All Eyes on the Animals: Animacy Guides Visual Attention
Bibliographic record
Abstract
The ability to detect moving entities within our visual field is essential for human survival, as these entities often signal potential threats or opportunities. Animate beings, such as animals, exhibit dynamic and unpredictable movement patterns, making them highly salient to our visual attention system. This study seeks to explore the extent to which animacy influences saccadic eye movements using a forced-choice saccade task. In Experiment 1, an animal image and a real-world size-matched inanimate object image both from the THINGSPlus Database, grey-scaled and backgrounds removed, were presented simultaneously on a monitor. The participants completed a series of trials organized into four blocks, where they were instructed to focus their gaze, as quickly as possible, on the animate image during two blocks and on the inanimate object image during the other two blocks. Experiment 2 presented a single image at a time, either an animal or an object, with the same block trial structure. This allowed for a more precise comparison of reaction times between the two categories of objects without the potential decision-making implicated in simultaneous stimuli presentation. In both experiments, participants’ saccadic eye movements were recorded with an eye tracker. The results show that participants exhibited shorter saccadic reaction times when looking toward animate objects than their inanimate counterparts. Eye movement errors and amplitudes did not differ between the two types of stimuli. Overall, this indicates that our visual system is tuned to prioritize and quickly respond to potential living threats or opportunities in our environment, underscoring the importance of animacy in our attentional processes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".