Visual memories can recover from recognition-induced memory biases
Bibliographic record
Abstract
How do we retain accurate visual memories over a long time? Studies have demonstrated that successfully retrieving a memory increases the likelihood that it can be retrieved later. However, other studies shown that information provided during retrieval can alter how the original memory is reported (i.e., misinformation effect). These seemingly-contradictory findings suggest that retrieval calls the memory into a malleable state where it is augmented or modified by information available at that time. To test this, we first had participants encode 240 pictures of colored real objects. Then, memory for those objects was tested in two types of retrieval tasks (i.e., the recognition bias task and the baseline recall task) on the same day and the day after. In the recognition bias task, participants were first presented with a grayscale object image and indicated whether or not they remembered encoding its colored version. Subsequently, participants completed a two-second-long recognition practice in which they saw two colored versions of the same object and identified the one more similar to the encoded version. Participants then recalled the encoded object’s color. The baseline recall task was identical, except for the recognition practice, which was replaced by a two-second blank retention interval. We found that irrespective of retrieval type, memories retrieved on Day 1 were more likely to be retrieved on Day 2 than memories not tested on Day 1. Additionally, recall following recognition practice was biased towards the probe judged to be more similar to the encoded object. Interestingly, however, this recognition-induced memory bias was transient and did not influence memory recall on Day 2. Taken together, these data support our hypothesis that retrieval brings visual memories into a malleable state to be augmented or altered by memory-relevant information. Fortunately, recognition-induced memory biases may not permanently change the encoded representation.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".