Do False Memories Look Real? Evidence That People Struggle to Identify Rich False Memories of Committing Crime and Other Emotional Events
Bibliographic record
Abstract
Two studies examined whether people could identify rich false memories. Each participant in both studies was presented with two videos, one of a person recalling a true emotional memory, and one of the same person recalling a false memory. These videos were filmed during a study which involved implanting rich false memories (Shaw & Porter, 2015). The false memories in the videos either involve committing a crime (assault, or assault with a weapon) or other highly emotional events (animal attack, or losing a large sum of money) during adolescence. In study 1, participants (n = 124) were no better than chance at accurately classifying false memories (61.29% accurate), or false memories of committing crime (53.33% accurate). In study 2, participants (n = 82) participants were randomly assigned to one of three conditions, where they only had access to the (i) audio account of the memory with no video, (ii) video account with no audio, or (iii) the full audio-visual accounts. False memories were classified correctly by 32.14% of the audio-only group, 45.45% of the video-only group, and 53.13% of the audio-visual group. This research provides evidence that naïve judges are not able to reliably identify false memories of emotional or criminal events, or differentiate true from true memories. These findings are likely to be of particular interest to those working in legal and criminal justice settings.
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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 | 0.007 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".