From gray at gmu.edu Fri Jun 8 05:48:19 2001 From: gray at gmu.edu (Wayne Gray) Date: Fri, 8 Jun 2001 11:48:19 +0200 Subject: ICCM-2001 Early Registration Deadline Message-ID: then rates for both students and non-students go up. Our full conference program is now listed on the web. Abstracts are available. ********************* The Fourth International Conference on Cognitive Modeling. http://hfac.gmu.edu/~iccm/ The meeting will be held from July 26 thru July 28. The program will be posted on the web site next week. We have an international collection of cognitive modelers of all types including connectionist models, symbol-system models, baysian models, math models, models of emotion, models of personality, models of the brain, models of memory and language, models that drive cars (while using their cellular phones), models that do Air Traffic Control Tasks, and so on and so forth. Hope to see you here (Fairfax Virginia, just outside Washington, DC) this summer! Wayne From lovett at andrew.cmu.edu Sat Jun 9 23:01:02 2001 From: lovett at andrew.cmu.edu (Marsha Lovett) Date: Sat, 09 Jun 2001 22:01:02 -0500 Subject: Modeling individual differences query Message-ID: data from all you ACT-R modelers regarding the inclusion of individual difference issues in your modeling work. Please respond if you have done computational modeling of cognition in a way that made explicit analysis of/reference to individual differences among participants. Here are a few more specific questions, too: #1 Was your modeling in ACT-R? Which version? (and of course, please point me to a citation if the work was published) #2 What kind of individual difference factor did you model? What task(s) did you model? At what level of aggregation did you fit the data (e.g., subgroups of participants, extremes, individuals)? #3 Did you model this individual difference factor by using different knowledge sets (procedural or declarative)? Or did you model this by using different parameter values (which paramater?) #4 Did you use other empirical measures to triangulate this individual difference? Thanks! -Marsha From rsun at cecs.missouri.edu Sun Jun 17 13:40:55 2001 From: rsun at cecs.missouri.edu (rsun at cecs.missouri.edu) Date: Sun, 17 Jun 2001 12:40:55 -0500 Subject: THE INTERACTION OF EXPLICIT AND IMPLICIT LEARNING: A Symposium at CogSci'2001 Message-ID: THE INTERACTION OF EXPLICIT AND IMPLICIT LEARNING A Symposium at CogSci'2001 (August 1-4, 2001), Edinburgh, Scotland =============================================================== Titles of the Talks: Axel Cleeremans: ``Behavioral, neural, and computational correlates of implicit and explicit learning" Zoltan Dienes: ``The effect of prior knowledge on implicit learning" Bob Mathews: ``Finding the optimal mix of implicit and explicit learning" Ron Sun: ``The synergy of the implciit and the explicit" ================================================================= The symposium will be held on August 4th, 2001, 2:30 - 4:10 pm. See http://www.hcrc.ed.ac.uk/cogsci2001/programme.html for futher details of the 23rd Cognitive Science Conference, Edinburgh, Scotland. ================================================================= Background: The role of implicit learning in skill acquisition and the distinction between implicit and explicit learning have been widely recognized in recent years (see, e.g., Reber 1989, Stanley et al 1989, Willingham et al 1989, Anderson 1993), Although implicit learning has been actively investigated, the complex and multifaceted interaction between the implicit and the explicit and the importance of this interaction have not been universally recognized; to a large extent, such interaction has been downplayed or ignored, with only a few notable exceptions. Research has been focused on showing the LACK of explicit learning in various learning settings (see especially Lewicki et al 1987) and on the controversies stemming from such claims. Despite the lack of studies of interaction, it has been gaining recognition that it is difficult, if not impossible, to find a situation in which only one type of learning is engaged (Reber 1989, Seger 1994, but see Lewicki et al 1987). Our review of existing data has indicated that, while one can manipulate conditions to emphasize one or the other type, in most situations, both