Judgments during perceptual comparisons predict distinct forms of memory updating.
Bibliographic record
Abstract
Comparing a visual memory with new visual stimuli can bias memory content, especially when the new stimuli are perceived as similar. Perceptual comparisons of this kind may play a mechanistic role in memory updating and can explain how memories can become erroneous in daily life. To test this possibility, we investigated whether comparisons can produce other types of memory distortion beyond memory bias that are commonly implicated in erroneous memories (e.g., memory misattribution). We hypothesized that the type of memory distortion induced during a comparison depends on the perceived overlap between the memory and incoming stimulus-when the input is perceived as similar, it biases memory content; when perceived as the same, it replaces memory content. Participants completed a delayed estimation task in which they compared their memories of color (Experiment 1) and shape stimuli (Experiment 2) to probe stimuli before reporting memory content. We found systematic errors in participants' memory reports following perceived similarity and sameness that were toward the probes and larger following perceived sameness. Simulations confirmed that these errors were not explained by noisy encoding processes that occurred before comparisons. Instead, computational modeling suggested that these errors were likely explained by the probabilistic replacement of the memory by the probe following perceived sameness and integration between the memory and the probe following perceived similarity. Together, these findings suggest that perceptual comparisons can prompt distinct forms of memory updating that have been described previously and may explain how memories become erroneous during their use in everyday behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.003 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".