Updating social knowledge via episodic memory prediction errors
Bibliographic record
Abstract
Impressions of other individuals-central components of our social schemas-are crucial for predicting how they will behave in social interactions. In some of these interactions, these predictions align with the way an individual behaves, but in other situations, the individual behaves in ways that contradict predictions of the associated schema. In the present study, we sought to understand the impact of these two scenarios on the attached social schema, focusing on the role of episodic memory encoding of such scenarios in driving schema change. Across two behavioral experiments, healthy young participants formed positive or negative impressions (schemas) about social targets. To test the formation of these schemas, they rated the likelihood that these targets would engage in a series of positive and negative social behaviors. Next, participants encoded narratives depicting these targets engaging in behaviors that were either congruent or incongruent with the valence of the associated schema. Finally, participants rated again the likelihood of each target engaging in positive and negative behaviors. Across both experiments, we found that updating, as measured by changes in the likelihood ratings, was influenced by the strength of memory encoding of the intervening narrative, particularly when that narrative was incongruent with a negative schema. These results emphasize the crucial role of episodic memory in updating our social schemas, or expectations of other individuals, and suggest that we more readily update these schemas to have a more positive impression of others.
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 | 0.003 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".