types of learning are involved, with varying amounts of contributions from each (see, e.g., Sun et al 2000; see also Stanley et al 1989, Willingham et al 1989). Likewise, in the development of cognitive architectures (e.g., Rosenbloom et al 1993, Anderson 1993), the distinction between procedural and declarative knowledge has been proposed for a long time, and advocated or adopted by many in the field (see especially Anderson 1993). The distinction maps roughly onto the distinction between the explicit and implicit knowledge, because procedural knowledge is generally inaccessible while declarative knowledge is generally accessible and thus explicit. However, in work on cognitive architectures, focus has been almost exclusively on ``top-down" models (that is, learning first explicit knowledge and then implicit knowledge on the basis of the former), the bottom-up direction (that is, learning first implicit knowledge and then explicit knowledge, or learning both in parallel) has been largely ignored, paralleling and reflecting the related neglect of %the complex and multifaceted the interaction of explicit and implicit processes in the skill learning literature. However, there are a few scattered pieces of work that did demonstrate the parallel development of the two types of knowledge or the extraction of explicit knowledge from implicit knowledge (e.g, Willingham et al 1989, Stanley et al 1989, Sun et al 2000), contrary to usual top-down approaches in developing cognitive architectures. Many issues arise with regard to the interaction between implicit and explicit processes, which we need to look into if we want to better understand this interaction: How can we best capture implicit processes computationally? How can we best capture explicit processes computationally? How do the two types of knowledge develop along side each other and influence each other's development? Is bottom-up learning (or parallel learning) possible, besides top-down learning? How can they (bottom-up learning, top-down learning, and parallel learning) be realized computationally? How do the two types of acquired knowledge interact during skilled performance? What is the impact of that interaction on performance? How do we capture such impact computationally? =========================================================================== Prof. Ron Sun http://www.cecs.missouri.edu/~rsun CECS Department phone: (573) 884-7662 University of Missouri-Columbia fax: (573) 882 8318 201 Engineering Building West Columbia, MO 65211-2060 email: rsun at cecs.missouri.edu http://www.cecs.missouri.edu/~rsun http://www.cecs.missouri.edu/~rsun/journal.html http://www.elsevier.com/locate/cogsys =========================================================================== From mspears at missvalley.com Wed Jun 20 09:04:43 2001 From: mspears at missvalley.com (mike spears) Date: Wed, 20 Jun 2001 13:04:43 +0000 Subject: Req. for list info Message-ID: Thank you, Michael Spears,MA,MSW Rt.1,Box 82-D Moberly,Missouri 65270 USA From gray at gmu.edu Sun Jun 24 11:46:26 2001 From: gray at gmu.edu (Wayne Gray) Date: Sun, 24 Jun 2001 11:46:26 -0400 Subject: Job Openings Message-ID: let me know. We see ICCM as an especially good time to discuss these positions with foreign colleagues who might not otherwise be familiar with GMU or Northern Virginia. (I will also be at Edinburgh for CogSci-2001.) Wayne ******************** Two job openings in Cognition at the Associate and Assistant Level. Cognition. The Department of Psychology at George Mason University anticipates two openings beginning in the Fall, 2002, one at the assistant level and one at the associate level. The ideal candidates will have a Ph.D. in Cognitive Psychology or related area and experience developing cognitive theory, preferably in the context of real-world problems. We will consider applicants from a variety of research specializations, such as computational cognitive modeling, human computer interaction, human factors, psychology of science, complex problem solving, higher level cognition, visual attention, training (especially computer-based), human performance, and decision making. A record of or strong potential for external funding is expected, and evidence of teaching skills and multi-disciplinary interests is highly desirable. Positions are