Intentional binding as Bayesian cue combination: testing predictions with trait individual differences
Bibliographic record
Abstract
Intentional binding refers to the subjective compression of the time between an action and its outcome, typically indicated by a forward shift in the judged time of an action toward its outcome (action binding) and the backward shift of an outcome toward the action that caused it (outcome binding). The effect is considered an implicit measure of the sense of agency as it is sensitive to intentional action without requiring explicit reflection upon agency. One way of explaining the sensitivity of intentional binding is to see it as a simple case of multisensory cue combination in which awareness of intentions increases knowledge of the timing of actions. Here we present results consistent with such a mechanism. An experience of involuntariness is central to hypnotic responding, and may arise from strategically being unaware of one’s intentions. Trait differences in the ability to respond to hypnotic suggestion may reflect differing levels of access to motor intentions, with highly hypnotisable people having relatively low access and low hypnotisable people greater access. In a contingent presentation of action and outcome events, low hypnotisables had more precise timing judgements of actions than highs, and showed weaker action binding than highs. These results support the theory that trait hypnotisability is related to access to information related to motor intentions, as increased availability of such information should support more precise judgements of the timing of an intentional action. Intentional binding may thus reflect the Bayesian combination of cross-modal cues.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".