From Stefani_Nellen at psi-sv2.psi.uni-heidelberg.de  Fri Dec 10 13:25:19 1999
From: Stefani_Nellen at psi-sv2.psi.uni-heidelberg.de (Stefani Nellen)
Date: Fri, 10 Dec 1999 19:25:19 +0100
Subject: (Fwd) ACT & LISP
Message-ID: <mailman.401.1617128274.6729.act-r-users@act-r.psy.cmu.edu>



Hello,

I want to include some calls to LISP-functions in the model I'm 
working on. The psychological plausibility of these remains to be 
discussed another time; right now I have some technical questions:

a)  where do I define the LISP-functions called for in the model ? Is 
it possible to write them directly in the MISC-window while editing 
the project ? Or is it better to define the functions in a LISP-file 
and load the file before each model run ? (how) ?

b) where do I write the scripting-extension-code ? (question is 
analogous to a))

I would be very grateful if someone could answer my questions !
cheers
Stefani


From wschoppe at osf1.gmu.edu  Fri Dec 10 15:13:46 1999
From: wschoppe at osf1.gmu.edu (Wolfgang Schoppek)
Date: Fri, 10 Dec 1999 15:13:46 -0500
Subject: (Fwd) ACT & LISP
Message-ID: <mailman.402.1617128274.6729.act-r-users@act-r.psy.cmu.edu>

Hello Stefani,

> I want to include some calls to LISP-functions in the model I'm
> working on. The psychological plausibility of these remains to be
> discussed another time; right now I have some technical questions:
>
> a)  where do I define the LISP-functions called for in the model ? Is
> it possible to write them directly in the MISC-window while editing
> the project ?

Yes.

> Or is it better to define the functions in a LISP-file
> and load the file before each model run ? (how) ?

This might be better, if your LISP code is quite large and you don't want
(or need) to load it each time you reload your model. Files can be loaded
with the programming environment or with the funktion (load ...).

> b) where do I write the scripting-extension-code ? (question is
> analogous to a))

Same answer as above. I would write it in the misc window since it is
closely related to the model (using the same symbols).

Tschuess,
-- Wolfgang
--------------------------------------------------------------------
 Dr. Wolfgang Schoppek                <<< Tel.: +1 703-993-4663 <<<
 HUMAN FACTORS & APPLIED COGNITION PROGRAM, George Mason University,
 Fairfax, VA 22030-4444
  http://www.uni-bayreuth.de/departments/psychologie/wolfgang.htm
--------------------------------------------------------------------




From db30+ at andrew.cmu.edu  Fri Dec 10 15:27:27 1999
From: db30+ at andrew.cmu.edu (Daniel J Bothell)
Date: Fri, 10 Dec 1999 15:27:27 -0500 (EST)
Subject: (Fwd) ACT & LISP
Message-ID: <mailman.403.1617128274.6729.act-r-users@act-r.psy.cmu.edu>

Excerpts from mail: 10-Dec-99 Re: (Fwd) ACT & LISP by Wolfgang Schoppek at osf1.g 
>  
> > Or is it better to define the functions in a LISP-file
> > and load the file before each model run ? (how) ?
>  
> This might be better, if your LISP code is quite large and you don't want
> (or need) to load it each time you reload your model. Files can be loaded
> with the programming environment or with the funktion (load ...).


    If you are running the Windows version of the environment there is
a performance advantage to using the Misc window.  ACL does not compile
functions when they are loaded (MCL compiles everything by default).  To
improve performance, the environment compiles the Misc window when you
open a model and whenever you press the reload button on the Control 
Panel.  Files included with the model are not compiled.  You could compile
the files manually, and then load the fasl with a load call in the model, 
but you would not be able to edit the file using the model manager, 
because it would try to open the fasl for editing.  You could also use the
compile function to load the file, but then it would not be included 
in the model manager list of files.


Dan 


From altmann at osf1.gmu.edu  Mon Dec 20 13:40:54 1999
From: altmann at osf1.gmu.edu (ERIK M. ALTMANN)
Date: Mon, 20 Dec 1999 13:40:54 -0500 (EST)
Subject: ACT-R submissions for comment
Message-ID: <mailman.404.1617128274.6729.act-r-users@act-r.psy.cmu.edu>

  This message is in MIME format.  The first part should be readable text,
  while the remaining parts are likely unreadable without MIME-aware tools.
  Send mail to mime at docserver.cac.washington.edu for more info.

--------------461113D558C
Content-Type: TEXT/PLAIN; charset=US-ASCII


Altmann & Gray.  The Anatomy of Serial Attention: An integrated model
of set shifting and maintenance.  Submitted to Psychological Science.

        Serial attention is the process of adopting a task set (or intention
        to do a task), maintaining that set for a time, then shifting
        set. When set must be shifted often, serial attention is difficult
        because old sets cannot be forgotten instantly and will interfere with
        the current one. This memory perspective on set shifting is the basis
        for a computational cognitive model that explains a variety of effects
        in serial attention, some well-documented (e.g., RT switch cost) and
        some novel (e.g., prolonged error switch cost and within-run
        performance decline). The mechanisms producing these effects are
        tightly integrated with each other and with memory theory. The model
        absorbs extant accounts of set shifting, including failure to prepare
        (De Jong, Berendsen, & Cools, 1999) and exogenous cueing (Rogers &
        Monsell, 1995), and promises to unify the effects presented here with
        other set-shifting phenomena.

Altmann.  Memory in Chains: A dual-code associative model of
positional uncertainty.  Submitted to ICCM.

        A persistent problem in modeling human memory has been how to explain
        positional uncertainty (or "near-miss" error) in memory for serial
        order. Models like perturbation (Estes, 1997), primacy (Henson,
        Norris, Page, & Baddeley, 1996), and partial matching (Anderson &
        Matessa, 1997) can fit uncertainty gradients but fail to address how
        cognition might encode the underlying memory representation in the
        first place. This paper introduces a model of uncertainty gradients
        that explains the encoding as well as the reconstruction of order
        information. The model is based on an integrated and computational
        cognitive theory (ACT-R/PM) that provides the central mechanisms: a
        dual-code representation of attended items, associative learning, and
        noise in activation levels. The model addresses the main structural
        objection to chaining models (over- predicting relative or "shift"
        error), fits experimental data better than the perturbation and
        partial matching models, and makes principled predictions concerning
        primacy and recency effects in memory for order.





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