Interaction Design for Complex Cognitive Activities with Visual Representations: A Pattern-Based Approach
Bibliographic record
Abstract
This paper is concerned with interaction design for visualization-based computational tools that support the performance of complex cognitive activities, such as analytical reasoning, sense making, decision making, problem solving, learning, planning, and knowledge discovery. In this paper, a number of foundational concepts related to interaction and complex cognitive activities are syncretized into a coherent theoretical framework. This framework is general, in the sense that it is applicable to all technologies, platforms, tools, users, activities, and visual representations. Included in the framework is a catalog of 32 fundamental epistemic action patterns, with each action pattern being characterized and examined in terms of its utility in supporting different complex cognitive activities. This catalog of action patterns is comprehensive, covering a broad range of interactions that are performed by a diverse group of users for all kinds of tasks and activities. The presented framework is also generative, in that it can stimulate creativity and innovation in research and design for a number of domains and disciplines, including data and information visualization, visual analytics, digital libraries, health informatics, learning sciences and technologies, personal information management, decision support, information systems, and knowledge management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".