ELIA: A software application for integrating spoken language and eye movements
Bibliographic record
Abstract
We have developed a new software application, Eye-gaze Language Integration Analysis (ELIA), which allows for the rapid integration of gaze data with spoken language input (either live or prerecorded). Specifically, ELIA integrates E-Prime output and/or .csv files that include eye-gaze and real-time language information. The process of combining eye movements with real-time speech often involves multiple error-prone steps (e.g., cleaning, transposing, graphing) before a simple time course analysis plot can be viewed or before data can be imported into a statistical package. Some of the advantages of this freely available software include (1) reducing the amount of time spent preparing raw eye-tracking data for analysis; (2) allowing for the quick analysis of pilot data in order to identify issues with experimental design; (3) facilitating the separation of trial types, which allows for the examination of supplementary effects (e.g., order or gender effects); and (4) producing standard output files (i.e., .csv files) that can be read by numerous spreadsheet packages and transferred to any statistical software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.011 |
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".