program instantiation of the model, all these representations could merely be unique numerical values. Sensory representations can in some cases directly trigger Reflex reactions (R) which could trigger autonomic Stress Drives and Emotions (DE or SE) and/or Motions (M).

6. RECOGNITION (READ BRAIN UTRs) – The representations that are part of the Anchor State (AS) are compared with those in the stored Associations (within the Association Database) to see if any of them correlates and can thus be recognized. If the Anchor State representation is recognized, it will immediately re-evoke the autonomic Stress Drive representation in the recognised Association – positive (stress) or negative (relief) – and the model will be ready and waiting to combine this Brain UTR Drive representation with will all the other autonomic Stress Drive representations generated symbiotically by all the other active Drives.

Anchor State representations that are recognised can also trigger Phobia (Brain UTR) reactions which will generate negative autonomic Stress Drive and Emotion representations with associative Motions (M). Phobias are just Associations that are not learned, but pre-programmed into the Association Database effectively creating instinctive negative Emotion representations (fears). Associations with positive autonomic Stress Emotion representations can also be pre-programmed to trigger positive Stress Drives for stress relief or calming, upon recognition.

7. READ BODY UTRs – For the current cycle of the program the Drive Algorithm will obtain the Control Variable (CV_i) representations and use them to generate Body UTR Drive representations for all the Body Drives.

8. CREATE EMOTIONS – The Drive Algorithm will use the Body UTR and Brain UTR Drive representations from step 6. and 7. above to calculate the positive (SE) and negative (DE) Emotion representations for all the active Drives (both Body UTRs and Brain UTRs).

To obtain a consolidated autonomic Stress Emotion representation the following originating mechanisms exist as part of the model:

1. Reflex – A Sensory representation (input) directly creates the Stress Emotion (e.g. instinctive threat object, loud noise for Deprivation (stress inducing negative emotion) or sound of running water for Satiation (calming positive emotion).
2. Phobia – A pre-programmed Association with a negative Stress Emotion is recognised via its Anchor State (e.g. complete darkness creating Deprivation).
3. Body UTR – Every Body UTR will always create a proportionate negative or positive Stress Emotion representation.
4. Brain UTR – Recognition of an Association via its Anschor State will regenerate its Stress Emotion representation. Threading through Associations in the Association Database as part of dreaming or thinking will also regenerate the Stress Emotion (negative or positive) of every Association accessed – discussed in Step 13 below.

To arrive at a consolidated autonomic Stress Emotion representation, the highest source of negative autonomic Stress Emotion (Deprivation) from the above list will be used as the prevailing negative Stress Emotion (between 0 and 1). However, if this Deprivation level is decreasing – meaning that the system is experiencing autonomic Stress (Satiation), the highest source of positive autonomic Stress Emotion (Satiation) from the above list will be used.

This will provide a consolidated autonomic Stress Emotion (either in Deprivation or Satiation) that can now be compared with all the Deprivation and Satiation levels of all other Emotions (generated from Body UTR Drives) that is part of the system during the current cycle.

9. SATIATION – A Satiation Event will be registered if the agent was in Deprivation during the previous logic loop cycle of the program and has moved to Satiation in the current cycle. This is the moment the model will implement its operant learning protocol – whereby the autonomic Stress Emotion representation (positive becasue of the Satiation) will also be assigned to the Association that was newly stored or updated during the previous cycle. The effect of this is that recognition of the Anchor State of the previous cycle Association is now turned into a Satiation Event – not becasue it provided homeostatic Satiation (e.g. food, warmth, etc.) but because it will now cause a lowering of the autonomic Stress Drive and Emotions caused by the Hunger Drive. For instance, recognising the green door leading to the kitchen will trigger a lowering of the autonomic Stress Drive and Emotions (stress relief) and lead to another Satiation Event. And again, this Satiation Event will, bu virtue of the operant learning process, turn the preceding Association into a navigational reward source. This process is called Reward-based Backpropagation and is how an Xzistor agent learns, through operant learning, to navigate to reward sources from anywhere in its environment. Under dynamic conditions the facial expressions of these agents will show and increasing Deprivation (clearly desperate frowns) as they try to find a reward source, while recognition of these en route navigation cues acting as Satiation sources, will trigger lowered autonomic Stress Emotions (momentary relieved smiles) which makes for a very realistic human-like behaviour.

