A dynamic neural field model of self-regulated eye movements during category learning.
Bibliographic record
Abstract
Computational models of category learning and attention have\nhistorically focused on capturing trial and experiment level\ninteractions between attention and decision. However,\nevidence has been accumulating that suggests that the\nmoment-to-moment attentional dynamics of an individual\naffects both their immediate decision-making processes as\nwell as their overall learning performance. To extend the\nscope of these formal theories requires a modeling approach\nthat can index fine-grained decision-making at millisecond\ntime scales. Here we implement a model of eye movements\nduring category learning using concepts from Dynamic\nNeural Field Theory research. Our model uses a combination\nof timing signals, spatial competition and Hebbian association\nto simultaneously account for a number of foundational\nattentional efficiency results from eye tracking and category\nlearning. We report the results of fitting this model to\naccuracy, fixation probabilities, fixation counts and fixation\nduration data in 42 subjects from a standard category learning\nexperiment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".