[From Samuel Saunders (951224:1:39:33 EST)]
[From Rick Marken (951222.1500)]
How, for example, is v1(B) related to observed response rate; how is
v0(C) related to observed reinforcement rate?
Probability of response P1(B) is v1(B)/(v1(B) + v(M)), assuming the the
base response only occurs when the contingent response C is unavailable.
This occurs in proportion (1-t) of the session, or over (1-t) * session
length time. There will be P1(B) * ((1-t) * session length) time
occupied by the base response B. If it takes time a to make a
response, the number of responses is then:
( P1(B) * ((1-t) * session length))/a
This is all a bit circular for FR, since the feedback function determines
the relation between number of responses and reinforcement time, so if the
total reinforcement time is known, the total number of reinforcements and
hence the total number of responses is known as well. If I had a clear
answer for this problem, I would have posted this a week earlier, but I
wanted to put something at least a little realistic into play, so I decided
to post this much. As I said, it is incomplete at best.
I'm glad you want to work on PCT models but I am _really_ puzzled by your
reluctance to compare and contrast PCT and reinforcement theory? I really
don't get it. Could you help me out here, Sam?
I don't think that comparing and contrasting reinforcement theory and PCT
is a bad idea. I do think that it can't be done with one experiment or one
data set. 'Reinforcement theory' consists of an array of approaches
loosely tied together by the concept of reinforcement. The domains of
interest are often divided between 'learning' and 'motivation', and what
variables, phenomenology, and theoretical concepts belong to each varies
from approach to approach. Given this diversity, it is almost certain that
a few 'reinforcement models' can accommodate any result that we may present.
The only solution, I think, will come from a sustained attack on some
problem area. In that case, it should be possible to show a series of
results, all consistent with PCT, which, somewhere within the series,
conflict with most of the well supported alternatives.
As a preliminary, I am trying to organize a set of more or less generic
representatives of classes of 'reinforcement theory'. This first in the
series was intended to represent the 'relative value/motivational' class.
Here's my problem: We have two theories of behavior (reinforcement and
PCT) that are based on totally different assumptions about how organisms
function; one (reinforcement theory) says that organisms function by
emitting behaviors (actions) that are strengthened or weakened (whether
the organism "likes it or not") by their consequences; the other (PCT)
says that organisms function by varying behaviors (actions) as necessary
to produce the consequences they want.
What could possibly be wrong with comparing these two drastically (and
rather importantly) different views to see which one seems closer to being
the correct picture of how organsisms work? This is a "real life"
question;
there are a lot of people running around out there -- some of them in very
important positions in society -- who believe, deep down in their hearts,
that people operate according to reinforcement theory. These people think
that rewards, punishments, incentives, and contingencies are essential
for dealing with employees, the unemployed, students, children, welfare
recipients, criminals, etc. Why don't we give the world a nice present in
1996 and show that the "reinforcement story" is just another Western myth;
let's show that behavior selects, and is not selected by, its
consequences.
There is also a lot of folk Psychology based on 'rewards' and
'punishments'. I agree that 'getting it right' is important. The world
has gotten by for centuries with that folk Psychology, and for a century or
two with a scientific Psychology based on similar assumptions. Why not
spend a little more time and get the counter argument right, and make it
convincing? An effort aimed at making the point all at once runs the risk
that a 'plausible' counter will be mounted, and the PCT approach will move
from mostly unknown to 'known to be wrong' (not perhaps because it failed
to predict correctly, but because it 'predicted that reinforcement theory
couldn't deal with x, when in fact it could'). I would hate to end up
making it harder for PCT to break through into the mainstream of social
science than it already has been.
If PCT can be shown to produce interesting ideas, deal with existing data,
and suggest new and interesting research in some restricted domain, perhaps
some of the workers in that domain will take an interest, and begin to
spread a PCT approach to related domains. I am sure a slow spread of PCT
would be frustrating to those who have invested a great deal in PCT
already, but I think that is a more realistic view of how scientific
revolutions really occur.
//----------------------------------------------------------------------------
//Samuel Spence Saunders,Ph.D.