We describe an empirically supported protocol for the building and assesment of connectionist models describing decision processes under the influence of emotion.

ARU grew out of attempts to apply connectionist principles and cognitive behavior. It is a comprehensive principle-driven model designed to treat muliple problems, difficult to treat reason because of emotional factors. Empirical literature supports ARU as an effective means for the examination and revision of decisions under uncertainty.

It is a well accepted tennant of the class of therapies called cognitive-behavioral that there is causal interaction among thoughts, behaviors, and emotions. (but see Angelette 2003 and, 2005) If we sucessfully effect change in one we can expect change in another.

In following subsections we will look at some standard cognitive-behavioral approaches to decision under uncertanty and see how ARU can be utilized.

!ARU is expected to have impact by directly addresses inconsistency of goals, priorities, and values. It may also serve to mitigate uncontroled emotions and impulsive behaviors. It should be clear, however, that !ARU is merely an aid and not a panacea. It's efficacy will be limited not only by the limitations of the software but also by the skill of the user.


Connectionism is not at odds with other rational decision traditions. It is possible to reinterpret the tradition in connectionist terms using the neurodevelopmental model and proptype theory. On a connectionist verion of prototype theory, there is much plasticity in the neuronal organization of the infant brain. Early life experiences are encoded in various areas of the brain to produce vectorial representations of basic categories. Relationships between prototypes from different categories may develop into more or less coherent organizational structures. These in turn influence the processing of new information through a process of cyclical brain growth.

In addition to atypical, invalidating, life experiences contributing to incoherent organizational structures it is hypothesized that individuals with a predisposition to frequent and intense emotion. This leads to emotion vulnerability. High sensitivity together with slower return to affective baseline increases the likelyhood of immediate reactions to stimulus which translates to more incoherent cognitive processes.

An invalidating environment punishes or ignores behavior or the internal experience of the person independent of actual validity or importance. Typical invalidating environments include trauma, emotional abuse and neglect, sexual abuse, and lack of emotion regulation skills in parents. In consequence the environment does not teach the individual to label private experiences consistent with social norms, effectively regulate emotions, or trust experience. Instead the individual learned self-invalidation and searches the social environment for cues about how to respond. !ARU exploits this disposition by placing an anamated and interactive representation of the clients own internal organization before them.


Intervention using!ARU is adjunctive to a multi-disiplinary approach to rationalitythat may include other modality such as standard logical reasoning. It is important that all participants and stakeholders have an oportunity to communicate and agree on expectancies and limitation of this exercise. There are risks associated with any self examination and we support conversation to develop plans addressing new insights.

!ARU appeals to our disposition to search the environment for clues to self identity by providing an environment that mirrors the unique internal coherence state of the individual. This strategy is implemented interactively with the user providing information about their conflicting, beliefs, goals, desires.


You cannot be expected to respond well to the method without first achieving a level of trust and confidence in both the method and the tools. To that end we will do a stepwise examination of the scientific principles that underlie the system and how they are implemented in ARU.

Developing a Working Relationship

!ARU is a complex program and may easily overwhelm or confuse the novice user. It is important to present aspects of !ARU in a stepwise and engaging manner. Do not move to a more complex use of !ARU before the user is completely comfortable with prior uses.

The user must understand and trust the outcomes of their interactions. To this end, it may be useful to aquaint the user with other cognitive decision making uses to which !ARU has been put. A library of models and results may be found here Others may respond better to simple models created on the fly in session.

Selecting Initial Interventions

It is crucial that the initial intervention be easy to understand and meets with success. The first measure of success will be the degree to which the user agrees that the initial !ARU model reflects her viewpoint. The second measure of success will be the ease withwhich the user can recognize the factors of simplisity, breath and emotion that weigh in the processing of their model.

Some hoped for outcomes of learning the use of ARU are: Decreasing Dichotomous Thinking

Increasing Control over Emotions

Improving Impulse Control

Strengthening Sense of Identity

Addressing Assumptions




= = = = = = = = = = ARU - AFFECT REASON UTILITY = = = = = = = = = = =

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____________________WILLOW ANGELETTE_________________


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This model provides the user with the ability to rationally assess competing ideas on the basis of coherence using a connectionist formula to update the activation of interconnected nodes. The network is also a (PDP) Parallel Distributive Processing Model in the sense that each node acts independently and simultaneously* to update its activation with information about only those nodes with which it is immediately connected.

As activation of a node increases, the node size increases. As activation decreases, the node size decreases. Below activation 0.0 the node changes from a circle to a triangle representing rejection. Some models may have nodes that include a valence in addition to an activation. Changes in valence are indicated by color changes of the nodes.

With the exception of orange special nodes set at 1, the default activation of a node is 0.01. Changes of activation and valences is achieved via links between the nodes. Positive links, colored green, are created with a default weight of 0.04 while negative links, colored red, have a default weight of -0.06. The activation and valence of unconnected nodes will decay at a rate of 0.05 per cycle.

* the model mimics simultaneous updating by first updating each node in a network and then beginning it's next cycle.