If already in Satiation (e.g. eating food, or charging its battery, etc.) the agent’s actions will not be interrupted, unless a stronger (more urgent) Body or Brain UTR is registered (e.g. higher value between 0 and 1). This will force the agent to abandon the learned Satiation activity (i.e. Motions) and act on the new higher priority Body or Brain UTR. The program therefore keeps previous cycle information in cache, to be able to see if there had been a move from Deprivation to Satiation in the current cycle.

10. DEPRIVATION – If the agent is not in Satiation, it either means it is in Deprivation (e.g. suffering Hunger, Thirst, Pain, Fear (e.g. the negative autonomic Stress Emotion triggered when observing a known Pain source) or no UTR is currently high enough (over the critical activation level) to warrant action. If no Body or Brain UTRs require attention, the agent’s behavior will still revolve around finding Satiation and avoiding Deprivation (this will be explained later).

11. PRIME UTR – The program will compare all the Body and Brain Drive Emotions and will confirm if the current Prime UTR is still the highest Drive in Deprivation meaning the agent should keep on executing the related behaviours (Motions) to minimise Deprivation, or if the current Prime UTR is still providing the highest Satiation so that the agent should keep on executing the related Satiation seeking behaviours (Motions). Else a new Prime UTR (Body or Brain) will be selected as the Prime UTR, which will start driving the agent’s behavior.

The adjudication is peformed as follows:

1. If the Prime Drive (Body or Brain UTR) is in Satiation (i.e. homeostasis/allostasis is being restored), keep on performing the learned Motions to restore (lower) the Prime Drive until it falls below its activation level (beneath which the system will be aware of it but not act on it).
2. If another Drive (Body or Brain UTR) is now offering stronger Satiation, make this Drive the new Prime Drive and change to perform the learned Motions to restore (lower) this new Prime Drive until it falls below its activation level (beneath which the system will be aware of it but not act on it).
3. If the Prime Drive (Body or Brain UTR) is in Deprivation (homeostasis/allostasis deficit is increasing), keep on performing the learned Motions to restore (lower) the Prime Drive that will achieve Satiation.
4. If another Drive (Body or Brain UTR) is now recording higher Deprivation, make this the Prime Drive and change to perform the learned Motions to restore (lower) the Prime Drive that will achieve Satiation.

This will confirm the Prime Drive for the current cycle.

12. THREADING – If all the agent’s Body and Brain UTR Drives are below their selective activation levels there will be no Prime UTR and the agent will perform Threading since there are no urgent imbalances to address i.e. no problems to be solved. A typcial activation level could be 0.1 on a range 0 to 1 (i.e. 10%). In this state the agent will Daydream or learn to obtain Satiation from other sources e.g. playing games. Activities like Playing might start off as instinctive infant exploration behaviours or can be learnt behaviours offering Satiation by artificially creating Deprivation (often mild autonomic Stress Emotion generated during physical games or computer games) that offer moments of intense Satiation (relief). Adults might look at more sophisticated and subtle forms of achieving Satiation e.g. studying new subjects, watching sports or having conversations involving friendship (bonding) and/or humor. Daydreaming will be achieved through a process called Threading whereby the system will recall Associations from the Association database akin to the human brain’s process of ‘mind wandering’. The criteria for selecting the next Association whilst performing Threading will be similarity in optic Sensory state (mainly) and the value of the Association’s Impact Factor (IF). Based on similarity with the current re-evoked optic Sensory state, e.g. recalling a tutor’s face that provided a high-Satiation food source, a list of Associations will be selected starting with those with the highest IF – this will mean that these Associations had made a strong emotional impact (good or bad), were often repeated or are very recent. Whilst Daydreaming can still be affected by what is oberved in the environment, Sleep Dreaming follows the same process except that effector Motions are disabled, unless strong Sensory input is experienced (tactile, sound, etc.) which will terminate the Sleep Dreaming process (wake the agent up).

