Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics
Bibliographic record
Abstract
Eye-movements in reading exhibit frequency spillover effects: fixation durations on a word are affected by the frequency of the previous word.We explore the idea that this effect may be an emergent property of a computationally rational eyemovement strategy that is navigating a tradeoff between processing immediate perceptual input, and continued processing of past input based on memory.We present an adaptive eye-movement control model with a minimal capacity for such processing, based on a composition of thresholded sequential samplers that integrate information from noisy perception and noisy memory.The model is applied to the List Lexical Decision Task and shown to yield frequency spillover-a robust property of human eye-movements in this task, even with parafoveal masking.We show that spillover in the model emerges in approximately optimal control policies that sometimes process memory rather than perception.We compare this model with one that is able to give priority to perception over memory, and show that the perception-priority policies in such a model do not perform as well in a range of plausible noise settings.We explain how the frequency spillover arises from a counter-intuitive but fundamental property of sequenced thresholded samplers.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".