Erasing and blurring memories: The differential impact of interference on separate aspects of forgetting.
Bibliographic record
Abstract
states that as similarity between interfering information and memory contents increases, so too does the degree of impairment. However, information is lost from memory in different ways. For instance, studied content might be erased in an all-or-nothing manner. Alternatively, information may be retained but the precision might be degraded or blurred. Here, we asked whether the similarity of interfering information to memory contents might differentially impact these 2 aspects of forgetting. Observers studied colored images of real-world objects, each followed by a stream of interfering objects. Across 4 experiments, we manipulated the similarity between the studied object and the interfering objects in circular color space. After interference, memory for object color was tested continuously on a color wheel, which in combination with mixture modeling, allowed for estimation of how erasing and blurring differentially contribute to forgetting. In contrast to the similarity assumption, we show that highly dissimilar interfering items caused the greatest increase in random guess responses, suggesting a greater frequency of memory erasure (Experiments 1-3). Moreover, we found that observers were generally able to resist interference from highly similar items, perhaps through surround suppression (Experiments 1 and 4). Finally, we report that interference from items of intermediate similarity tended to blur or decrease memory precision (Experiments 3 and 4). These results reveal that the nature of visual similarity can differentially alter how information is lost from memory. (PsycINFO Database Record
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".