Interhemispheric ERP asymmetries over inferior parietal cortex reveal differential visual working memory maintenance for fearful versus neutral facial identities
Bibliographic record
Abstract
The goal of the present investigation was to discover whether visual working memory maintenance for faces is modulated by facial expression using event-related potentials (ERPs). Each trial consisted of two sequential arrays, a memory array and a test array, each including either two or four faces with neutral or fearful expressions. The faces were displayed to the left and to the right of a central fixation cross. Two central arrows cued participants to encode one face or two faces displayed on one side of the memory array. The sustained posterior contralateral negativity (SPCN) component of the ERP time-locked to the onset of the memory array was used as an index of visual working memory maintenance. Visual working memory performance was quantified using indexes of memory capacity (Cowan's K and K-iterative), a standard index of sensitivity (d'), and reaction times (RTs). Relative to neutral faces, superior memory and longer change-detection RTs to fearful face identities were observed when two faces were displayed on the cued side of the memory array. Fearful faces elicited an enhanced SPCN relative to neutral faces, especially when only one face was displayed on the cued side of the memory array. These findings suggest increased maintenance in visual working memory of faces with a fearful expression relative to faces with a neutral expression and that the representational format in which fearful faces are stored in memory may be characterized by enhanced resolution relative to that subtended in the maintenance of neutral faces.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".