Memory‐guided perception is shaped by dynamic two‐stage theta‐ and alpha‐mediated retrieval
Bibliographic record
Abstract
How does memory influence auditory perception, and what are the underlying mechanisms that drive these interactions? Most empirical studies on the neural correlates of memory-guided perception have used static visual tasks, resulting in a bias in the literature that contrasts with recent research highlighting the dynamic nature of memory retrieval. Here, we used electroencephalography to track the retrieval of auditory associative memories in a cue-target paradigm. Participants (N = 64) listened to real-world soundscapes that were either predictive of an upcoming target tone or nonpredictive. Three key results emerged. First, targets were detected faster when embedded in predictive than in nonpredictive soundscapes (memory-guided perceptual benefit). Second, changes in theta and alpha power differentiated soundscape contexts that were predictive from nonpredictive contexts at two distinct temporal intervals from soundscape onset (early-950 ms peak for theta and alpha, and late-1650 ms peak for alpha only). Third, early theta activity in the left anterior temporal lobe was correlated with memory-guided perceptual benefits. Together, these findings underscore the role of distinct neural processes at different time points during associative retrieval. By emphasizing temporal sensitivity and by isolating cue-related activity, we reveal a two-stage retrieval mechanism that advances our understanding of how memory influences auditory perception.
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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 | 0.002 |
| 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 | 0.000 | 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".