Hindsight bias for emotional faces.
Bibliographic record
Abstract
People who learn the outcome to a situation or problem tend to overestimate what was known in the past-this is hindsight bias. Whereas previous research has revealed robust hindsight bias in the visual domain, little is known about how outcome information affects our memory of others' emotional expressions. The goal of the current work was to test whether participants exhibited hindsight bias for emotional faces and whether this varied as a function of emotion. Across five experiments, participants saw images of faces displaying different emotions. In the foresight phase, participants watched several emotional faces gradually clarify from blurry to clear. Once participants believed they knew what emotion the face was exhibiting, they identified the emotion from several options (e.g., angry, disgusted, happy, scared, surprised). In the hindsight phase, participants saw clear versions of each face before stopping the clarification at the point at which they previously identified the emotional expression. On average, participants exhibited hindsight bias for all emotions except happy faces (i.e., they indicated that they identified the emotional expressions at a blurrier point in hindsight than they had in foresight). A multinomial processing tree model of our data revealed that this was not due to participants' better recollection of foresight judgments for happy faces compared to the other emotions. Additionally, participants showed a smaller reconstruction bias for happy faces than the other emotions. We discuss the social implications of these findings as well as the potential for this paradigm to be used across cultures and ages. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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 | 0.022 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".