Indirect, but not direct, down-regulation of visual long-term memory encoding through strategic biasing of attentional allocation.
Bibliographic record
Abstract
Visual long-term memory allows us to store a virtually infinite amount of visual information (Brady, Konkle, Alvarez, & Oliva, 2008; Standing, 1973). Despite its massive storage capacity, our ability to encode visual long-term memory fluctuates from moment-to-moment, and for that reason, not every piece of visual information that we wish to encode gets stored into our visual long-term memory. At the same time, we occasionally encounter visual information that we do not wish to remember. To what extent can we control our memory encoding ability at will? Here, we showed that although there are multiple mechanisms to directly up-regulate memory encoding, it is more difficult, if not impossible, to down-regulate memory encoding directly. However, we are capable of down-regulating memory encoding indirectly by biasing attentional allocation away from the encoding of an unwanted stimulus and toward the encoding of a simultaneously encoded item. However, this strategy is effective only if it is exerted prior to perceptual encoding of the unwanted stimulus. Thus, our findings not only support the existence of the biased competition mechanism of voluntary control of memory encoding but also reveals its critical period in indirectly down-regulating memory encoding of unwanted information. (PsycInfo Database Record (c) 2020 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".