Cognitive Science in the Design of Graphical Images and Interfaces
Notice bibliographique
Résumé
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:
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,005 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».