Cortical evidence accumulation for visual perception occurs irrespective of reports
Bibliographic record
Abstract
Perception is a multi-faceted, dynamical process that can be tackled empirically through measures of stimulus detectability and confidence. We recorded stereo-electroencephalographic data of 29 participants partaking in three pre-registered experiments to assess if evidence accumulation, a form of sequential sampling of sensory evidence, can explain perception. In an immediate-response experiment, high-gamma activity from individual channels and decoded multivariate latent variables in the visual, inferior frontal, and anterior insular cortices display a correlation between the slope of their increase and reaction times. In two further experiments, this signal in the ventral visual cortex differentiates between (1) stimuli reported as seen vs. unseen in delayed detection, (2) high and low intensity stimuli during passive viewing, and (3) levels of confidence when stimuli were detected. A computational model of leaky evidence accumulation can successfully reproduce both behavioral and neural data. Overall, we show that evidence accumulation explains subjective aspects of visual perception.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".