Can Repeating a Brand Claim Lead to Memory Confusion? The Effects of Claim Similarity and Concurrent Repetition
Bibliographic record
Abstract
Repetition of brand claims is frequently used to promote the learning of brand-related information. Using dual component models of recognition memory, the author examines whether repetition, in the face of repetitions by similar competitors, might paradoxically increase memory confusion. In Experiment 1, the repetition of similar claims of equally familiar competitor brands produced two opposing effects: It increased memory for accurate claim recognition but also elevated brand claim confusion among advertised competitors. The pattern of results was similar when memory was tested a week after the initial exposure. In Experiment 2, in which participants were required to engage in a task designed to promote the “binding” between a brand and its claim, the memory confusion effects of repetition were significantly reduced. Finally, Experiment 3 replicated and generalized these findings by using more realistic stimuli and procedures. Thus, across three studies, the evidence strongly suggests that the confusion-elevating effects of repetition are a result of weak binding between memory for brand and 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".