Verbal manipulations of learning expectancy do not enhance reconsolidation
Bibliographic record
Abstract
OBJECTIVES: Pharmacological studies using propranolol suggest that if reactivation signals that new information will be learned (i.e., there is an expectation for learning) reconsolidation can be enhanced. We examined if the verbal instructions to expect new learning will enhance reconsolidation of fear memories using the post-retrieval extinction paradigm. METHODS: On day one, participants (n = 48) underwent differential fear conditioning to two images (CS+ and CS-). On day two, participants were randomly assigned to one of three groups; groups one and two had their memory for the CS+ reactivated (i.e., a single presentation of the CS+) 10 minutes prior to extinction, whereas group three did not have their memory reactivated but went right to extinction (no reactivation group). One reactivation group was told that they would learn something new about the images (expectation for learning group), and the other group was told that they would not learn anything new (no expectation for learning group). On day three, return of fear was measured following reinstatement (i.e., four shocks). Fear potentiated startle (FPS) and skin conductance response (SCR) were measured throughout. RESULTS: There was evidence of fear acquisition for participants for SCR but not FPS. With regards to reconsolidation, SCR increased for the CS+ and CS-in all groups from the end of extinction to the beginning of re-extinction (i.e., return of fear). For FPS, post-hoc tests conducted on the sub-group of participants showing fear learning showed that FPS remained stable in the two reactivation groups, but increased to the CS+, but not the CS- in the no reactivation group. IMPLICATIONS: These findings suggest that a verbal manipulation of the expectation for learning may not be salient enough to enhance reconsolidation. Results are discussed in relation to theories on differences in between SCR, as a measure of cognitive awareness, and FPS, as a measure of fear.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".