13. THINKING – If the agent has performed the Motions to resolve the Deprivation of a Prime UTR many times before, it will quickly recognize the correct environmental cues (Anchor State representations in the Association) as well as the actions (Motions) from the Association Database e.g. navigating from the kitchen to the battery charging station in the lounge could be a quick, smooth Motion (motor inputs updated every 0.1 seconds). When originally starting out though, the robot would have bumped into walls and often cried for help from the tutor (crying is a Learn-modifiable Reflex that can be triggered by a high level of Body or Brain UTR Deprivation – typically when Deprivation reach 0.3 on a scale from 0 – 1 i.e. 30%). Later, all the correct learning wil become almsot instantaneously available as a quick succession of retrieved Motions from recognised Associations. If the agent does not recognize ts current environment as an area where it had learnt to Satiate Body or Brain UTRs, no learned Motions will exist in the Association Database – and the agent will have to Think (this is triggered by a period of increasing Deprivation and no recognition of Associations with known Motions to perform – say 3 seconds). This the model refers to as ‘directed’ Threading where the agent now searches for the ‘closest’ Association to fit with the UTR and environment (i.e. the closest Anchor State match), and just ‘try’ the Association’s stored Motions to see if it works (some applications will use a Tolerance Factor to indicate which Associations had often been prone to predictions errors i.e. mismatches). As the agent’s Deprivation level increases (for example due to increasing Hunger), the coupled negative autonomic Stress Emotion will increase and the agent will become more rushed to find an Association. Associations chosen from the Association Database might become more random and less accurately filtered – leading to increasingly desperate behaviours to find a food source. The Threading (mind wandering) process is now narrowly ‘directed’ by constantly returing (restarting) the search for a match using the optical images (Sensory representations) of learned food sources (only) as part of a specific Anchor State and the search becomes focussed on Associations providing Motions to these food sources within that environment. Narrowly ‘directing’ the Threading process in a manner to find behaviours (Motions) that can solve problems is called Thinking by the model. During Thinking the model will generate the ‘context’ around what is being thought about by recalling relevant Associaitons. The quickly recalled Associations based on images of the food sources (which could include recollections of helpful navigation cues in the environment) will form the ‘context’ around the situation.

14. ACTION COMMANDS – The program will use Steps 4 to 13 above to arrive at the most appropriate Motions commands for the current cycle, including where necessary through the process of Thinking aided by context generation.

These Motions will provide the best estimate from past learning as to what the agent should do, in a specific environment, to reduce Deprivation or maintain and optimise Satiation.

The Satiation Motion commands for the Hunger UTR could be to remain in one position and ingest the food (food intake is normally simulated). Identifying the correct Motion commands (representations) mean the program will also consider if any Reflexes were triggered and factor in where tutor instructions should override own decisions.

15. MOTIONS – Here the final Motion commands identified in Step 14 are executed by means of the Motion effectors e.g. motors, actuators, speakers, lights, etc. The Motions of virtual agents will be simulated.

After this step, the program will return to Step 4 above.

KEY EFFECTS GENERATED BY THE FUNCTIONAL BRAIN MODEL UNDER DYNAMIC CONDITIONS

The information passed between the 5 functional algorithms for every logic loop cycle is crucial to how the Xzistor model works, but equally important is the information passed from one cycle to the next cycle.

Reinforcement Learning

The Association stored / updated during the previous logic loop cycle must be available in the current cycle to determine if the Prime Drive (or any other Body or Brain UTR Drive) has changed from being in Deprivation to Satiation. This will indicate that a Satiation (SAT) Event had taken place.

When a SAT Event occurs, meaning an action by the agent is bringing a reduction in the Deprivation the agent is suffering, it is important that the brain model stores to memory the effector motions that were performed on the moment the SAT Event happened. It is equally important that the brain model stores to memory the successful effector motions leading up to the SAT Event. When a Prime Drive was Satiated, the Association preceding the SAT Event (i.e. ocurring during the previous cycle of the logic loop) will therefore retrospectively be updated and credited (‘reinforced’) as an Association that, for that Prime Drive and Sensory representations (environmental cues), offered/informed the correct effector motions that led to the SAT Event. The system will remember that when next it is in that same physical location and experiencing strong Deprivation from the same Prime Drive, it should use that specific Anchor State – i.e. environmental cues (Si) and Prime Drive representation (PD) – to ‘recognise’ this ‘reinforced’ Association and extract the correct effector motions from it (as a best-estimate) towards achieving Satiation. As the physical environment might have changed slightly, it could at times become a ‘trial and error’ effort by the model. If the attempt is successful, the effector motions will again be ‘reinforced’, also for the preceding Association, for future use. The model will, when the Prime Drive (let’s say A) and the Sensory representations (let’s say B), search for an Anchor State match in the Association Database containing A and B – and if it had provided Satiation before – execute its effector motions fo the matched Association. When an accurate Anchor State match is not available, the model will seek the closest Anchor State match Association and ‘try’ the effector motions to see if it works (in some applications a Tolerance Factor is used to inform the level of accuray required for a match). If no Anchor State match can be made, the model will resort to Threading to find an Association with potentially helpful effector motions by exploring the ‘context’ around the current Anchor State (discussed below).

