Can Repeating a Brand Claim Lead to Memory Confusion? The Effects of Claim Similarity and Concurrent Repetition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it