Cognition, situatedness, and situated design

In the rationalistic perspective, the human expert is seen as a data processing system having properties similar to computers. As a consequence, the design of man machine interfaces, workplaces, and organizational procedures has been mainly driven by technological advances, focusing on replacing humans rather than supporting their actual needs. A more appropriate explanation of human cognition is based on the notion of situatedness: human cognition is considered to be emergent from the interaction of the human with the environment, i.e., the current situation the human is involved in. More generally, the system environment coupling is a prerequisite of cognition and cannot be abstracted away. We summarize the rationalistic perspective, its pitfalls, and its (undesirable) influences on design. As an alternative, we propose "situated design", a design methodology capitalizing on the notion of the human as a situated agent. We demonstrate how "situated design" can be applied to workplace design and computer system design, and we outline a situated perspective on man machine interface design supporting humans in coping with the so called "information overload" phenomenon.

[1]  Louise T. Su.  The Relevance of Recall and Precision in User Evaluation , 1994, J. Am. Soc. Inf. Sci..

[2]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[3]  Robert C. Holte,et al.  Inferring What a User Is Not Interested in , 1996, AI.

[4]  Terry Winograd,et al.  Understanding computers and cognition - a new foundation for design , 1987 .

[5]  Filippo Menczer,et al.  Artificial Life Applied to Adaptive Information Agents , 1995 .

[6]  Susan T. Dumais,et al.  Personalized information delivery: an analysis of information filtering methods , 1992, CACM.

[7]  Andrew Jennings,et al.  A Personal News Service Based on a User Model Neural Network , 1992 .

[8]  Kenrick J. Mock.  Hybrid Hill-Climbing and Knowledge-Based Methods for Intelligent News Filtering , 1996, AAAI/IAAI, Vol. 1.

[9]  Gary Marchionini,et al.  A Conceptual Framework for Text Filtering , 1996 .

[10]  Pattie Maes,et al.  Evolving agents for personalized information filtering , 1993, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications.

[11]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[12]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[13]  Z. Pylyshyn.  Robot's Dilemma: The Frame Problem in Artificial Intelligence , 1987 .

[14]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[15]  Robert B. Allen,et al.  User Models: Theory, Method, and Practice , 1990, Int. J. Man Mach. Stud..

[16]  Shan-Ju L. Chang,et al.  Browsing: a multidimensional framework , 1993 .

[17]  Lucy Suchman.  Plans and situated actions: the problem of human-machine communication , 1987 .

[18]  Carol L. Barry.  User-defined relevance criteria: an exploratory study , 1994 .

[19]  Timothy D. Wilson,et al.  Telling more than we can know: Verbal reports on mental processes. , 1977 .

[20]  Stephen W. Smoliar,et al.  The Robot's dilemma: The frame problem in artificial intelligence: Zenon W. Pylyshyn (Ed.), (Ablex, Norwood, NJ, 1987); xi + 156 pages, $29.50 , 1988 .

[21]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[22]  Yoichi Shinoda,et al.  Information filtering based on user behavior analysis and best match text retrieval , 1994, SIGIR '94.

[23]  Bradley N. Miller,et al.  Applying Collaborative Filtering to Usenet News , 1997 .

[24]  Thomas J. Froehlich,et al.  Relevance reconsidered—towards an agenda for the 21st century: introduction to special topic issue on relevance research , 1994 .

[25]  P. Agre.  Lucy A. Suchman, Plans and Situated Actions: The Problem of Human-Machine Commuinication (Cambridge University Press, Cambridge 1987) , 1990, Artif. Intell..

[26]  Stevan Harnad,et al.  Symbol grounding problem , 1990, Scholarpedia.

[27]  Frank Curtis Stevens,et al.  Knowledge-based assistance for accessing large, poorly structured information spaces , 1993 .

[28]  Rolf Pfeifer,et al.  Developing effective computer systems supporting knowledge-intensive work: situated design in a large paper mill , 1997 .

[29]  Richard Zeckhauser,et al.  Recommender systems for evaluating computer messages , 1997, CACM.

[30]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

[31]  Rolf Pfeifer,et al.  Situated Adaptive Design: Toward a New Methodology for Knowledge Systems Development , 1991, Wissensbasierte Systeme.

[32]  J. Kenrick,et al.  Hybrid HillClimbing and Knowledge-Based Techniques for Intelligent News Filtering , 1996 .

[33]  A. Meystel.  Architectures for intelligent control systems: The science of autonomous intelligence , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[34]  Gary Marchionini,et al.  Information Seeking in Electronic Environments , 1995 .

[35]  Susan T. Dumais,et al.  The vocabulary problem in human-system communication , 1987, CACM.

[36]  Nicholas J. Belkin,et al.  Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.