Reconstructing mental images using Bubbles and electroencephalography
Bibliographic record
Abstract
The exact nature of the visual features that are brought to consciousness when one is engaging in mental imagery is still difficult to study empirically. The few studies that have attempted to reconstruct mental images obtained poor quality results due in part to the extremely sparse coverage of the “scene space” (Shen et al., 2019). Hence, the aim of the present study was to reconstruct better quality mental images by increasing the sampling resolution of the visual features within the scene space using the Bubbles method (Gosselin & Schyns, 2001) and electroencephalography (EEG). So far, we have recorded the brain activity of four participants during two alternating tasks divided into 6 one-hour sessions. In the perception task, participants were presented with two scene images through different sets of randomly located Gaussian apertures or “bubble masks” (1,500 trials per image in total). In the mental imagery task, subjects were shown the two stimuli successively and asked to imagine either the first, or second one (300 trials per image in total). For each participant and for each scene, we correlated the EEG activity patterns between mental imagery and visual perception. Specifically, we correlated the EEG activity in each trial of the visual imagery task with the EEG activity elicited from partial presentation of the scenes through bubbles. Correlation-weighted sums of the associated bubble masks were computed in order to produce, for the first time, “classification images” of mental images.
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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 | 0.001 |
| 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".