From TRJohnson at mail.sahs.uth.tmc.edu Sun Sep 2 19:15:12 2001 From: TRJohnson at mail.sahs.uth.tmc.edu (Todd R Johnson) Date: Sun, 2 Sep 2001 18:15:12 -0500 Subject: Competitive latency problem Message-ID: problematic for certain kinds of tasks. In the model I'm working on, subjects briefly see thirty three-shape exemplars of the form aba, such as (circle triangle circle) and (diamond circle diamond). They are then presented a series of test triples and must indicate whether the test triple was or was not seen during study. Experimental results show that subjects sometimes base their decision only on whether the form matches (e.g, aba vs. abb). The model uses a positional encoding for each element of a triple and also stores the relationship (aba) with each element: (s11 isa entity value star location pos1 relation aba exemplar e1) When given a test triple, one production attempts to retrieve an entity with the same relation. As a result of the representation, there are 90 entity chunks. With partial matching on, the latency paramater (F) at 1, and goal activation set to 0, Act-R takes nearly 90 seconds to retrieve the relationship. Even with F at a more reasonable .05, it still takes around 4.5 seconds. Turning on goal activation doesn't help much, because the shapes in the test triple activate many of the entities. It possible to produce more reasonable times by assuming that subjects notice the aba pattern and store it separately. However, its easy to think of tasks where this is not possible. For instance, the Sternberg model in Unit 6 avoids this problem becasue Act-R is reset before each trial. But of course subjects cannot simply reset their memory. I suspect that if the model were written to retain all of the previously studied items, that each successive trial would take slightly longer than the previous trial. I've tried to do this, but it seems that Act-R 5 is no longer creating chunks on the RHS, as in: +goal> ISA do-sternberg trial =trial The dummy trial chunk should allow the model to discriminate between old and new memory sets, but in this case, I end up with a new goal where trial is nil. It seems odd to me that it would take so long to recognize an item when you have studied many similar items. In fact, I would think that it would take less time. Perhaps this has something to do with the difference between recognition and recall. In Act-R to recognize, you must recall. Perhaps recognition is a different process with a different time equation. Todd From Luis.Botelho at iscte.pt Tue Sep 4 11:54:12 2001 From: Luis.Botelho at iscte.pt (Luis Botelho) Date: Tue, 4 Sep 2001 16:54:12 +0100 Subject: Virus Message-ID: My computer was recently infected with a virus. As one of the consequences, it automatically sent infected email messages to several address of my email address book. You might have received one or more of such infected messages. If it is not too late, don't try to open the attach file. I am sorry for that -- Luis From reder at andrew.cmu.edu Tue Sep 4 14:46:04 2001 From: reder at andrew.cmu.edu (Lynne Reder) Date: Tue, 4 Sep 2001 14:46:04 -0400 Subject: Virus Message-ID: >Hi all >My computer was recently infected with a virus. As one of the consequences, >it automatically sent infected email messages to several address of my email >address book. >You might have received one or more of such infected messages. >If it is not too late, don't try to open the attach file. >I am sorry for that > >-- Luis Luis, Do you use Windows or a Mac or? My experience is that Macs tend not to get infected ;-) --Lynne From N.A.Taatgen at ai.rug.nl Thu Sep 20 05:28:05 2001 From: N.A.Taatgen at ai.rug.nl (Niels Taatgen) Date: Thu, 20 Sep 2001 11:28:05 +0200 Subject: Post-doc position Message-ID: use of ACT-R for User Modeling. This project is part of a larger "Token"-project, which is funded by the Dutch Science Organization (NWO). Anyone who is interested can contact me for more information, or if you know someone else who might be interested, please pass on this message. Niels Taatgen ALICE ArtificiaL Intelligence and Cognitive Engineering University of Groningen Grote Kruisstraat 2/1 9712 TS Groningen, Netherlands niels at ai.rug.nl http://www.ai.rug.nl/~niels =================================================================== Postdoc position (two year) in cognitive modeling The post-doctoral researcher will participate in the Optima project (Optimal personal interfaces through manumitting agents). The goal of this project is to develop agent based on the ACT-R cognitive architecture that can adapt themselves to individual users. The project has to main parts: a part that focusses on the development of theory, mainly in the area of skill acquisition, and a part that focusses on application: how can the learning component be implemented in useful way in an application. More information on the project can be found on the following web page: http://www.ai.rug.nl/~niels/optima/description.html We are looking for someone with a Ph.D. in cognitive science, computer science or artificial intelligence, and experience with cognitive modeling, preferably using the ACT-R architecture. If you are interested in this position, or want more information, please contact dr. N.A. Taatgen, +31 50 3636435, email: niels at ai.rug.nl From cl at andrew.cmu.edu Fri Sep 21 15:00:28 2001 From: cl at andrew.cmu.edu (Christian Lebiere) Date: Fri, 21 Sep 2001 15:00:28 -0400 Subject: Postdoctoral Position @ CMU Message-ID: The postdoctoral researcher will take the lead in developing cognitive models of individual performance in close quarter combat environments. These models will closely emulate human behavior in every respect, including cognitive, perceptual and motor. The goal of the project is to use those models as synthetic agents to animate opponents in virtual reality training simulations. The models will be developed using the ACT-R cognitive architecture, a hybrid architecture combining a rule-based production system with adaptive neural-like activation processes and perceptual and motor modules. This project will present a wide range of technical and scientific challenges, including three-dimensional perception, full-body motion, and decision-making in a real-time fast-paced dynamic multi-agent environment featuring approximate and uncertain information. Requirements include a Ph.D. in cognitive science, computer science or psychology with cognitive modeling experience, preferably but not necessarily using the ACT-R cognitive architecture. . Applicants are expected to have an interest in all aspects of cognitive modeling. Programming experience, especially in Lisp, and an interest in video/computer games are preferred. This position is funded for two years by the Office of Naval Research and offers a highly competitive salary and benefits. Carnegie Mellon University offers a stimulating research environment in livable Pittsburgh, Pennsylvania. To apply or obtain additional information, contact (email preferred): Dr. Christian Lebiere Human-Computer Interaction Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 Tel: 412-268-5920 Email: cl at cmu.edu