Designing Human-Computer Communication from Epistemic andCognitive First Principles
Bibliographic record
Abstract
How should we ensure effective communication between humans and digital computing systems? How might visualisations be designed to make transparent deep patterns in complex data? Or interfaces engineered so that users can directly and meaningfully interact with simulation models or computational processes? How could we use AI to adapt the form and sophistication of explanations to suit users with different amounts of target domain knowledge or ability in computational thinking? Current responses to these questions focus upon how information is structured and on the cognitive capabilities of humans. For example, recognition of the potential benefits of graphical interfaces and visualisations are now common place. And designers are increasingly aware of our perceptual, attentional and memory capabilities. This paper goes further by advocating that representations for communication should be designed using epistemic and cognitive first principles. Specifically, effective representations should be created that (a) directly encode the fundamental conceptual structure of their target domain and (b) are compatible with the sophisticated mental processes found in higher forms of cognition, such as problem solving and learning. I will present a selection of past work, from my research group, that takes this approach, including: discovery learning environments for science education; tools for complex problem solving; and visualisations for large quantitative datasets; and also a current project to improve communication between AI systems and humans.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".