Impact of the expectation on memory reconsolidation using a post retrieval extinction paradigm
Bibliographic record
Abstract
Abstract Objective The present study examined if the expectation for learning enhances reconsolidation of conditioned fear memories using the post-retrieval extinction paradigm in an undergraduate sample (n = 48). Methods The study took place over three consecutive days. The expectation for learning was manipulated through oral instructions prior to memory reactivation. On day one, participants underwent differential fear conditioning to two spider images (CS+ and CS-). On day two, participants were assigned to either a reactivation with expectation for learning group, a reactivation with no expectation for learning group, or a no reactivation group. On day three, return of fear in response to the CS+ spider image was measured following reinstatement (i.e., four shocks). Fear potentiated startle (FPS) and skin conductance response (SCR) were taken as measures of fear. Results The study found evidence that the expectation for learning may enhanced reconsolidation with FPS as a measure of fear as it was only the expectation for learning group in which FPS to the CS+ remained stable following reinstatement, however this effect was small and non-robust. In contrast, no evidence of reconsolidation was observed for SCR, as all participants exhibited a return of fear following reinstatement. Implications These findings suggest that a verbal manipulation of the expectation for learning may not be salient enough to induce reconsolidation as measured by SCR but may be sufficient as measured by FPS. Additionally, given in the inconsistent findings between SCR and FPS, the study’s results bring into question whether the post-retrieval extinction paradigm is appropriate to investigate reconsolidation using both physiological measures concurrently.
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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 | 0.001 |
| 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 | 0.001 |
| 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".