The efficacy of retroactive control of visual memory encoding depends on preceding oscillatory activities.
Bibliographic record
Abstract
Despite the virtually unlimited capacity of visual long-term memory, our ability to encode new visual information into this offline storage fluctuates from moment to moment, thus rendering some information forgotten later in time. Is there any way to monitor this moment-to-moment fluctuation and correct them when needed in real time? Previously we have demonstrated that we can monitor this fluctuation from multiple electroencephalograms (EEG) measures of memory encoding in real time and intervene failed memory encoding by providing re-encoding opportunities for stimuli that were predicted to be poorly encoded by the EEG measures (Fukuda & Woodman, 2015). However, it is unclear whether we can voluntarily change the fate of failed memory encoding without providing re-encoding opportunities. To test this, we recorded participants' EEG while they were sequentially presented with 600 pictures of random objects to remember. Importantly in a third of the trials, a post-stimulus cue was presented to prompt participants to try harder to encode the preceding stimulus. Our results replicated our previous findings that power of occipital alpha (8-14Hz) and frontal theta (5-7Hz) activities during encoding predicted the quality of memory encoding. More importantly, participants successfully upregulated the memory encoding for cued items even when the EEG indices measured prior to the cue onset predicted failed encoding (i.e., strong occipital alpha power and weak frontal theta power). Interestingly, the magnitude of post-stimulus cueing benefit depended on the frontal theta power but not on the occipital alpha power measured prior to the cue onset. More specifically, weak frontal theta power induced larger cueing benefit than strong frontal theta power, but the alpha power did not differentiate the magnitude of cueing benefit. These results not only demonstrate another approach to intervene our failed memory encoding but also suggest dissociative cognitive mechanisms underlying the two EEG indices of memory encoding. 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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".