[ACT-R-users] Efficient approximation of the Base-Level Learning Equation

[Alex Petrov]

Dear ACToRs,

As you know, the base-level equation is one of the pillars of ACT-R's success. 
It has a serious practical drawback, however -- it consumes a lot of memory 
and CPU cycles.  The approximate formula published in the 1998 book is good 
for many purposes but does not capture the transient boost after each use of 
a chunk. I have developed an improved approximation, which does take all 
critical properties of the exact equation into account. It was used with 
great success in the ANCHOR model of Petrov & Anderson (2005).  It can be 
very useful for large simulations, allowing ACT-R to scale up to more 
realistic memory sizes and more prolonged learning periods.  As far as I know 
(though I'm not sure), it is already implemented in the current version of 
ACT-R.  (Dan, can you please advise on that?)

A short two-page description of this new approximation has just been submitted 
to ICCM06.  It is attached below, together with its associated MATLAB code.  
All this stuff will soon be posted on my web page at  http://alexpetrov.com

I do hope this will be helpful to the ACT-R community.

Best regards,

  -- Alex

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Alexander A. Petrov:  apetrov at alexpetrov.com

Post-doctoral Researcher
Department of Psychology
University of Colorado, Boulder
http://alexpetrov.com

It is better to light one candle than to curse the darkness.
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