Supervised Learning

When Motions (M) are imposed on the agent by an external ‘supervisor’, and these Motions become part of the Motions leading up to a SAT Event, they will also be reinforced and in future re-evoked when the same SAT source is pursued due to the same Drive. In simple robotic models tachometers are used to record the effector motions of the regulated electrical motors resulting from tutor interventions. These effcetor motions are then stored as part of Associations and can be re-generated when the Association is recalled. In more sophisticated models limb/joint forces and accelrations can also be measured and used as an additional proprioception sense (part of the Anchor State) to aid complex motions and coordinated effector routines.

Reward-based Backpropagation

As mentioned in Section 5.6, the specific way in which the Xzistor brain model achieves Reinforcement Learning leads to another important effect called Reward-based Backpropagtion. Reward-based Backpropogation is based on the manner in which certain states in the human brain linger in the brain while new ones are being introduced, allowoing cross modulation (as evidenced in the academic literature).

During Reinforcement Learning, the Association from the previous cycle is rewarded by changing this Association’s autonomic Stress Emotion representations from Deprivation to Satiation (proportionally to the level of Satiation generated in the current cycle from the Satiation Event). Based on the current cycle Prime Drive Satiation Emotion representation (SE), this Prime Drive Emotion must also be turned into a Satiation Emotion representation for the Association formed/updatd during the previous cycle. This will tag all the Drive and Sensory representations of the previous cycle Anchor State with Satiation Emotions that will enable these Anchor States, when ‘recognised’, to generate a new autonomic Stress Drive Satiation Event (and therefor a learning or reinforcement opportunity). This will progressively lead to Anchor States (with their effector motions) positioned further and further away from the Prime Drive Satiation Source location to be become recognisable as ‘approach’ states – leading to a physical navigation path being created towards the Prime Drive Satiation Source. Simple Xzistor robots have successfully demonstrated how they will learn to navigate from any point in their learning confines to a Satiation Source (e.g. food) using effectivley autonomic Stress Emotions tagged to environmental cues that encourages ‘approach’ behaviours. For for sophistacated future Xzistor models these navigation paths can include for instance solving a complex Hunger resolving navigational route – learning to drive a car, fetching the keys of the car, putting gasoline in the car, driving to the supermarket, buying food, driving home, cooking food, etc. In some Xzistor robotic applications a cache of the Associations formed/updated during previous cycles is maintained to add proportional rewards further back in time. This significanly speeds up operant learning.

Subjective Emotions

As mentioned before Emotion representations will be cast into pseudo-somatosensory representations. This means these representations will be somatotopically placed within the body – so that the agent will sense an Emotion state as ‘inside the boundaries of its body’ after the process of learning the boudaries of its body. This happens through interaction with the environment and tactile and pain sensations leading to the ability to ‘locate’ sensory representations within the somatotopic map of the body in the brain (i.e. computational correlate of the cortical homunculus). Emotions (DEs and SEs) are always consciously experienced (felt) by the brain model – as if originating from within the body – because they are constantly presented to the executive part of the model, along with their learned effector compulsions, to determine the next behaviour.

Although the Stress and Nausea Drives can act by being coupled to a Body UTR Drive (e.g. Pain), it can also be re-evoked from autonomic Stress Emotion representations residing in the Association Database (hence these are called Brain UTR Drives). When these autonomic responses occur as part of Body UTR or Brain UTR Drives, they will also lead to pseudo-somatosensory representations that will somatotopically be placed within the body (e.g. humans might experience stress as ‘butterflies’ in the stomach or nausea as an ‘urge to vomit from te stomach’.) When there are no strong Body UTRs driving the behaviour of the model, these autonomic Drives will, even when extremely subtle, drive behaviour. As the agent brain learns, new more complex and nuances concepts will need to be aquired by the agent to ensure access to Satiation sources. These might attract (e.g. through Reward-based Backpropagation) nuanced and complex Emotion sets (combinations) that will determine the meaning/value these concepts have in the mind of the agent, and lead to ‘common sense’.

