The effect of camera presence on arousal, attentional control and inhibition
Bibliographic record
Abstract
Previous research has shown that the presence of a camera can have various profound effects on human behavior, from increasing privacy-related behaviors (Caine, K., Sabanovic, S., & Carter, M., 2012) and pro-social behaviours (van Rompay, T. J., Vonk, D. J., & Fransen, M. L., 2009) to reducing scores on a memory test (Constantinou, M., Ashendorf, L., & McCaffrey, R. J., 2005). In each of these examples, the camera acted as an implied social presence, producing results similar to human observation. More recently, these findings have been extended to the field of visual attention. Risko and Kingstone (2011) showed that the presence of an eye-tracker leads to changes in looking behaviour. However, looking behaviours are only one measure of attention, and the field contains several other well-established behavioural paradigms. For this reason, we tested participants on a battery of tasks measuring various aspects of visual attention, either in the presence of a camera or not. The battery was comprised of a single- and dual-task version of an attentional capture task, cueing and gaze-cueing tasks, a visual search task, and the Sustained Attention to Response Task (SART). Across these four different visual attention tasks, any effect of camera presence served solely to reduce the response latency of the participants rather than impact the automatic or controlled allocation of visuospatial attention. The single exception to this pattern of results was in the inhibitory control of the automatic capture of attention by a distractor under a condition of high (dual-task) load. These results suggest that the presence of a camera may operate by increasing arousal, thereby reducing reaction time. However, when combined with a cognitive load manipulation, the capacity of attentional inhibition is reduced and so capture by distractors becomes pronounced. Meeting abstract presented at VSS 2014
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".