Trial-by-trial fluctuations in post-stimulus attention during memory encoding predict subsequent associative context memory performance
Bibliographic record
Abstract
Abstract Episodic memory formation rate varies over time partly due to fluctuations in attentional state during memory encoding. Emerging evidence suggests that fluctuations in pre- and/or post-stimulus attention during encoding impact subsequent memory performance. It remains unclear how these fluctuations may differentially impact the subsequent retrieval of items alone, compared to items + their contextual details (associative context memory). In this study, we explored this in 30 healthy younger adults (21-34 years old). We developed the Montreal Attention at Encoding (MAET) task where on each encoding trial, participants responded as quickly as possible to a central fixation cross that expanded in size after a random duration. They then had to encode a picture of an object and its spatial location. Memory for the object-location associations was tested during retrieval. Response time (RT) to the fixation cross presented prior to each object gauged pre-stimulus attention levels on a trial-by-trial basis, while RT to the fixation cross that ensued each object indexed post-stimulus attention levels. Within-subject logistic regressions were used to predict context and item memory performance from pre- and post-stimulus RTs. Results revealed that encoding pre-stimulus attentional levels did not differentially predict context vs. item memory. However, post-stimulus RTs did predict subsequent context retrieval such that, longer post-stimulus RT to the fixation was related to poorer subsequent context retrieval. This study introduces a novel paradigm for investigating the impact of attentional state at encoding on subsequent memory performance and indicate a link between post-stimulus delays in attention-related RT and associative 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 | 0.001 | 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) | 0.002 | 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".