Brain correlates of conscious perceptions
Bibliographic record
Abstract
A fundamental problem in vision is to understand which visual information correlates with the perception of a stimulus and how the brain extracts and aggregates this information to arrive at a conscious perception. We examined this problem in a case study involving the perceptual reversal of an ambiguous image (here, Dali's painting the Slave Market with the Disappearing Bust of Voltaire). We applied Bubbles in 3D space (2D image space × 5 spatial scales) to five observers disambiguated the image. We determined the visual information correlated with each perception from the observers' responses (“nuns” vs. “Voltaire” vs. “don't know”). Simultaneously, we recorded the observers' EEG at 1024 Hz with a 64 electro-cap and determined the visual information correlated with modulations of amplitude in the theta, alpha, beta and gamma bandwidths of oscillatory EEG activity. Brain correlates of conscious perceptions are likely to integrate the scale information that determines the “the nuns” vs. “Voltaire” behavioral responses. To find the time points of highest scale integration, we first derived the sensitivity of oscillatory brain activity to the scale information associated with behavioral responses, independently for each EEG band. We computed these sensitivity curves from −200 to +800 ms around stimulus onset, in 2 ms independent time steps. The sensitivity curves (one for each spatial scale considered) effectively project EEG activity into the space of the scale information associated with perception. To find the time points of highest information integration, we computed phase-locking factors between all possible pairs of sensitivity curves and averaged them. With this new approach, we were able to isolate the time points at which brain signals are mostly correlated with an integration of the spatial scale information underlying the perception of each observer. This suggests a new approach to find the brain correlates of conscious subjective perceptions.
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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 | 0.004 |
| 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".