Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it