Suppression-induced forgetting: a pre-registered replication of the think/no-think paradigm
Bibliographic record
Abstract
Post-traumatic stress disorder is characterised by recurring memories of a traumatic experience despite deliberate attempts to forget (i.e., suppression). The Think/No-Think (TNT) task has been used widely in the laboratory to study suppression-induced forgetting. During the task, participants learn a series of cue-target word pairs. Subsequently, they are presented with a subset of the cue words and are instructed to think (respond items) or not think about the corresponding target (suppression items). Baseline items are not shown during this phase. Successful suppression-induced forgetting is indicated by the reduced recall of suppression compared to baseline items in recall tests using either the same or different cues than originally studied (i.e., same- and independent-probe tests, respectively). The current replication was a pre-registered collaborative effort to evaluate an online experimenter-present version of the paradigm in 150 English-speaking healthy individuals (89 females; MAge = 31.14, SDAge = 7.73). Overall, we did not replicate the suppression-induced forgetting effect (same-probe: BF01 = 7.84; d = 0.03 [95% CI: −0.13; 0.20]; independent-probe: BF01 = 5.71; d = 0.06 [95% CI: −0.12; 0.24]). These null results should be considered in light of our online implementation of the paradigm. Nevertheless, our findings call into question the robustness of suppression-induced forgetting.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".