Author response: Multiple mechanisms link prestimulus neural oscillations to sensory responses
Bibliographic record
Abstract
Give a computer the same input and you should get back the same response every time. But give a human brain the same sensory input and you will see a range of different responses. This is because the brain’s response to sensory input depends not only on the properties of the input, but also on its own internal state at the time when the input is processed. Even in the absence of any input, the brain generates complex patterns of spontaneous activity. Fluctuations in this activity affect how the brain responds to the outside world. The electrical activity in the brain – both spontaneous and in response to sensory input – can be measured using electrodes close to the scalp: this measurement is referred to as electroencephalography, or EEG. Spontaneous brain activity takes the form of rhythmic waves, also known as oscillations. In a person who is awake and relaxed, the EEG consists mainly of slow oscillations called alpha and beta waves. Sensory input, such as an image or a sound, triggers changes in brain activity that can be seen in the EEG. This EEG response is called an event-related potential, or ERP, and consists of a characteristic pattern of peaks and troughs in the EEG. To find out how spontaneous brain activity affects ERPs, Iemi et al. presented images of black and white checkerboards to healthy volunteers. The results showed that the ERP looked different if the stimulus occurred during strong alpha and beta waves. The early part of the ERP – which occurs between 80 and 200 milliseconds after the onset of the stimulus – decreased in size, presumably because it was inhibited by strong alpha and beta waves. In contrast, the later part of the ERP – which occurs more than 400 milliseconds after stimulus onset – increased in size. This paradox is accounted for by a newly recognized feature of the oscillations, namely that they fluctuate around a non-zero value of the EEG. Thus, two different mechanisms contributed to these opposite changes. The findings add to our understanding of how spontaneous brain activity influences how we perceive the world around us. Furthermore, spontaneous brain activity differs in a number of disorders, including schizophrenia and autism. Understanding how spontaneous neural oscillations affect how the brain processes information from the senses could provide new insights into these conditions.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.160 | 0.074 |
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".