Posterior ERP tracks evidence accumulation for memory-based decisions.
Bibliographic record
Abstract
Recollection is a retrieval of episodic memory accompanied by high subjective confidence. Given their subjective vividness, recollection-based memories are the building blocks of autobiographical memories and what define who we are. Past studies have proposed the late posterior positivity (or LPP) as an electrophysiological marker of recollection-based memory retrieval. Recent findings in perceptual decision-making research, on the other hand, suggest that the LPP might instead track the accumulation of decision evidence to determine the presence or absence of episodic memory. To adjudicate between the two hypotheses, we amassed two EEG datasets (n = 59 and 45) in which human participants performed a standard recognition memory task. Here we show that while the LPP amplitude is uniquely higher when participants endorse the presence of episodic memory with high confidence, its amplitude universally increases until any recognition decision is made, even when participants endorse the absence of any episodic memory. Thus, our results question the process purity of the LPP as the electrophysiological marker of recollection-based memory retrieval and elucidate its additional role as a marker of evidence accumulation for mnemonic decisions.
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".