Up-regulatory nature of voluntary control for visual long-term memory encoding and its down-regulatory side effects
Bibliographic record
Abstract
The capacity for visual long-term memory (VLTM) to store detailed and accurate representations of images is quite remarkable (e.g., Brady et al., 2008). However, the nature and the extent of voluntary control over the quality of VLTM encoding is unclear. To test this, we sequentially presented a pair of random objects for participants to remember. Critically in some trials, one of the paired objects was cued to be up-regulated (i.e., try harder to remember) or to be down-regulated (i.e., try not to remember) either before (Experiment 1; pre-cue) or after (Experiment 2; post-cue) the onset of the objects. Here we found that participants successfully up-regulated the quality of memory encoding regardless of the cue-to-object stimulus onset asynchrony (SOA). However, they failed to down-regulate the quality of memory encoding regardless of cue-to-object SOA. Interestingly, we also observed reliable down-regulation of memory encoding quality for objects that accompanied up-regulated objects, only when the cue was provided prior to the onset of the objects. To further investigate the underlying neural mechanisms of voluntary control of memory encoding, we repeated the pre-cue experiment while measuring participants' electroencephalograms (EEG). Our results showed that participants reduced the posterior alpha (8-14Hz) power contralateral to the up-regulation cue following the cue onset, thus reflecting the exertion of selective attention to the to-be-up-regulated object. Taken together, these results suggest that our ability to voluntarily control the memory encoding quality is up-regulatory in nature, but it can be used to indirectly down-regulate the memory encoding quality of accompanying stimuli only if the cue is provided before the stimuli onset in order to attentionaly bias the perceptual encoding of the stimulus. Meeting abstract presented at VSS 2018
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".