Threading (Mind Wandering)

The Xzistor brain model uses a Threading mechanism akin to ‘mind wandering’ in the human brain to achieve some important effects. It is helpful to think about Threading as a mechanism whereby the brain model constantly wants to re-evoke Associations from the Association Database. This ‘compulsion’ of the model is resisted when urgent Drives (Body and Brain UTRs) need to be solved as a priority. When no urgent action is required to solve Drives (subjectively felt as Emotions), goal-based effector Motions will stop and Threading will start.

The way Threading works is that it starts with the current Anchor State and then searches the Association Database for Associations with closely correlating Anchor States – firstly re-evoking the ones with the highest correlation and Impact Factor, before searching deeper into the Association Database. The key attribute the next re-evoked Association shares with the input state Association could be its Anchor State or any representation (e.g. visual objects like faces, places, landscapes, artifacts, dagrams, images, words, distinct smells, specific sounds like melodies, or even a Drive, Reflex or Motions representation). Typically, the visual Sensory representation of each Anchor State belonging to the Associaiton will be re-evoked and again ‘seen’ by the agent – in a manner comparable with Daydreaming in the human brain. These recalled flashed images can be displayed on a screen for the tutor to see when using digital Xzistor robots.

As with the human brain, the brain model will learn to use this random searching mechanism of the Association Database to solve problems. When an answer cannot immediately be found (e.g. the way to navigate to a food source) the agent will pause and allow this involuntary Threading process to start re-evoking Associations, except now it will ‘direct’ the process by constantly returnign to the Anchor State that represents the Satiation Source (e.g. Sensory representation of hamburger). This will prevent the Threading process to continue unhindered and ‘narrowly direct’ it to only re-evoke Associations directly related/correlated to the problem (the Anchor State). As the Prime Drive starts to repeatedly flash this Anchor State before the executive part of the brain model (ensuring that ‘directed’ Threading are not allowed to go into ‘undirected’ Threading i.e. mere Daydreaming), it will reset the Threading process back to search for a match with this Anchor State and avoid it from wondering off topic. The hope is that searching the ‘context’ around a Satiation Source in this way could lead to an Association in the Associaton Database with helpful effector Motions that can aid in solving the problem.

Xzistor robots can be made to sleep and experience Sleep Dreaming which is just Threading with effector Motions disabled – unless Sensory inputs above a certain threshold is experienced (e.g. loud noise, nudges, etc.) Again, these recalled flashed images during Sleep Dreaming can be displayed on a screen for the tutor to see when using digital Xzistor robots.

Fears

We have seen how Body and Brain UTR Drives can generate Deprivation singly or collectively. Associations that were stored with high DEP values, will also generate DEP (albeit reduced) when they are re-evoked, mainly because of the autonomic Stress Drive (and in some applications a modelled Nausea Drive). DEP generated in this way will be referred to ‘Fear of’ or just ‘Fears’. We refer to the subjective Emotions generated based on the conditions of these Brain UTR Drives (DEP or SAT) as allostatic Emotions, but they can also just be viewed as homeostatic Emotions where the recalled Emotion representation also acts as the Control Variable for the homeostatic control loop. So the autonomic Stress Emotions can be triggered while the agent is experiencing intense Pain (as these are coupled), or when it recounts the Association formed during the painful episode.

Some Fears (by definiton also subjective i.e. somatotopically placed within the brain model’s body map), when evoked, can be stronger than the strongest Emotions from the current Body UTR Drive, which means it, as a Brain UTR Drive will take priority over such a Body UTR Drive and its Emotions. One of the first steps the Xzistor brain model takes as it enters the logic loop cycle and re-evoke an Association, is to see if the subjective Fears are stronger than the body UTR Drives. If so, behaviour will be prioritized by Fears and not Body UTR Drives. The Fears thus act like Drives in that they generate DEP when they are re-evoked. For example, when an agent navigates past a cactus it had previously bumped into, recognising the Association of the cactus will evoke such DEP that the agent’s priority will temporarily change to moving away from the cactus, before continuing on its way. And as the agents move away from the cactus the reducing DEP will cause a Satiation Event, causing the agent to learn to navigate away from this ‘avoidance’ state i.e. the optic representation of the cactus.

