Dynamic reduction of neural uncertainty regulates perceptual decisions in a Bayes-optimal manner
Bibliographic record
Abstract
Abstract Fast and accurate decisions are fundamental for adaptive behaviour. Theories of decision making posit that evidence in favour of different choices is gradually accumulated until a critical value is reached. It remains unclear, however, which aspects of the neural code get updated during evidence accumulation. Here we investigated whether evidence accumulation relies on a gradual increase in the precision of neural representations of sensory input. Healthy human volunteers discriminated global motion direction over a patch of moving dots, and their brain activity was recorded using electroencephalography. Time-resolved neural uncertainty was estimated using multivariate feature-specific analyses of brain activity. Behavioural measures were modelled using iterative Bayesian inference either on its own (i.e., the full model), or by swapping free model parameters with neural uncertainty estimates derived from brain recordings. The neurally-restricted model was further refitted using randomly shuffled neural uncertainty. The full model and the unshuffled neural model yielded very good and comparable fits to the data, while the shuffled neural model yielded worse fits. Taken together, the findings reveal that the brain relies on reducing neural uncertainty to regulate decision making. They also provide neurobiological support for Bayesian inference as a fundamental computational mechanism in support of decision making.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".