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======================== AFFECT =======================

Affective elements can be modeled by !ARU and are represented by nodes that connect with Special Star shaped nodes. In addition to activation these star shaped nodes deliver a valence set at a default of 0.05 for positive valences and - 0.05 for negative valences.

Principles to consider in the construction of coherence networks to model the influence of Affect are:

Elements have positive or negative valences.

Elements can have a positive or negative emotional connections to other elements.

The valence of an element is determined by the valences and acceptability of all the elements to which it is connected," according to Thagard (2000 p. 173).

This particular example models a coherence network with the influence of affect shown as a star.

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======================== REASON =======================

The theory of explanatory coherence can be summarised in the following principles (Thagard 1992. Conceptual revolutions. Princeton, NJ: Princeton University Press, 2000. Coherence in thought and action. Cambridge, MA: MIT Press.)

Principle E1. Symmetry. Explanatory coherence is a symeteric relation, unlike, say, conditional probability. That is, two propositions p and q cohere with each other equally.

Principle E2. Explanation. (a) A hypothesis coheres with what it explains, which can either be evidence or another hypothesis;

(b)" hypothesis that together explain some other propositon cohere with each other' and (c) the more hypothesies it takes to explain something, the lower the degree of coherence.

Principle E2. Analogy. Similar hypotheses that explain similar pieces of evidence cohere.

Principle E4. Data priority. Propositions that describe the results of observations have a degree of acceptability on their own.

Principle E5. Contradiction. Contradictory propositions are incoherent with each other.

Principle E6. Competition. If P and Q both explain a propostion, and if P and Q are not explanatorily connected, the P and Q are incoherent with eachother. (P and Q are explanatorily connected if one explains the other or if thgether they explain something.)

Principle E7. Acceptance. The acceptability of a proposition in a system of propositions depends on its coherence with them.

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======================== UTILITY =======================

Thagard (2000) offers the following principles to be considered in the design of PDP models of utility and which principles may be applied to !ARU:

"Principle L1: Symmetry Coherence and incoherence are semmetrical relations: if factor (action or goal) F1, coheres with factor F2, then F2 coheres with F1.

Principle L2: Facilitation Consider actions Ai, . . . , An that together facilitate the accomplishment of goal G. Then

(a) each Ai coheres with G,

(b) each Ai coheres with each other Ai, and

(c) the greater the number of actions required, the less the coherence among the actions and goals.[we may imagine this to be a way to interpert work ]

Principle L3: Incompatibility

(a) If two factors cannot both be performed or achieved, then they are strongly incoherent.

(b) If two factors are difficult to perform or achieve together, then they are weakly incoherent.

Principle L4: Goal priority Some goals are desirable for intrinsic or other noncoherence reasons.

Principle L5: Judgment Facilitation and competition relations can depend on coherence with judgments about the acceptability of factual beliefs.

Principle L6: Decision Decisions are made on the basis of an assessment of the overall coherence of a set of actions and goals (p. 128)."

Actions like hypotheses are evaluated with respect to their coherence with each other and with goals. In addition goals get a degreee of priority since they will connect directly with specials or else Vspecials via a valence node.

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= = = = = = = = = = = = = USING THE INTERFACE = = = = = = == = = = = = =

Buttons and sliders along the top of the interface provide the user with the ability to load and run the default model and to modify its operation.

Buttons and sliders along the left allow the user to add and remove components of the model.

Networks are displayed in the large center square.

Information about the status of the network is displayed on demand in the white rectangle below screen.

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================TO RUN THE DEFAULT MODEL=================


Click the button labeled Make Blank
click the button near the top left labeled Observationsa window will pop up. enter in brief discription of what you've observed.
A node will be created. click Observations again to enter another observation. Continue in this manner until you've input all the observations you think relevant to the current problem.
Next, click the button labeled Beliefs. A window will pop up. Enter a discription of the belief you have as a consequence of some observations you've already input. When you click OK another box will pop up asking you if your belief is explained by something.
If your belief is supported by some observations you've already input,Click OK
If you find the belief is unsuported or is supported by something you have not already input, click the down arrow and select NO and then click OK.
Supposing you have indicated that your belief is explained by somthing, a new input box appears asking how many observations you consider to be responsible for your belief. Look over your observation nodes and note which ones account for this particular belief. Put the sum into the blank and click ok.
This will bring up a series of new popups, as many as will be needed. Look at the observatin nodes. Notice that each node has a number at its center. Type into the input popup the number of a node that accounts for your belief and click OK. This will bring up yet another input box asking you to name the theory to which the belief belongs. Enter the name of the theory and click OK (Use a short name such as T1 or T2 as any mistype will cause an error) Continue in this way until you've identified all the observations that account for this belief.

When windows stop topping up you know you're done with this belief and it's time to connect the dots. Look in the left column for the control button labeled click+linking-nodes. Now click on the belief node you've just created and then click on the observation you've related it to. A green link line should appear. Continue in this way to connect your belief with each observation withwhich it is related.

Now think of another belief that you have about what you've observed, click on the Beliefs button and continue as before.