Instincts & Phobias

Any behaviors that can be achieved by learning, can be pre-programmed into the model as ‘instinctive’ behaviours (merely a set of permanent read-only Associations or preprogrammed Associations that can be modfied through learning).  Animals are born with a great number of very complex pre-programmed or ‘instinctive’ behaviors. The human brain has less pre-programmed behaviors and requires much more learning. The animal approach can provide an animal with advanced skills within minutes from being born aimed at a very specific domain, but very little flexibility to survive in other domains. Humans on the other hand need a long time to learn about their environments, but can adapt in different environments by means of goal-based learning and transferring skills learned in one domain to other domains. The human brain normally comes with some Phobias which are just pre-programmed Fears (i.e. autonomic Stress Emotions representing Deprivation). These are simply Associations that are already in the brain (Association Database) at birth, which will generate DEP (often by using the FoF Reflex), normally when a specific Sensory state is experienced (e.g. loud noise, sharp pain). Examples: fear of heights, the dark, confined spaces, specific animals e.g. spiders, snakes, etc.

Base Fears

The human brain quickly learns to fear the unknown. This happens because we learn that it is often in unknown environments that we encounter new threats i.e. sources of DEP. We also learn to be apprehensive about people because although people can be a source of SAT, they can also be a major source of DEP. Later we learn about our own mortality and understand there are permanent risks to keeping all our physiological and mental needs satisfied (the actual reason why we fear death). These risks could come from natural disasters, personal injuries, security of income, losing one’s shelter (house), health, the suffering of friends and family, public embarrassment, spousal rejection, violent crime and victimization in the workplace and a myriad of other modes of misfortune. Even just balancing our bodies involves the fear of falling down. These current fears and emergent anticipatory anxiety (fear) over the future, becomes a permanent DEP overhead we carry and constantly generates a ‘mood level’ mainly as a rsult of the FoF Reflex being triggered. We learn behaviours to minimise these Base Fears by e.g. focusing the mind on other issues (like playing games/sports), drinking alcohol, listening to music and escapism like reading books or watching movies. It is very difficult to completely reduce the DEP associated with these fears, and when we get close to a state where we are able to Saitiate all phyiscla and mental needs – we call it Euphoria. This can typically be achieved during sexual orgasm or when taking drugs like heroine, morphine and fyntanel. This extreme SAT is achieved when the total Drive D_tot is forced to drop low into the Base Fear regime – temporarily unburdening the brain of these Base Fears. 

Body State Override Reflex

The Body State Override Reflex is a mechanism extensivley elaborated on in the early Xzistor brain model provisional patent specifications (2002, 2003). The fact that all the motivations and behaviours originate from homestatic and allostatic control mechanisms create an opportbity for the brain to ‘interfere’ with the manner these mechanisms process input and output signals. Instead of these mechanisms acting on Variable and Control Variable readings, the brain can override these readings to artificially manipulate the Drives that lead to Emotions. For instance without Thirst in the brain being present, the brain can artificially create a sense of Thirst when food is being ingested. This is indeed what happens in the biological brain (this is to encourage that enough fluid is present as food is being ingested). But this principle can be extended to intervention in all the circuits that create Deprivation or Satiation Emotions from all Drives – leading to sets of artifically induced Emotions that can serve many purposes. For instance, the brain can enhance the Satiation Emotion experienced when eating by artifically triggering Satiation across many other Emotions – other than Hunger Satiation – creating moments of intense Satiation that will be ‘subjectively’ experienced as highly pleasuerable. This could be compared to the limbic system in the mammalian brain, known for its ability to create strong feelings of pleasure (Satiation). This has only been tested in early Xzistor simualtions, but will form part of a complete brain model in futue Xzistor humanoid robots.

Recognition & ‘Gut Feel’

When we enter a new situation, we will have new Sensory inputs flooding into the brain (e.g. optical states, audio, states, tactile states, olfactory states, etc). Each input state could have its own historic set of Associations it is part of i.e. the input state forms part of numerous Anchor States and their related Associations. The autonomic SAT and DEP values of all these Associations (for each input state) will be averaged, and again averaged across all input states to re-evoke an overall autonomic stress emotion (resultant autonomic SAT and DEP values). This will be the immediate resultant emotion we experience when faced with a new situation, even before we had time to understand the context around it.

If the situation has never before been experienced (no corresponding Anchor States), the same thing will happen but the brain will use the ‘closest correlating’ Associations it can find instead of Associations with precise Anchor State matches.

This enables the brain to quickly judge if a new situation is essentially ‘good’ (approach) or ‘bad’ (avoid).

The brain will immediately continue to re-evoke more Associations based on the current situation proposing to the brain what effector motions to perform (even if that is to perform no motions). If this initial context, along with the resultant emotion, is generated from matching Associations (experienced before), we will refer to this as Recognition. If it is generated from correlating Associations (never experienced before), we will refer to as a ‘Gut feel’.

