Sensorimotor predictions shape reported conscious visual experience in a breaking continuous flash suppression task
Why this work is in the frame
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Bibliographic record
Abstract
Accounts of predictive processing propose that conscious experience is influenced not only by passive predictions about the world, but also by predictions encompassing how the world changes in relation to our actions-that is, on predictions about sensorimotor contingencies. We tested whether valid sensorimotor predictions, in particular learned associations between stimuli and actions, shape reports about conscious visual experience. Two experiments used instrumental conditioning to build sensorimotor predictions linking different stimuli with distinct actions. Conditioning was followed by a breaking continuous flash suppression task, measuring the speed of reported breakthrough for different pairings between the stimuli and prepared actions, comparing those congruent and incongruent with the trained sensorimotor predictions. In Experiment 1, counterbalancing of the response actions within the breaking continuous flash suppression task was achieved by repeating the same action within each block but having them differ across the two blocks. Experiment 2 sought to increase the predictive salience of the actions by avoiding the repetition within blocks. In Experiment 1, breakthrough times were numerically shorter for congruent than incongruent pairings, but Bayesian analysis supported the null hypothesis of no influence from the sensorimotor predictions. In Experiment 2, reported conscious perception was significantly faster for congruent than for incongruent pairings. A meta-analytic Bayes factor combining the two experiments confirmed this effect. Altogether, we provide evidence for a key implication of the action-oriented predictive processing approach to conscious perception, namely that sensorimotor predictions shape our conscious experience of the world.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it