Peer Review #3 of "Maintaining fixation does not increase demands on working memory relative to free viewing (v0.2)"
Bibliographic record
Abstract
The comparison of memory performance during free and fixed viewing conditions has been used to demonstrate the involvement of eye movements in memory encoding and retrieval, with stronger effects at encoding than retrieval.Relative to conditions of free viewing, participants generally show reduced memory performance following sustained fixation, suggesting that unrestricted eye movements benefit memory.However, the cognitive basis of the memory reduction during fixed viewing is uncertain, with possible mechanisms including disruption of visual-mnemonic and/or imagery processes with sustained fixation, or greater working memory demands required for fixed relative to free viewing.To investigate one possible mechanism for this reduction, we had participants perform a working memory task -an auditory n-back taskduring free and fixed viewing, as well as a repetitive finger tapping condition, included to isolate the effects of motor interference independent of the oculomotor system.As expected, finger tapping significantly interfered with n-back performance relative to free viewing, as indexed by a decrease in accuracy and increase in response times.By contrast, there was no evidence that fixed viewing interfered with n-back performance relative to free viewing.Our findings failed to support a hypothesis of increased working memory load during fixation.They are consistent with the notion that fixation disrupts long-term memory performance through interference with visual processes.
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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 | 0.005 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.230 | 0.127 |
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".