Understanding Micro Cognition using SGOMS/ACT-R predictions of a SGOMS based Mobile application
Bibliographic record
Abstract
This research continues to develop SGOMS, a general theory of how all experts organize routine expert task knowledge. Previous predictions of SGOMS/ACT-R motivated experimental and model design improvements to predict all expert players (expected of a general theory of expertise). In this study, the micro-strategies predictions of two models (Internal and External SGOMS/ACT-R models) are compared to the micro-strategies used by individual participants. A visual cue was added to the experimental game played in this study to encourage the same SGOMS game strategy. It was assumed that participants would use the color cue to help them follow the Internal model (a previously proposed model), but surprisingly, all participants better matched the External model. The external model assumes the visual system learns and handles higher gameplay knowledge. This provided evidence that high-level expert task information can and is more readily offloaded to the visual system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".