In terms of the brain model, we define the initial context and the resultant emotion as giving ‘meaning’ to a situation.

Day Dreaming

When the brain has no Body or Brain UTR Drive Emotions to urgently attend to, the Threading mechanism will uninterruptedly re-evoke Associations. As the Sensory input states from the environment cause changes to the Anchor States, they will trigger new Association searches which will lead to new ‘Threads’. Autonomic SAT and DEP will be re-evoked along with each Association and Reflexes (e.g. slight FoF Reflex activity). No ‘learned’ Motions will be executed during this phase because no Drive will require to be Satiated and the Drive ID will thus not for part of the Anchor State. This phenomenon will be referred to as Day Dreaming. Day Dreaming (by virtue of Threading) will occasionally Thread onto an Association which reminds the brain of something it needs to do e.g. buy some ingredient to cook a dish. The brain could then re-evoke autinimic stress as to the fear of forgetting to buy the ingredient and this might rise to a levwl where in interrupts the Day Dreaming to undertake the shopping task (to Saitiate the autonomic stress). If the brain is too focused on urgent activities to Day Dream, it can easily forget things. It is when the brain is free of obliations to undertake urgent tasks, that this Threading reminds it of what other things it was supposed to do but might be forgetting at the moment.

Thinking

We learn that Threading sometimes can provide answers, or clues as to what action the brain should take to solve problems. This happens by re-evoking Associations of which the Motions (i.e. stored effector actions) can remedy a new  problem. We also learn that Threading can be ‘directed’ i.e. influenced to be more effective as a problem solving tool by performing certain actions while it is taking place. We learn that the probability of finding a helpful answer from Threading is enhanced if we:

1. Look at the objects involved in the problem.
2. Not think about other things.
3. Look at details of the objects.
4. Get different views of the objects.
5. Touch the objects.
6. Ignore sensory distractions from the environment.
7. Ask questions about the object.
8. Follow learned problem solving techniques.
9. Look in guides/manuals.
10. Ask clever people for solutions.
11. Look on the Internet for information.

The brain automatically increases the efficacy of the Threading process during problem solving. It determines how urgent solving the problem is based on the current Prime Drive level, because the level of Deprivation i.e. the ‘urgency to restore’ is contained in the Emotion representation. It ‘focuses’ the mind by forcing the Threading mechanism to Thread from its current inputs states (Anchor States), using these as filter criteria, and not Thread for so long as to completely digress from the current filter criteria. It thereby allows only Associations close to the current problem (input states) to be considered. The more urgent the problem-solving effort becomes (and the higher the Prime Drive Deprivation), the shorter the periods the Threading mechanism will be allowed to search the Association Database before being returned to the current input states. This will cause the brain to narrow its search i.e. improve its ‘focus’ or ‘concentration’. The brain function of steering the Threading mechanism so as to find appropriate solutions to problems, we will refer to as Thinking.

Individuals with a natural tendency to digress faster from the search topic we can call lateral thinkers. Individuals with a natural tendency to stay close to the search topic we can call logical thinkers. The lateral thinker’s solution might be more creative, but more unproven, whilst the logical thinker’s solution might be less novel, but more conservative.

The model uses the simple relation:

Focus  DEP

And can define Focus to have a value between 0 (no Focus) and 1 (100% Focus), where:

for DEP = 0, Focus = 0, and

for DEP = 1, Focus = 1

Sleep Dreaming

When we sleep, sleep fatigue Reflexes will shut down limb movement and close the eyes. Because Threading is still constantly trying to re-evoke Associations, this will continue during sleep. Thinking can also occur during sleep if a problem occurs as part of a re-evoked Association or train of Associations (episode). Due to the basic Threading rules, we find that dreams were often related to ‘vivid’ Associations i.e. high DEP or SAT, recent and/or often repeated events (meaning Associaiotns with a high Impact Factor). Strong inherent Fears (DEP) will encourage episodes to be constructed from parts of Associations that will represent these Fears. This will lead to visual images being re-evoked that is often referred to as metaphors of these contextualised Fears. Transient observations forming part of one Association can influence parts of another Association whilst dreaming, contorting and modifying it, creating new states never actually experienced before e.g if we saw a lion charching in the zoo, we may dream of a horse charching us. Here the opticall triansition of the animals rapidly growing in size as it appraoched becomes an attribute that is applied to an Anchor Stare object from another Assocition.