targeted at the assistant and associate professor levels; however senior applicants with external funding may be considered. George Mason University is located approximately 15 miles SW of Washington, DC and is the newest university in the Virginia Commonwealth system. The psychology department has Ph.D. programs in applied cognitive psychology, industrial/organizational psychology, developmental, and clinical psychology. The successful candidate will join the core faculty of the Human Factors and Applied Cognitive Program in the ARCH Lab. The ARCH Lab houses HFAC faculty, their research facilities, and graduate students in a collaborative and highly productive environment. Applications will be evaluated starting on November 15, 2001, and will continue until a suitable candidate is found. A vita, three letters of recommendation, a brief statement of research and teaching interests, and copies of relevant preprints/reprints should be sent to: Cognitive Search Committee, George Mason University, MSN 3F5, Fairfax, VA 22030-4444. For more information about the Human Factors & Applied Cognitive Program, see our web page: http://www.hfac.gmu.edu. We encourage applications from women and minority candidates. George Mason University is an Affirmative Action/Equal Opportunity Employer. If you have any questions about this position, please contact Wayne Gray (gray at gmu.edu) or Deborah Boehm-Davis (dbdavis at gmu.edu). -- _/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/ Wayne D. Gray, Professor of Psychology Program Director HUMAN FACTORS & APPLIED COGNITIVE PROGRAM SNAIL-MAIL ADDRESS (FedX et al) VOICE: +1 (703) 993-1357 George Mason University FAX: +1 (703) 993-1330 ARCH Lab/HFAC Program ************************ MSN 3f5 * Work is infinite, * Fairfax, VA 22030-4444 * time is finite, * http://hfac.gmu.edu/~gray * plan accordingly. * _/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/ From convas at ccs.yonsei.ac.kr Wed Jun 27 05:43:58 2001 From: convas at ccs.yonsei.ac.kr (=?ks_c_5601-1987?B?sejH9sijXChraW0gaHl1biBob1wp?=) Date: Wed, 27 Jun 2001 18:43:58 +0900 Subject: subscribe Message-ID: ------=_NextPart_000_0005_01C0FF39.20643230 Content-Type: text/plain; charset="ks_c_5601-1987" Content-Transfer-Encoding: base64 SSdkIGxpa2UgdG8gc3Vic2NyaWJlIGUtbWFpbC4uDQoNCg== ------=_NextPart_000_0005_01C0FF39.20643230 Content-Type: text/html; charset="ks_c_5601-1987" Content-Transfer-Encoding: base64 PCFET0NUWVBFIEhUTUwgUFVCTElDICItLy9XM0MvL0RURCBIVE1MIDQuMCBUcmFuc2l0aW9uYWwv L0VOIj4NCjxIVE1MPjxIRUFEPg0KPE1FVEEgY29udGVudD0idGV4dC9odG1sOyBjaGFyc2V0PWtz X2NfNTYwMS0xOTg3IiBodHRwLWVxdWl2PUNvbnRlbnQtVHlwZT4NCjxNRVRBIGNvbnRlbnQ9Ik1T SFRNTCA1LjAwLjMzMTUuMjg3MCIgbmFtZT1HRU5FUkFUT1I+DQo8U1RZTEU+PC9TVFlMRT4NCjwv SEVBRD4NCjxCT0RZIGJnQ29sb3I9I2ZmZmZmZj4NCjxESVY+PEZPTlQgc2l6ZT0yPkknZCBsaWtl IHRvIHN1YnNjcmliZSBlLW1haWwuLjwvRk9OVD48L0RJVj4NCjxESVY+Jm5ic3A7PC9ESVY+PC9C T0RZPjwvSFRNTD4NCg== ------=_NextPart_000_0005_01C0FF39.20643230-- From goldfarb at unb.ca Thu Jun 28 01:41:22 2001 From: goldfarb at unb.ca (Lev Goldfarb) Date: Thu, 28 Jun 2001 02:41:22 -0300 (ADT) Subject: What is a structural represetation? Message-ID: Dear colleagues, The following paper, titled "What is a structural representation?", ( http://www.cs.unb.ca/profs/goldfarb/struct.ps ) which we believe to be, in a sense, the first one formally addressing the issue of structural representation and proposing the formal ETS model, should be of interest to many researchers in various areas. It implies, in particular, that the properly understood (non-trivial) "structural" representations cannot be "replaced" by the classical numeric, e.g. vector-space-based, representations. Moreover, the concept of "structural" representation emerging from the ETS model is not the one familiar to all of you. (The abstract of the paper is appended below; for a change, the default paper size is A4. Unfortunately for some, the language of the paper is of necessity quite formal, since the main concepts do not have any analogues and therefore must be treated carefully.) Although the proposed model was motivated by, and will be applied to, the "real" problems coming from such areas as pattern recognition, machine learning, data mining, cheminformatics, bioinformatics, and many others, in view of the required radical rethinking that