Blinded by emotion? Effect of the emotionality of a scene on susceptibility to false memories.
Bibliographic record
Abstract
Meme si la distorsion est communement presente dans la memoire, le rapport entre l'emotivite d'une scene vecue et la susceptibilite aux souvenirs fausses n'est pas claire. Des participants (au nombre de 90) ont ete recrutes dans le cadre d'une recherche portant sur le « traitement des emotions ». Ils n'ont pas ete informes que leur memoire etait a l'etude. On leur a ensuite presente une scene soit tres positive, neutre ou tres negative (par ex., un accident fatal illustre) tiree du International Affective Picture System (par ex., Lang, Bradley & Cuthbert, 1999). La moitie des participants ont ete exposes a des questions trompeuses - dont l'une comportait une suggestion fausse tres importante (c.-a-d. un gros animal dans la scene). Une heure plus tard on a demande aux participants de se rappeler la scene et on leur a pose 10 questions directes, dont cinq portaient sur la mesinformation presentee auparavant. Dans l'ensemble, les questions trompeuses ont entrave l'exactitude du souvenir de 37 % des participants. De plus, l'emotion negative a accru la susceptibilite aux souvenirs fausses relativement a la mesinformation. Alors qu'aucun des participants non exposes aux questions trompeuses dans toutes les conditions se sont souvenu d'avoir vu le detail fautif important, les participants qui ont ete trompes dans la condition negative se sont souvenu plus souvent (80 %) du detail fautif que les participants dans des conditions positives (40 %) et neutres (40 %).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.056 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".