Code to plot activation growth & Mental lexicon/Lexical decision questions

[Hedderik van Rijn]

In the last couple of weeks, I've been investigating how to implement a
large mental lexicon in ACT-R. (Consisting of all 4 letter words the CELEX
(http://www.kun.nl/celex/) English orthographic word-forms database.)

** Code to plot activation growth

However, during tests with the model that uses this lexicon, I discovered to
my shame that I my knowledge of how the activation of chunks is
calculated/approximated fell short in certain important areas. Because just
reading formulas doesn't give me "feeling" for fine nuances, I wrote some
R/S code to plot figures like Figure 4.1 in the Atomic Components of Thought
book. For those interested, it is available at:

  http://swipc30.swi.psy.uva.nl/~rijn/actr-activations/

(Evaluate fig4.1() and fig4.1.2() to get plots like Fig4.1)

After playing around, a lot of the issues involving activation became a lot
clearer to me. However, there is one issue still unsolved.

** Approximated activation with d=.9 is too high?

If the decay (d) is set to .5, like in Fig 4.1 of the book, the "real
base-level activation equation" is closely approximated by the "optimized
learning base-level activation equation". (As is argued at p.124 of the
book.) However, if d is set to a very high value, for example .9, the
approximation equation seems to yield structurally higher base-level
activations than the real equation. This is illustrated in the two plots
shown on the same web page as above. (I didn't attach these graphs not
clutter the mailing list with binary information.) Can someone shed some
light on this issue? Is the approximation function indeed "better" for
values of d close to .5? Or is there a bug somewhere in my
interpretation/code?

** Mental Lexicon/Lexical Decision task questions

The project that was causing this all, is an attempt to model lexical
decision data. I searched around a bit, but did not find any previous
ACT-R (or related) modeling for this task. However, if someone can point
me to relevant information, I would be very pleased.

Alternatively, less specifically, did someone already try to model a large
mental lexicon (current model has +- 2500 word entries) in ACT-R? I would
like to discuss some issues involving, for example, representations or
activations of low frequent words. Same as for the previous question,
pointers to relevant modeling literature would be very welcome.

 - Hedderik.