Maximizing Recall of Gruesome Images for Health Effects Advertising: An Experimental Investigation
Bibliographic record
Abstract
The objective of this case study was to experimentally manipulate the impact on arousal and recall of two characteristics frequently occurring in gruesome depictions of body parts in smoking cessation advertisements: the presence or absence of an external physical insult to the body part depicted; whether or not the image contains a clear figure/ground demarcation. Three hundred participants (46% male, 54% female; mean age 27.3 years, SD = 11.4) participated in a two-stage online study wherein they viewed and responded to a series of gruesome 4-s video images. Seventy-two video clips were created to provide a sample of images across the two conditions: physical insult versus no insult and clear figure/ground demarcation versus merged or no clear figure/ground demarcation. In stage one, participants viewed a randomly ordered series of 36 video clips and rated how “confronting” they considered each to be. Seven days later (stage two), to test recall of each video image, participants viewed all 72 clips and were asked to identify those they had seen previously. Images containing a physical insult were consistently rated more confronting and were remembered more accurately than images with no physical insult. Images with a clear figure/ground demarcation were rated as no more confronting but were consistently recalled with greater accuracy than those with unclear figure/ground demarcation. Makers of gruesome health warning television advertisements should incorporate some form of physical insult and use a clear figure/ground demarcation to maximize image recall and subsequent potential advertising effectiveness.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".