You may find that you will need more than one Theory to account for your experiences and sometimes there will be competition between the beliefs. It is important to note these competitions by placing negative constraints between them. This is accomplished using the click--linking-nodes button

Now it's time to take note of how many theories it takes to account for your experience. Move the number of theories slider to the number of theories you've created. (note: number of theories and number of beliefs are not the same thing.)

Click on the "balance weights" then
click on "the button labeled "go".

As activation of a node increases, the node size increases. As activation decreases, the node size decreases. Below activation 0.0 the node changes from a circle to a triangle representing rejection. If "valence off" is not selected, color changes will represent the flow of valence through the network.

Click the button at the top right labeled display output to see display of the current status of each node.

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= = = = = = = = = TO RUN ADDITIONAL PRE-SCRIPTED NETWORKS = = = = = =

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= = = = = = = = = = = = TO MAKE CHANGES TO A NETWORK = = = = = = = = = = =


Click the control labeled "leison nodes"
Using your mouse, click on each node to delete. All links to the node will be deleted as well.
Click the "leison nodes" control again after having deleted all nodes you selected.


Several types of nodes may be created with default settings and user interpretations. NOTE: Any nodes that are added by the user can only operate correctly if the network is reset to it's initial conditions before making additions and "Accounts" of new relationships among nodes must be done only after all new nodes have been added. To run your newly revised network, click reset, then balance weights, then go.

Look in the left hand column of buttons.

Click the button labeled "AddObervation" to add nodes representing the interface between the world and how our senses interpret observation. A user input box appears allowing you to enter a brief descriptive label of the observation to be interpreted, e.g. "the wind turns me on". ARU will then create a new node of activation 0.01 with your discriptive label and identify the number of the node, the fact that it is to be regarded as evidence, and link it to a special node. At a later stage in developing your network you can use the mouse to link your new node to others.

Click the button labeled "AddHypothesis" to add nodes representing concepts that may account for some bits of evidence. A user input box appears allowing you to enter a brief descriptive label of your hypothesis, e.g., "Because the wind is high" Another box appears allowing you to assign your hypothesis to a collection of nodes representing hypotheses that give a collective name, e.g., "T1". ARU will then create a new node of activation 0.01 with your descriptive label, identify the number of the node, and tag it as a hypothesis. At a later stage in developing your network you can use the mouse to link your new node to nodes representing supporting evidence and other concepts withwhich it may cohere or in-cohere.


Once you have created and linked all addtional nodes you can click the ACCOUNTS button to begin updating any new explanitory relations among nodes. Next, click a node you've newly created that is explained by another node or combination of nodes. An input box will appear which asks "Is this being explained by anything" Click the drop down arrow if the node has no explaination and pick "no" other wise click "OK" and a new box asks "How many other nodes will this node be explained by?" Using the AFFECT REASON UTILITY principles as your guide, count how many other nodes this node is explained by and enter the numeral in the blank provided. A new input box will now appear that asks you to provide a node number of each explaining node. Each time you enter a node number of an explaining node and click ok another input box will appear asking for the next node number until you have entered all of the node numbers that explain the node you at first clicked.

when the explainations are exhausted for the current clicked node no more input boxes will appear and you can click on the next node in need of explaination. When you're done exploring explanations click the ACCOUNTSbutton again to turn it off.


To create a positively weighted link, Click the button labeled click+linking-nodes

point your cursor at the node to which you'd like to link and click. The reset-click should turn to true if you've been sucessful.

point your cursor at the node it will link with and click. A green link will appear between the two nodes and reset-click will read false.

You may then create more positive links in this manner.

When you're done, click the button labeled click+linking-nodes again.

To create a negatively weighted link, Click the button labeled click--linking-nodes

point your cursor at the node to which you'd like to link and click. The reset-click should turn to true if you've been sucessful.

point your cursor at the node it will link with and click. A red link will appear between the two nodes and reset-click will read false.

You may then create more negative links in this manner.

When you're done, click the button labeled click--linking-nodes again.


Click the control labeled "leison links"
Using your mouse point to the midpoint of the link to be deleted, click on each link to delete.
Click the "leison links" control again after having deleted all nodes you selected.

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Be Forgetful: This button changes the character of the special link to evidence from activation from it's default value to random weights. The effect is that evidence gets "learned" at random and under the influence of the rest of the network some evidence may be forgotten.

Randomize activation: Each node will be reset to a random activation between 1 and -1. This can demonstrate whether a network will robustly produce similar results over a broad range of initial conditions.

Activation 0.01 : Returns the activation of all nodes back to the default initial conditions.

reset-click: If you're mouse click refuses to work, clicking reset-click may help.

resetcycle: Resets the count of cycles to zero.

supress valence: This sets the value of the valence to zero at each cycle. The network should respond by ignoring valences.

Move Node: This button appears torward the top right of the interface below the click reset control. Once clicked, hold the mouse pointer over a node you'd like to move and drag the node to a new position. When you're finished rearanging nodes click the Move Node button again to turn it off.

do-animate: When switched to the on position, nodes that cohere will drift together, those that in-cohere will drift apart. This, however, will slow updating considerably and is not recommened for networks larger than 5 or 6 nodes.

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=======================SCRIPTING RULES====================

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