Rendering visual representations from oscillatory brain activity
Bibliographic record
Abstract
The subjectively seamless nature of visual experience would intuitively suggest that the underlying representations of the visual world evolve continuously. There is, however, a controversial alternative suggesting that these visual representations are in fact discrete, built up in the brain over a number of discrete processing epochs. In order to investigate this assertion we extended a new method, based on Bubbles (Gosselin & Schyns, 2001; Smith, Gosselin & Schyns, 2004), to relate EEG oscillatory activity (low frequency theta band, 4–8Hz) to the time course of visual stimulus information processing. In a first experiment naïve observers categorized sparsely sampled pictures of faces, by gender in one session and expressive or not in a second. Using estimates of the information driving behavioral response (accuracy, reaction times) we derived the sensitivity of low frequency EEG oscillations to facial features when observers resolved each of the tasks. We show that theta (4–8Hz) oscillations support discrete information processing epochs, corresponding to a modulated sensitivity of the brain to specific facial features. We reveal the integration of these features over several epochs to forge specific visual representations for different face categorizations. These later epochs not only represent more facial features, but they also integrate information across hemi-fields (i.e. bilaterally rather than contra-laterally). In a second experiment, we instructed naïve observers to categorize by expression, (fear, disgust, anger or surprise), sparsely presented images of expressive faces sampled over a range of spatial frequency bands. Applying this methodology we again found evidence of discrete processing epochs. This technique also enables a tracking in time of the sensitivity to specific facial features in the brain providing more direct evidence of “information picking” strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".