Automated object detection in mobile eye-tracking research: comparing manual coding with tag detection, shape detection, matching, and machine learning
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
The goal of the current study is to compare different methods for automated object detection (i.e. tag detection, shape detection, matching, and machine learning) with manual coding on different types of objects (i.e. static, dynamic, and dynamic with human interaction) and describe the advantages and limitations of each method. We tested the methods in an experiment that utilizes mobile eye tracking because of the importance of attention in communication science and the challenges posed by this type of data when analyzing different objects because visual parameters are consistently changing within and between participants. Machine learning was found to be the most reliable method to detect all types of objects and was slightly more conservative compared to manual coding. Feature-based matching worked well for static objects. We discuss the advantages and challenges of each method along with key considerations for researchers depending on their research objective, the type of object, and the object detection method they will use.
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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