Dissociating the Impact of Memorability on Electrophysiological Correlates of Memory Encoding Success
Bibliographic record
Abstract
Despite its unlimited capacity, not all visual information we encounter is encoded into visual long-term memory. Traditionally, variability in encoding success has been ascribed to variability in the types and efficacy of an individual's cognitive processes during encoding. Accordingly, past studies have identified several neural correlates of variability in encoding success, namely, frontal positivity, occipital alpha amplitude, and frontal theta amplitude, by contrasting the electrophysiological signals recorded during successful and failed encoding processes (i.e., subsequent memory). However, recent research demonstrated individuals remember and forget consistent sets of stimuli, thereby elucidating stimulus-intrinsic factors (i.e., memorability) that determine the ease of memory encoding independent of individual-specific variability in encoding processes. The existence of memorability raises the possibility that canonical EEG correlates of subsequent memory may reflect variability in stimulus-intrinsic factors rather than individual-specific encoding processes. To test this, we recorded the EEG correlates of subsequent memory while participants encoded 600 images of real-world objects and assessed the unique contribution of individual-specific and stimulus-intrinsic factors on each EEG correlate. Here, we found that frontal theta amplitude and occipital alpha amplitude were only influenced by individual-specific encoding success, whereas frontal positivity was influenced by stimulus-intrinsic and individual-specific encoding success. Overall, our results offer novel interpretations of canonical EEG correlates of subsequent memory by demonstrating a dissociable impact of stimulus-intrinsic and individual-specific factors of memory encoding success.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".