Playing & Humor

When animals are born, it is with a resident set of generic instincts (i.e. pre-programmed read-only Associations). Playing is a Reflex to encourage animals to enhance these instincts with environment-specific or ‘detail’ Associations. Humans play for another reason. When the brain is not preoccupied with strong Drives or Fears, basically all that remain are some low Drives below the Detection Threshold and the Base Fears. The human is still obsessed with finding SAT, even under these conditions. The brain learns that certain events can make the low Drives and Base Fears decrease to cause SAT. More importantly, the brain learns that some activities will bring a temporary increase in Base Fears (normally by means of the FoF Reflex) and then lead to an immediate release i.e. SAT.

By doing things that slightly increases DEP, SAT can be achieved. This is why human games always involve some tension build-up, followed by sudden SAT. Humor also subtly lifts the Base Fears by creating some mental dissonance, or expectation (e.g. related to the fear of the unknown), then uses a punch line to release the tension and produce SAT.  Sport is also just a search for SAT, using the above mechanism, but enhanced with DEP from the Aggression Drive, where SAT is associated with violent physical action aimed at beating an adversary. Over the years sport has become less brutal, but we still talk about ‘beating’ the enemy.

Perhaps the most subtle of these ‘entertainment type’ activities are the things the brain does for intellectual stimulation or out of curiosity, where the slight fear of the unknown encourages the brain to seek for the truth. These ‘answers’ or insights become ‘minor victories’ over the fear of the unknown and provide satisfaction or SAT. Even just watching TV forces the brain to escape reality (Base Fears) and also makes the brain witness drama, excitement (e.g. sport, competitions, adventure), intrigue and insight, al creating SAT. The essence of entertainment is thus repeatedly creating mild DEP, followed by SAT.

Balancing & Walking

Balancing and walking is achieved in an interesting manner by the model. First the agent learns not to fall down because this is painful. It learns to avoid the pain associatd with falling through Rewatd-based Backpropagation – meaning it slowly learns that keeping balance and not falling down will Satiate the fear of pain from falling down). It also learns to stand up straight because other poses are too strenuous causing fatigue (i.e. muscle pain).

Lastly it learns to walk as a means to locate SAT sources in its environment by means of ‘reinforcement learning’.

Coordination & Optimisation

To have the agent optimize its own efforts, a Fatigue Drive can be introduced. This Drive will create DEP as a function of personal effort e.g. power (which is energy/time).

It can co-generate Pain. So the agent will ‘suffer’ DEP from exerting power and even feel Pain (analogous to muscle pains).

To find SAT associated with this Drive, the brain will learn behaviours to optimize its efforts in terms of DEP i.e. take short cuts, avoid steep inclines, even avoid navigational routes past object that cause Fear and choose navigation routes past object creating SAT e.g. music or landscape where optic images are associated with SAT.

Language Aquisition

The model allows for an agent to develop language (verbal) skills in exactly the same fashion it learns motion (non-verbal) skills. As such the model views language just as muscular motion sequences learnt to elicit SAT from the environment. The agent will optimize the syntax of a specific lexicon during communication to minimize the time it waits for SAT. The agent might well learn that adopting the terminology of an existing language, is the easiest way to communicate with other agents which could act as SAT sources.

Note: Research paper currently in production at Xzistor LAB defining a project to demonstrate language aquisition in Xzistor agents.

Higher Tier Thinking

The model requires no predetermined higher tier, or subsumption layers (see Rodney Brookes), and ‘abstract’ or higher level concepts simply gets learnt when the paths to SAT gets more complicated and presents sub-goals (achieved through subtasks) which must be achieved before the actual SAT can be experienced. An agent can, for instance, be forced to learn that it must use a specific technique to solve a certain problem, and part of using that technique means learning some abstract concepts.

Comprehending these concepts just become sub-goals on the agents path to finding SAT. Of course it might ask many questions along the way and need pain staking training to get there, but it must be remembered that even if the X-zistor Concept was a 100% accurate brain model, it will still take the agent many years of training, including mastering a language, before it could use abstract concepts in its thinking. It thus requires time and reinforcement learning (B.F. Skinner), rather than predetermined ‘man-made’ structures or layers (Noam Chomsky).

MODEL DIAGRAM ON A SINGLE A4 PAGE

(Yes – a single page diagram is possible of the Xzistor logic loop. Blurred because in the process of improving!)