Bibliographic record
Abstract
While theoretical models increasingly use Bayesian frameworks to explain neural processing, a significant gap exists in understanding how these mathematical principles are biologically implemented. This thesis proposes that serotonin serves as a biological mechanism for precision modulation in predictive processing, tested through experiments on exploratory behavior, perceptual illusions, and psychedelic-induced neural states. The research reveals how serotonergic modulation in rodent whisking behavior demonstrates precision weighting as a key mechanism influencing sensory processing. Using active inference frameworks, the work shows serotonin modulates the precision of sensory inputs and prior habits, regulating exploratory behavior and environmental sampling. Robotics experiments translate these biological insights into artificial systems, demonstrating how precision-based active inference can guide autonomous behavior in humanoid robots through adaptive sensorimotor control and efficient information seeking. Theoretical work on attention and body ownership illusions explores precision control in embodied systems. Mathematical modeling of the rubber hand illusion shows how the brain arbitrates between competing sensory models via precision-weighted inference. Analysis of psilocybin's neural effects demonstrates that this serotonergic agent increases chaotic brain responses and neural transition complexity, supporting theories that psychedelics decrease hierarchical neural communication precision through fundamental reorganization of neural dynamics.
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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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".