Cognitive Science in the Design of Graphical Images and Interfaces
Bibliographic record
Abstract
Cognitive Science in the Design of Graphical Images and Interfaces Brian Fisher (bfisher@sfu.ca) Interactive Arts & Technology, Simon Fraser University 250-13450 102 Ave., Surrey BC V3T 0A3 W. Bradford Paley (brad@didi.com) Computer Science, Columbia University and Information Esthetics 170 Claremont Avenue, Suite 6, New York, NY 10027 Zenon Pylyshyn (zenon@ruccs.rutgers.edu) Centre for Cognitive Science, Rutgers University 152 Frelinghuysen Road Piscataway, NJ 08854-8020 Ronald A. Rensink (rensink@cs.ubc.ca) Psychology & Computer Science, University of British Columbia 2136 West Mall, Vancouver, B.C. Canada, V6T 1Z4 Barbara Tversky (bt@psych.stanford.edu) Psychology, Stanford University Jordan Hall, Bldg. 420, 450 Serra Mall, Stanford, CA 94305 Keywords: visual analytics; graphical communication; spatial structure; spatial cognition; psycholinguistics Introduction Innovations in information and communication technology enable us to collect, process, and graphically portray novel conceptual diagrams or immense quantities of data. These data can potentially inform learning and decision-making in areas as diverse as science and medicine, design and manufacturing, and law enforcement and disaster relief. To do so will require us to learn how to make information easily accessible and understandable. Applying research in human perception, spatial cognition, and communication to the design of visualization environments. Working with skilled designers to elicit design knowledge that may be applied in the design of visualization environments. Analyzing the perceptual and cognitive processes that occur in human interaction with graphical information. The talks will examine the application of perceptual and cognitive science to the design of graphical representations and interactive visual interfaces. They will also explore ways in which new research questions and methods emerge from visualization tasks and problems, as well as the potential for emergence of a cognitive science of visual analytics. The speakers include familiar cognitive science researchers and their collaborators in graphical and interaction design. Discussion will focus on research problems and approaches that combine cognitive science and visual representation. Format will include 15-20 minute talks from three participants followed by a panel discussion with substantial input from workshop attendees. The information visualization approach to this problem relies on graphical representations of information that are generated by computers on request. Currently, these representations compare unfavorably to those produced by skilled graphical designers who undergo extensive training to master the ability to generate effective visual representations. Visual analytics takes a cognitive approach to the design of the interactive visual interface. It is informed by graphical design and the perceptual and cognitive sciences. Its goal is to produce computer-generated graphical representations of complex datasets that support users’ innate “visual intelligence” to help them to understand the situations those data represent. Topics Computer graphics and perception: Parsing complex graphical scenes, role of attention and spatial indexing, change blindness in dynamic display environments. Psychophysical and cognitive testing. Links to traditional human-computer interaction approaches. Perception and action in large screen and stereo (3D) displays. This symposium will explore the interaction between cognitive science and the design of graphics and interactive visualization systems. This interaction can take multiple forms:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".