Bibliographic record
Abstract
Abstract Pupillometry is the scientific study of pupil size changes due to low-level factors like ambient lighting conditions and changes in focal distance, and, more interestingly, due to higher level brain activity and attentional, emotional, or cognitive demands. Because it offers a way to assess cognitive activity, pupillometry has attracted research gauging cognitive processing effort on and off since the 1960s. Due to developments in eye tracking technology and statistical analysis methods, pupillometry has enjoyed a renewed interest as a tool for language processing research since the 2010s. Language processing tasks using Task-Evoked Pupillary Response (TEPR)—measured changes in pupil dilation time-locked to the presentation of a stimulus—have shown that the human pupil responds to and can be used to replicate many of the standard findings in the psycholinguistic literature. For example, pupil size varies with lexical frequency during word processing tasks, showing more dilation for low than for high frequency words. Similarly, changes in pupil size correlate with differences in complexity, ambiguity, predictability, and ease of contextual integration during sentence processing. Perhaps most interestingly, pupillometry responds to processing demands induced by pragmatic inferences. Further, it is sensitive to several individual difference factors, for the investigation of which it offers a non-invasive, task-free method. This is without doubt one of the most attractive sides of pupillometry: it can be used to investigate many factors influencing spoken language processing effort, including pragmatics and socially situated language processes, and it can be used to do so without an explicit task and with natural, felicitous spoken stimuli. However, the literature reporting use of pupillometry in psycholinguistics is still very sparse, so more research is needed to find the areas where pupillometry is both reliable and useful, and where it independently benefits research in language sciences, not just replicates results and phenomena obtained from other, more established methods. Current developments in data preprocessing, statistical methods, and eye tracking technology carry some promise that this time, pupillometry might have come to stay as one more useful method in a psycholinguist’s toolbox.
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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 | 0.004 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| 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; 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".