Interact is designed so that you don't have to worry about technical matters very much: you describe people and events in words, and the program makes all of its predictions in words, too. But everything that Interact predicts about people and social life is figured out at a completely different level by translating a situation into measurements of sentiments and feelings.
In fact, Interact understands very little about the words you use to describe a scene. The program simply uses the words to render the scene as a numerical problem - a representation that literally could be drawn on a graph. Then Interact calculates with equations that describe how attitudes and sentiments combine and change, and thereby Interact comes up with predictions. The predictions are numbers, but Interact translates numerical predictions back into words. (If you are interested, you can check out the technical description of the java program.)
For example, suppose you specify that Mary is a "woman." Interact knows very little about women - nothing about what is required to be a woman or what responsibilities women have. All that Interact knows is how people feel about women - that on the whole, women are quite good, somewhat powerful, and somewhat lively. This information is contained in an EPA profile which summarizes ratings from a number of people, a profile like you can produce yourself using the measurement program. The EPA profile for "woman" is what Interact uses to set up a situation with a woman in it, and that EPA profile is all that Interact uses to make predictions about womanly behavior.
Here are the EPA profiles for "woman" stored inside the computer.
male: 2.34 0.43 1.14; female: 1.74 0.67 0.85
The first set of numbers summarizes ratings from about 25 males, and these numbers mean that on the average the males rated a "woman" as between quite and extremely nice, between neutral and slightly powerful, and slightly lively. The second set of numbers represents the ratings from about 25 females: they rated "woman" almost the same as males did - a little less nice, a bit more powerful, and less lively, but not enough to make a difference. (The corresponding numbers for males and females would have to differ by about .8 or more to make a difference.)
Here are the EPA profiles for "friend."
male: 2.66 1.81 0.92; female: 3.48 1.21 0.25
In words, friends are extremely nice, slightly to quite powerful, and slightly lively. The ratings from males and females are similar, but females think a friend is even nicer than males do, and the female ratings also represent a friend as weaker and quieter than the male ratings do.
Suppose we were to set up a woman-friend interaction. If we specify that both parties are female, then Interact would represent the scene using female profiles: 1.74 0.67 0.85 for woman and 3.48 1.21 0.25 for friend. From those numbers Interact produces predictions about the standard behaviors of a woman to a friend
dazzle, entertain, rally, amuse, amaze, exalt, awe
and about a friend's behaviors to a woman
please, cheer, congratulate, aid, welcome, apologize to, assist.
Interact doesn't arrive at names for behaviors directly. It computes an EPA profile to represent the ideal behavior of a woman to a friend or a friend to a woman, then searches for behaviors with a profile like that. For example, the ideal profile for the behavior of a friend to a woman is 2.3 1.3 0.3 (and you'll learn how to find that out pretty soon). Evidently the behavior of pleasing must have an EPA profile close to 2.3 1.3 0.3 since "please" was the top ranking act of friend to woman.
Let's check that. The EPA profiles for the behavior "please" are as follows.
male: 1.70 1.06 0.25; female: 2.06 1.06 0.21
Sure enough, the female EPA profile for "please" is close to the computed EPA profile for a friend's ideal behavior toward a woman, though not exactly the same.
If you select the option where Interact reports EPA profiles then EPA profiles are reported every time a word in an Interact dictionary is selected and every time a prediction is displayed. For example, with the option on in the Janet-Mary interaction you will see that the EPA profile for woman is 1.73 0.68 0.85, and the profile for friend is 3.48 1.20 0.26. Also you will see that the ideal EPA profile for Janet's action after Mary comforted her is 2.08 1.51 1.61. These numbers are the inputs and outputs of the mathematical computations that give Interact its capacity for intuition. (The numbers may be slightly different depending on the update version of equations used and the dictionary from which the profiles are drawn).
Another option in Interact let's you look at the equations which are used in the computations. For example, these are the first few lines of the U.S.A. male actor-behavior-object (ABO) impression-formation equations.
Z000000000 -0.25 -0.09 0.07 -0.15 0.03 -0.02 -0.09 -0.38 -0.03
Z100000000 0.44 -0.02 0.05 0.11 0.03 -0.01 0.01 0.00 -0.01
Z010000000 0.00 0.59 -0.05 0.03 0.15 -0.07 0.00 -0.06 0.00
Impression formation equations are the heart of Interact. They describe how an event changes feelings about a person, and also how an event changes feelings about behaviors and settings. "ABO equations" deal with events specified in terms of actor, behavior, and object person.
Each column of decimal numbers in the table represents a different equation, and the numbers are the coefficients for different terms in the equation. For example, the first column of decimal numbers defines an equation for predicting how an actor will be evaluated after an event. The second column gives an equation for predicting how powerful an actor will be after an event. In the case of ABO equations, there are columns defining how to predict the EPA outcomes for actors, behaviors, object persons.
The column of zero-one numbers, preceded by "Z" identifies the terms that are in the equations, as follows.
if the first digit in a line has the value 1, then the term of the equation defined by that line involves the pre-event evaluation of the actor, Ae
" 2nd digit " pre-event potency of the actor, Ap
" 3rd digit " pre-event activity of the actor, Aa
" 4th digit " pre-event evaluation of the behavior, Be
" 5th digit " pre-event potency of the behavior, Bp
" 6th digit " pre-event activity of the behavior, Ba
" 7th digit " pre-event evaluation of the object, Oe
" 8th digit " pre-event potency of the object, Op
" 9th digit " pre-event activity of the object, Oa
" none of the digits " equation constant
" more than one digit " multiplication of several pre-event quantities
Now let's put together the equation for predicting the outcome evaluation of an actor as a result of an event, Ae'. The column of zero-one numbers, the term that those numbers define, and then the first column of decimal numbers are as follows.
