Intentional binding as Bayesian cue combination: Testing predictions with trait individual differences.
Bibliographic record
Abstract
We investigated differences in intentional binding in high and low hypnotizable groups to explore two questions relating to (a) trait differences in the availability of motor intentions to metacognitive processes and (b) a proposed cue combination model of binding. 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. Intentional binding refers to the subjective compression of the time between an action and its outcome, 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 a causal action (outcome binding). Intentional binding 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. In a contingent presentation of action and outcome events, low hypnotizable had more precise timing judgments of actions and also showed weaker action binding than highs. These results support the theory that trait hypnotizability is related to access to information related to motor intentions, and that intentional binding reflects the Bayesian combination of cross-modal cues. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".