Population Activity in the Human Dorsal Pathway Predicts the Accuracy of Visual Motion Detection
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Bibliographic record
Abstract
A person's ability to detect a weak visual target stimulus varies from one viewing to the next. We tested whether the trial-to-trial fluctuations of neural population activity in the human brain are related to the fluctuations of behavioral performance in a "yes-no" visual motion-detection task. We recorded neural population activity with whole head magnetoencephalography (MEG) while subjects searched for a weak coherent motion signal embedded in spatiotemporal noise. We found that, during motion viewing, MEG activity in the 12- to 24-Hz ("beta") frequency range is higher, on average, before correct behavioral choices than before errors and that it predicts correct choices on a trial-by-trial basis. This performance-predictive activity is not evident in the prestimulus baseline and builds up slowly after stimulus onset. Source reconstruction revealed that the performance-predictive activity is expressed in the posterior parietal and dorsolateral prefrontal cortices and, less strongly, in the visual motion-sensitive area MT+. The 12- to 24-Hz activity in these key stages of the human dorsal visual pathway is correlated with behavioral choice in both target-present and target-absent conditions. Importantly, in the absence of the target, 12- to 24-Hz activity tends to be higher before "no" choices ("correct rejects") than before "yes" choices ("false alarms"). It thus predicts the accuracy, and not the content, of subjects' upcoming perceptual reports. We conclude that beta band activity in the human dorsal visual pathway indexes, and potentially controls, the efficiency of neural computations underlying simple perceptual decisions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it