Optimal Endogenous Neural Noise Speeds Reaction Time
Bibliographic record
Abstract
Objective: To determine whether the detection of weak visual stimuli is facilitated by a particular level of endogenous neural noise.Method: We measured the EEG while subjects responded to the occurrence of weak luminance increments. We used independent component (IC) analysis and dipole fitting to locate the neural sources of EEG responses to the stimuli in occipital, frontal, and temporal cortex. We then determined the relationship between the variability of the frequency-specific oscillations of the relevant neural sources in the 1 sec before stimulus presentation and the RT on hit (correct detection of the increment) trials.Results: An intermediate amount of power variability yielded the fastest RTs. This nonlinear relationship was found for occipital and frontal cortical sources and was strongest for theta (4- 8 Hz) and alpha (9-14 Hz) frequencies, but was also present for gamma (30-50 Hz) in frontal cortex.Conclusion: These results support the idea that an intermediate level of endogenous neural noise optimizes the brain’s response to a weak visual stimulus.
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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".