Perceptual blurring and recognition memory: A differential memory effect in pupil responses
Bibliographic record
Abstract
Perceptual degradation of visual stimuli decreases performance in many tasks. In word-reading, response time (RT) for words with no blur (NB) are slightly faster than for words with low blur (LB) and much faster than for words with high blur (HB). However, subsequently probing recognition memory for these words reveals superior memory sensitivity for HB words than NB words, and numerically worse memory for LB words than NB words. (Rosner, Davis & Milliken, 2015). This result suggests that a high level of perceptual degradation can enhance long-term memory, perhaps due to the upregulation of attention in response to processing difficulty at the time of encoding. Borrowing from the literature on pupil dilation as an index of mental effort (e.g., Beatty, 1982), we incorporated pupil size as a measure of attentional engagement in the present study. In the encoding phase, half of the participants were presented with NB and LB words intermixed, and the other half with NB and HB words intermixed. In the test phase, participants completed a surprise recognition memory task. Pupil size was recorded throughout the experiment. As expected, RT in the encoding phase increased with stimulus blur. More important, recognition memory in the test phase (relative to NB words) was slightly worse for LB words and significantly better for HB words. Evoked pupillary response (EPR) during the encoding phase did not differ between NB and LB words, but was larger for HB than NB words. Critically, the larger EPR to HB words at study was driven by 'old' words that were later recognized (hits), rather than those that were not (misses). Follow-up analyses showed the EPR results did not depend on longer time-on-task (slower RT) for HB words. The results are consistent with an attentional-upregulation account of the effect of perceptual degradation on long-term memory. Meeting abstract presented at VSS 2018
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".