Wearable computing: Will it make people prosocial?
Bibliographic record
Abstract
We recently reported that people who wear an eye tracker modify their natural looking behaviour in a prosocial manner. This change in looking behaviour represents a potential concern for researchers who wish to use eye trackers to understand the functioning of human attention. On the other hand, it may offer a real boon to manufacturers and consumers of wearable computing (e.g., Google Glass), for if wearable computing causes people to behave in a prosocial manner, then the public's fear that people with wearable computing will invade their privacy is unfounded. Critically, both of these divergent implications are grounded on the assumption that the prosocial behavioural effect of wearing an eye tracker is sustained for a prolonged period of time. Our study reveals that on the very first wearing of an eye tracker, and in less than 10 min, the prosocial effect of an eye tracker is abolished, but by drawing attention back to the eye tracker, the implied presence effect is easily reactivated. This suggests that eye trackers induce a transient social presence effect, which is rendered dormant when attention is shifted away from the source of implied presence. This is good news for researchers who use eye trackers to measure attention and behaviour; and could be bad news for advocates of wearable computing in everyday life.
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 | 0.002 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".