must now go into its implementations, at this time, we can only offer a very preliminary discussion, in the following companion paper, addressing the model's potential applications in chemistry http://www.cs.unb.ca/profs/goldfarb/cadd.ps (please keep in mind that the last paper was written on the basis of an earlier draft of the paper we are announcing now and it will be updated accordingly next month). We intend to discuss the paper shortly on INDUCTIVE mailing list. (To subscribe, send to INDUCTIVE-SERVER at UNB.CA the following text SUBSCRIBE INDUCTIVE FIRSTNAME LASTNAME) We would greatly appreciate any comments regarding both of the above papers. Best regards, Lev Goldfarb Tel: 506-458-7271 Faculty of Computer Science Tel(secret.): 453-4566 University of New Brunswick Fax: 506-453-3566 P.O. Box 4400 E-mail: goldfarb at unb.ca Fredericton, N.B., E3B 5A3 Home tel: 506-455-4323 Canada http://www.cs.unb.ca/profs/goldfarb/goldfarb.htm ***************************************************************************** WHAT IS A STRUCTURAL REPRESENTATION? Lev Goldfarb, Oleg Golubitsky, Dmitry Korkin Faculty of Computer Science University of New Brunswick Fredericton, NB, Canada We outline a formal foundation for a "structural" (or "symbolic") object/event representation, the necessity of which is acutely felt in all sciences, including mathematics and computer science. The proposed foundation incorporates two hypotheses: 1) the object's formative history must be an integral part of the object representation and 2) the process of object construction is irreversible, i.e. the "trajectory" of the object's formative evolution does not intersect itself. The last hypothesis is equivalent to the generalized axiom of (structural) induction. Some of the main difficulties associated with the transition from the classical numeric to the structural representations appear to be related precisely to the development of a formal framework satisfying these two hypotheses. The concept of (inductive) class--which has inspired the development of this approach to structural representation--differs fundamentally from the known concepts of class. In the proposed, evolving transformations system (ETS), model, the class is defined by the transformation system---a finite set of weighted transformations acting on the class progenitor--and the generation of the class elements is associated with the corresponding generative process which also induces the class typicality measure. Moreover, in the ETS model, a fundamental role of the object's class in the object's representation is clarified: the representation of an object must include the class. From the point of view of ETS model, the classical discrete representations, e.g. strings and graphs, appear now as incomplete special cases, the proper completion of which should incorporate the corresponding formative histories, i.e. those of the corresponding strings or graphs. From rijn at swi.psy.uva.nl Fri Jun 1 08:32:39 2001 From: rijn at swi.psy.uva.nl (rijn at swi.psy.uva.nl) Date: Fri, 1 Jun 2001 14:32:39 +0200 (CEST) Subject: Workshop Computational Model of Memory, Amsterdam, The Netherlands Message-ID: This is an announcement for the 'Computational Models of Memory' workshop, which will be held August 31-September 2, 2001, in Amsterdam, The Netherlands. Our objective is to provide the necessary conditions for an inspiring workshop by bringing together internationally renowned experts in the field of computational modeling. We would like to stress that this workshop will not be about one particular kind of model, nor about one particular kind of memory task. Instead we aim at bringing together experts from a variety of backgrounds that share a common interest in memory and computational modelling. Topics that will be covered in the workshop are implicit memory, explicit memory, amnesia, word recognition and reaction time models. A number of well-known modelers and memory researchers will participate. Speakers include John Anderson, Erik Altmann, Art Jacobs, Mike Masson, Dennis Norris, Randy O'Reilly, Roger Ratcliff, Trish Van Zandt and Richard Shiffrin. For additional information and registration visit: http://www.swi.psy.uva.nl/compumem/ As the number of participants is limited, registration will be on a first-come, first-served basis.