When unpredictable does not mean difficult to process
Bibliographic record
Abstract
During language comprehension, words that are less expected tend to take more effort. This phenomenon has been described by the hypothesis that cognitive cost scales in surprisal (negative log probability; Hale, 2001; Levy, 2008), with a core justification being that surprisal quantifies the amount by which a rational comprehender's beliefs about meaning change upon encountering a word. However, this focus on next-word prediction may be too narrow. In this work we advocate measuring processing cost directly with the size of the change in beliefs about meaning, a reframing which implies a novel class of potential situations where surprisal may systematically overestimate cost. We identify typographical errors as a test case, and implement estimators of surprisal and belief-update in a noisy-channel model of comprehension as inference about intended strings. In a self-paced reading time study, we present evidence that human reading time behaves as predicted by belief-update size, rather than surprisal.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.027 |
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; both teacher heads agree on what is shown here.
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".