Facing fear: The effect of emotional expressions on visual short-term memory for faces
Bibliographic record
Abstract
Visual short-term memory (VSTM) is capacity-limited, with most people able to keep no more than 3 – 4 simple items in VSTM at any given time (Luck & Vogel, 1997). Notably, as the complexity of stored items increases, capacity seems to decrease suggesting that this range may represent the upper limit of VSTM capacity (Alvarez & Cavanagh, 2005). Is there anything that can offset the trade-off between stimulus complexity and VSTM capacity? Recent studies using face stimuli — which are visually complex — suggest that our extensive experience with faces translates into a VSTM advantage for upright but not inverted faces (Curby & Gauthier, 2007). Here, we connect this line of research to a large body of research showing that emotion can benefit memory. Although such memory-emotion studies have typically pertained to long-term memory, we investigate whether faces signaling emotion have a VSTM advantage over those that are more neutral, and if so whether this effect depends on encoding time (as might be suggested based on work by Maljkovic & Martini, 2005). We measured VSTM capacity for faces with either neutral or fearful expressions under long (4000 ms) or short (1000 ms) encoding durations. To control for basic perceptual differences between the fearful and neutral faces while decreasing the salience of emotional expression, we also tested the same faces in an inverted orientation. Results revealed that VSTM for emotional faces was larger than that for neutral faces, but that this VSTM advantage was limited to upright faces and was equivalent across long and short encoding conditions. In order to clarify the nature of this main finding, we report additional studies probing the impact of more limited encoding durations on this VSTM advantage.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".