Low‐Involvement Learning: Repetition and Coherence in Familiarity and Belief
Bibliographic record
Abstract
Over thirty years ago Krugman (1965) claimed that learning of advertising messages was much more like an Ebbinghaus nonsense syllable memory task than an exercise in rhetoric. If anything, he seems even more right today in a media environment that continues to become more cluttered. In this article, we investigate the role that memory plays in the development of beliefs within this context and focus on the formation of beliefs that develop with little intention or opportunity to learn. Following on previous work, we investigate the effect of repetition‐induced increases in belief for advertising claims that are hierarchically related: a superordinate general benefit claim (e.g., security of a lock) and multiple subordinate feature claims (e.g., pick resistant and professional installation required). We find that beliefs in feature claims increase monotonically with number of exposures, although at a diminishing marginal rate. We find no evidence of horizontal spillover of repetition‐induced increases in belief from one subordinate feature claim to another. However, we find a substantial amount of vertical spillover of repetition‐induced increases in belief from individual subordinate feature claims to the superordinate general benefit. A dual mediation analysis suggests that the vertical spillover comes from both an increase in familiarity of the general benefit and greater belief in the set of subordinate feature claims.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".