Rhythmic Information Sampling in the Brain during Visual Recognition
Bibliographic record
Abstract
When we fixate an object, visual information is continuously received on the retina. Several studies observed behavioral oscillations in perceptual sensitivity across such stimulus time, and these fluctuations have been linked to brain oscillations. However, whether specific brain areas show oscillations across stimulus time (i.e., different time points of the stimulus being more or less processed, in a rhythmic fashion) has not been investigated. Here, we revealed random areas of face images at random moments across time and recorded the brain activity of male and female human participants using MEG while they performed two recognition tasks. This allowed us to quantify how each snapshot of visual information coming from the stimulus is processed across time and across the brain. Oscillations across stimulus time (rhythmic sampling) were mostly visible in early visual areas, at theta, alpha, and low beta frequencies. We also found that they contributed to brain activity more than previously investigated rhythmic processing (oscillations in the processing of a single snapshot of visual information). Nonrhythmic sampling was also visible at later latencies across the visual cortex, either in the form of a transient processing of early stimulus time points or of a sustained processing of the whole stimulus. Our results suggest that successive cycles of ongoing brain oscillations process stimulus information incoming at successive moments. Together, these results advance our understanding of the oscillatory neural dynamics associated with visual processing and show the importance of considering the temporal dimension of stimuli when studying visual recognition. SIGNIFICANCE STATEMENT Several behavioral studies have observed oscillations in perceptual sensitivity over the duration of stimulus presentation, and these fluctuations have been linked to brain oscillations. However, oscillations across stimulus time in the brain have not been studied. Here, we developed an MEG paradigm to quantify how visual information received at each moment during fixation is processed through time and across the brain. We showed that different snapshots of a stimulus are distinctly processed in many brain areas and that these fluctuations are oscillatory in early visual areas. Oscillations across stimulus time were more prevalent than previously studied oscillations across processing time. These results increase our understanding of how neural oscillations interact with the visual processing of temporal stimuli.
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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 | 0.001 |
| 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 | 0.000 | 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".