Distinct signatures of social and emotional cues in memory and eye movements.
Bibliographic record
Abstract
Negative emotional stimuli are associated with increased recognition accuracy but decreased memory for the associative context, an effect coined as "tunnel memory" (Steinmetz & Kensinger, 2013). Recently, Stewardson et al. (2023) found that social cues enhance both recognition and associative memory and weaken the effects of negative emotion on memory, suggesting potentially distinct mechanisms underlying how adaptively relevant information is processed and retained when social cues are present. In this study (conducted in 2023-2024), we sought to replicate these findings and use eye tracking to explore attention as a mechanism underlying this divergence. As predicted, both negative images and social cues enhanced recognition memory, with differential effects on associative memory (diminishing for negative, enhancing for social). Negative pictures with few social cues were associated with a "tunneling" of both memory and attention, that is, better recognition but poorer associative memory alongside more frequent, longer fixations on the picture and reduced picture-object saccades. By contrast, social cues led to a partial tunneling of attention-that is, more frequent but shorter fixations and fewer linking saccades-and yet enhanced both picture recognition and associative memory. Perhaps most striking, negative emotion's effects on memory and attention were significantly attenuated when social cues were present. These findings suggest that differences in how negative versus neutral content is processed and retained depend on the social context. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".