Gain neuromodulation mediates perceptual switches: evidence from pupillometry, fMRI, and RNN Modelling
Bibliographic record
Abstract
Abstract Perceptual updating has been proposed to rely upon evolving activity within a recurrent, distributed thalamocortical network whose interconnections are modulated by bursts of ascending neuromodulatory neurotransmitters, such as noradrenaline. To test this hypothesis mechanistically, we leveraged a combination of pupillometry, fMRI and recurrent neural network modelling of an ambiguous figures task. Qualitative shifts in the perceptual interpretation of an ambiguous image were associated with peaks in pupil diameter, an indirect readout of phasic bursts in neuromodulatory tone. We hypothesized that increases in neuromodulatory tone led to neural gain alteration so as to causally mediate perceptual switches. To test this hypothesis, we trained a recurrent neural network to perform an analogous perceptual categorisation task, and then manipulated the gain of the RNN to mimic the effect of neuromodulatory tone. As predicted, we observed an earlier perceptual shift as a function of heightened gain. Leveraging a low-dimensional readout of the RNN dynamics, we developed two novel predictions: perceptual switches should co-occur with peaks in low-dimensional brain state velocity and with flattened energy landscape dynamics. We used dimensionality-reduced summaries of whole-brain fMRI dynamics to independently confirm each of these predictions. These results support the role of the neuromodulatory system in the large-scale network reconfigurations that mediate abrupt changes in perception.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".