Bibliographic record
Abstract
In the past few years, there has been an explosion of eye movement research in cognitive science and neuroscience. This has been due to the availability of 'off the shelf' eye trackers, along with software to allow the easy acquisition and analysis of eye movement data. Accompanying this has been a realisation that eye movement data can be informative about many different aspects of perceptual and cognitive processing. Eye movements have been used to examine the visual and cognitive processes underpinning a much broader range of human activities, including, language production, dialogue, human computer interaction, driving behaviour, sporting performance, and emotional states. Finally, in the past thirty years, there have been real advances in our understanding of the neural processes that underpin eye movement behaviour. The Oxford Handbook of Eye Movements provides the first comprehensive review of the entire field of eye movement research. In over fifty chapters, it reviews the developments that have so far taken place, the areas actively being researched, and looks at how the field is likely to devlop in the coming years. The first section considers historical and background material, before moving onto section 2 on the neural basis of eye movements. The third and fourth sections looks at visual cognition and eye movements and eye movement pathology and development. The final sections consider eye movements and reading and language processing and eye movements. Bringing together cutting edge research from and international team of leading psychologists, neuroscientists, and vision researchers, this book is the definitive reference work in this field. Features The first comprehensive review of eye movement research, providing a much needed review of the field Brings together leading researchers from across the world, resulting in an authoritative and state of the art review of a rapidly expanding
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.030 |
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".