The role of active inference in conscious awareness
Bibliographic record
Abstract
Active inference, a first-principles framework for modelling the behaviour of sentient agents, is beginning to be applied in consciousness research. One hypothesis arising from the framework is that active inference is necessary for changes in conscious content. As one component of an extensive adversarial collaboration among competing theories of consciousness, active inference will be contrasted with two other theories of consciousness, neither of which posit that active inference is necessary for consciousness. Here, we thus present a Study Protocol designed to test the active inference hypothesis using a carefully controlled adaptation of the motion-induced blindness paradigm, where an 'active' condition with richer active inference is contrasted with a 'passive' condition. In the active condition, participants direct their gaze towards a target stimulus following its disappearance from consciousness, and report on its subsequent reappearance. In the passive condition, participants maintain central fixation, while the stimulus array is moved across the visual field (in a replay of the active condition based on eye-tracking data acquired during active trials). In two experiments, we plan to investigate target reappearance across active and passive conditions to evaluate the contribution of active inference to conscious awareness. Results will eventually be considered in the context of all the experiments conducted as part of the overall adversarial collaboration.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".