Eye-movement reinstatement and neural reactivation during mental imagery
Bibliographic record
Abstract
Abstract Half a century ago, Donald Hebb posited that mental imagery is a constructive process that emulates perception. Specifically, Hebb claimed that visual imagery results from the reactivation of neural activity associated with viewing images. He also argued that neural reactivation and imagery benefit from the re-enactment of eye movement patterns that first occurred at viewing (fixation reinstatement). To investigate these claims, we applied multivariate pattern analyses to functional MRI (fMRI) and eye-tracking data collected while healthy human participants repeatedly viewed and visualized complex images. We observed that the specificity of neural reactivation correlated positively with vivid imagery and with memory for stimulus image details. Moreover, neural reactivation correlated positively with fixation reinstatement, meaning that image-specific eye movements accompanied image-specific patterns of brain activity during visualization. These findings support the conception of mental imagery as a simulation of perception, and provide evidence of the supportive role of eye-movement in neural reactivation.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".