how can ACT-R models age?

[Lynne]

yet arrived in my mailbox.  I might have been inhibited to mention it 
given his comments; however, given his cavalier dismissal of my view 
(I would not be surprised if CL, ML and LD agree, but I can not speak 
for them), I am motivated to comment
further.

My impression of aging effects is that the *biggest* liability of 
aging is in acquiring new information not retrieving old 
information--this occurs  with  poor encoding of simple facts, but is 
most pronounced in learning new skills and concepts.  It seems that 
older individuals are most handicapped in domains where their prior 
knowledge is of least value, e.g., learning new technologies or video 
games.   It is not obvious to me how Erik's filling up the brain with 
too many chunks would predict that general pattern/problem with
aging.  My hunch (and again, since it has not been simulated it is only a
hunch) is that W would do a better job of explaining that aspect of 
the demise of intellect with age.

Erik's other remark was that his proposal was a natural consequence 
of aging, while W is a free parameter.  Well, it would at least be
constrained to go down with age, not up.  Moreover, it is a single parameter,
while my reading of Erik's proposal involved twiddling several parameters,
but perhaps I'm mistaken (low W and lots of decayed chunks due to my 
advanced age, or at least, that's my excuse).

--L.

p.s. Since Bush's coronation by the Supreme Court, I've wanted to change
the name of the parameter to something other than W.


At 6:51 PM -0800 1/4/01, Erik M. Altmann wrote:
>At 5:09 PM -0500 1/4/01, Dario Salvucci wrote:
>>Might anyone know the current status of work relating ACT-R and 
>>aging?  In particular, I'm wondering if anyone has done work toward 
>>the following question: Given a "young expert" ACT-R model, is 
>>there a general (domain-independent) way of making it an "elderly" 
>>model simply by changing appropriate parameters?  For instance, one 
>>might imagine that cycle time increases by some percentage causing 
>>general slowdown (there seems to be EPIC work suggesting this), or 
>>that W decreases, and/or that the latency of certain 
>>perceptual-motor parameters increases.  I'm specifically interested 
>>in modeling elderly drivers using an existing model of younger 
>>drivers, but I'm hoping to carry over any related results / 
>>parameter changes from other domains if at all possible.
>
>I've been thinking about a representation of age in which the brain 
>fills up with chunks that aren't completely decayed.  For this to 
>have typical aging effects, I believe you have to dispense with the 
>retrieval threshold and particularly with indexed retrieval (in 
>which you force the retrieval of a specific chunk, and give up if 
>that specific chunk isn't above the retrieval threshold).  That is, 
>you have to be willing to let activation play the role it's meant to 
>play under rational analysis, and let it predict the need for a 
>chunk based on history and context.  If you do this, then on a given 
>retrieval cycle the accuracy of the retrieval (in terms of 
>probability of retrieving the target chunk) is determined by the 
>chunk choice equation, which factors in the activation of all old 
>chunks in memory.  The more old chunks there are, the lower the 
>probability of retrieving the correct one, and the greater the 
>probability of going off on a tangent as a function of a 
>mis-retrieval.  This captures, at an abstract level, the general 
>aging phenomenon of decreasing ability to inhibit irrelevant 
>information.  This kind of forgetting model (in which the main 
>factors are interference and decay and not so much the retrieval 
>threshold) also seems quite general, with successful applications to 
>the Tower of Hanoi, task switching, order memory, Brown-Peterson, 
>and the Waugh and Norman probe digit task.  Some of these 
>applications involve long-term as well as short-term memory.
>
>Some other pieces to the puzzle.  First, if cognitive aging is 
>represented this way, the existence of vast numbers of old chunks is 
>counterbalanced by the fact that each individual one is highly 
>decayed and so contributes little to the denominator of the chunk 
>choice equation.  So the basic idea seems structurally plausible -- 
>background noise in the head increases gradually over a lifetime. 
>Second, to approximate the effect of tens or hundreds of millions of 
>chunks in memory, one can simply increase activation noise (s).  The 
>decline in accuracy (as governed by the chunk choice equation) 
>follows a slightly different curve, but is still curvilinear and 
>makes for a much more efficient simulation.  In my order memory 
>model, I use this trick to account for order reconstruction accuracy 
>at a retention interval of 24 hours, in which chunks would 
>ordinarily be added to memory at a rate of several per second 
>(assuming that people don't turn their brains off when they're not 
>thinking about your task).  Third, the advantage of representing 
>aging in terms of declarative memory filling up is that it actually 
>specifies an aging mechanism, unlike twiddling W, for example.
>
>Erik.
>
>--
>
>~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
>Erik M. Altmann
>Department of Psychology
>Michigan State University
>East Lansing, MI  48824
>517-353-4406 (voice)
>517-353-1652 (fax)
>ema at msu.edu
>http://www.msu.edu/~ema
>~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

-- 

__________________________________________________________
Lynne M. Reder, Professor
Department of Psychology
Carnegie Mellon University
Pittsburgh, PA 15213

phone:     (412)268-3792
fax:          (412) 268-2844
email:      reder at cmu.edu
URL:         http://www.andrew.cmu.edu/~reder/reder.html