Z000000000 constant -0.25
Z100000000 Ae 0.44
Z010000000 Ap 0.00
Z001000000 Aa 0.01
Z000100000 Be 0.41
Z000010000 Bp -0.04
Z000001000 Ba -0.10
Z000000100 Oe 0.02
Z000000010 Op -0.02
Z000000001 Oa -0.01
Z100100000 Ae·Be 0.05
Z100010000 Ae·Bp -0.03
Z100000100 Ae·Oe 0.00
Z100000010 Ae·Op 0.01
Z010100000 Ap·Be 0.01
Z010010000 Ap·Bp 0.00
Z010000010 Ap·Op 0.02
Z010000001 Ap·Oa -0.01
Z001001000 Aa·Ba 0.00
Z000100100 Be·Oe 0.13
Z000100010 Be·Op -0.06
Z000010100 Bp·Oe -0.06
Z000010010 Bp·Op 0.07
Z000010001 Bp·Oa 0.01
Z000001010 Ba·Op 0.03
Z100100100 Ae·Be·Oe 0.03
Z100010010 Ae·Bp·Op 0.02
Z010010010 Ap·Bp·Op -0.02
Z010010001 Ap·Bp·Oa 0.02
We start off building the equation by representing the equation constant like this:
Ae' = -0.25
Ae is the evaluation of the actor before the event; it is associated with a large coefficient, 0.44, so evaluation of the actor before an event has a major impact on evaluation after the event. By including the term for Ae the equation becomes:
Ae' = -0.25 + 0.44·Ae
The actor's potency before the event doesn't affect evaluation afterwards - the coefficient for Ap is zero. In fact, let's consider any coefficient with a magnitude of 0.10 or less as too small to count for our heuristic purposes at the moment. Then the next term that is important is Be with a coefficient of 0.41. The equation becomes
Ae' = -0.25 + 0.44·Ae + 0.41·Be
Continuing this way down the column we get the following equation for predicting the evaluation of the actor after an event has happened.
Ae' = -0.25 + 0.44·Ae + 0.41·Be - 0.10·Ba + 0.13·Be·Oe
Note that Be·Oe - the product of behavior evaluation with evaluation of the object person - is positive if someone does something nice to a nice person or does something bad to a bad person; the quantity is negative when evaluations of behavior and object person do not correspond - when one evaluation is negative and the other is positive.
Thus the equation says that good impressions of actors are created when the actors are good to begin with, when they engage in good and quiet behaviors, and when behavior evaluations are appropriate to the evaluation of object persons. Remember in the Janet-Mary analysis: Mary comforted Janet which was an unusually nice and quiet act from a friend. Now you see why Mary did this. Mary had to act nice in order to confirm being a friend - the goodness of behavior has a very strong effect in determining what kind of impression is created. However, being nice to a gloomy woman friend gained nothing for Mary in terms of matching act to object (the Be·Oe effect), so Mary had to build up extra goodness by choosing an act that is especially good and quiet.
We have achieved what we wanted - a general sense of how Interact arrives at predictions. Of course, Interact computations do not drop terms with coefficients having a magnitude less than 0.10 because many such terms actually are important in representing human psychology. The following full equation with all terms is the one employed in Interact.
Ae' = -0.25 + 0.44·Ae + 0.00·Ap + 0.01·Aa + 0.41·Be - 0.04·Bp - 0.10·Ba + 0.02·Oe - 0.02·Op - 0.01·Oa + 0.05·Ae·Be - 0.03·Ae·Bp + 0.00·Ae·Oe + 0.01·Ae·Op + 0.01·Ap·Be + 0.00·Ap·Bp + 0.02·Ap·Op - 0.01·Ap·Oa + 0.00·Aa·Ba + 0.13·Be·Oe - 0.06·Be·Op - 0.06·Bp·Oe + 0.07·Bp·Op + 0.01·Bp·Oa + 0.03·Ba·Op + 0.03·Ae·Be·Oe + 0.02·Ae·Bp·Op - 0.02·Ap·Bp·Op + 0.02·Ap·Bp·Oa
Interact also employs impression-formation equations describing how modifiers combine with identities. For example, here is the short-hand representation of the U.S.A. male equations.
Z000000 -0.26 -0.18 0.07
Z100000 0.67 -0.15 0.05
Z010000 -0.29 0.76 -0.09
Z001000 -0.11 0.06 0.67
Z000100 0.47 -0.02 0.01
Z000010 -0.02 0.56 -0.09
Z000001 0.00 0.07 0.67
Z100100 0.12 0.00 0.00
The three columns of decimal numbers define the equations for predicting the evaluation, potency, and activity of a combination. The first three Z digits refer to the evaluation, potency, and activity of the modifier. The second three digits refer to the evaluation, potency, and activity of the identity.
For example, the equation for predicting the evaluation of a modifier-identity combination is:
Ce = -.26 + .67·Me - .29·Mp - .11·Ma + .47·Ie - .02·Ip + .00·Ia + .12·Me·Ie
In words, the evaluation of the combination, Ce, is predictable by summing approximately two-thirds of the modifier evaluation, Me, and half of the identity evaluation, Ie, and then adjusting a little for modifier potency and activity, Mp and Ma, and for evaluative consistency between the modifier and identity, Me·Ie.
Interact frequently turns the amalgamation equations inside out in order to find modifiers - for example, in order to find emotion terms that can be combined with a person's identity in order to describe the person's transient state.
Note. From David R. Heise and Elsa Lewis, Introduction to Interact, documentation for programs Interact, Tech, and Attitude, distributed by Wm. C. Brown Publishers, Dubuque, Iowa 1988-1993.