Sensorimotor contingency modulates breakthrough of virtual 3D objects during a breaking continuous flash suppression paradigm
Bibliographic record
Abstract
To investigate how embodied sensorimotor interactions shape subjective visual experience, we developed a novel combination of Virtual Reality (VR) and Augmented Reality (AR) within an adapted breaking continuous flash suppression (bCFS) paradigm. In a first experiment, participants manipulated novel virtual 3D objects, viewed through a head-mounted display, using three interlocking cogs. This setup allowed us to manipulate the sensorimotor contingencies governing interactions with virtual objects, while characterising the effects on subjective visual experience by measuring breakthrough times from bCFS. We contrasted the effects of the congruency (veridical versus reversed sensorimotor coupling) and contingency (live versus replayed interactions) using a motion discrimination task. The results showed that the contingency but not congruency of sensorimotor coupling affected breakthrough times, with live interactions displaying faster breakthrough times. In a second experiment, we investigated how the contingency of sensorimotor interactions affected object category discrimination within a more naturalistic setting, using a motion tracker that allowed object interactions with increased degrees of freedom. We again found that breakthrough times were faster for live compared to replayed interactions (contingency effect). Together, these data demonstrate that bCFS breakthrough times for unfamiliar 3D virtual objects are modulated by the contingency of the dynamic causal coupling between actions and their visual consequences, in line with theories of perception that emphasise the influence of sensorimotor contingencies on visual experience. The combination of VR/AR and motion tracking technologies with bCFS provides a novel methodology extending the use of binocular suppression paradigms into more dynamic and realistic sensorimotor environments.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".