The cost of facing fear: Visual working memory is impaired for faces expressing fear
Bibliographic record
Abstract
Previous research has identified numerous factors affecting the capacity and accuracy of visual working memory (VWM). One potentially important factor is the emotionality of the stimuli to be encoded and held in VWM. We often must hold in VWM information that is emotionally charged, but much is still unknown about how the emotionality of stimuli impacts VWM performance. In the current research, we performed four studies examining the impact of fearful facial expressions on VWM for faces. Fearful expressions were found to produce a consistent cost to VWM performance. This cost was modulated by encoding time, but not set size. This cost was only present for faces in an upright orientation consistent with this cost being a product of the emotionality of the faces rather than lower-level perceptual differences between neutral and fearful faces. These findings are discussed in the context of existing theoretical accounts of the impact of emotion on information processing. We suggest that a number of competing effects drive both costs and benefits and are at play when emotional information must be stored in VWM, with the task context determining the balance between them.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".