How words can guide our eyes
Bibliographic record
Abstract
Abstract Pursuing cognitively stimulating activities, such as engaging with art, is crucial to a healthy lifestyle. The current work simulates visits to an art museum in a laboratory setting. Using eye tracking, we explored how linguistically guided visual search may increase attention, enjoyment and retention of information when viewing art. Two groups of adults, young (under 35 years) and older (over 65 years) viewed ten paintings on a computer screen presented either with or without an accompanying audio-guide, while having their eye movements recorded. Audio-guides referred to specific areas of the painting, marked as Interest Areas (IA). Across age groups, as attested by gaze fixations, the audio-guides increased attention to these areas compared to free-viewing. Audio-guided viewing did not lead to a significantly increase over free-viewing in information recall accuracy or feelings of enjoyment and engagement. Overall, older adults did report feeling more positively about both audio-guided and free viewing than young adults. Thus, the use of audio-guides, specifically the gamification through linguistically guided visual search, may be a useful tool to promote meaningful attentional interactions with art.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".