Superstitious conditioning forms the experience of free will under causal determinism
Bibliographic record
Abstract
The question of whether humans have free will or not is longstanding in the areas of philosophy, psychology, and neuroscience. While the issue has not been resolved, determinist and compatibilist frameworks have received support from the neuropsychological data. In both cases, decisions are made within a broader framework of causal determinism. If thoughts and behaviours are inevitable consequences of a continuous chain of cause-effect events, why do we feel free? Here, we propose a neuropsychological model for the experience of free will as a conditioned illusion – a misattribution of causality reinforced through operant learning. Drawing on classical findings such as the readiness potential preceding conscious intention and more recent work on active inference, we argue that the temporal contiguity of premotor and motor activations gives rise to superstitious conditioning, shaping an experience of – and belief in – free will that is compatible with determinism. Sustained by dopaminergic circuits and reinforced by sensorimotor feedback loops, our framework situates free will as an experiential phenomenon rooted in the functional neuroanatomy of learning. We discuss implications for medicine, ethics, law, and suggest conditions under which the experience of free will may be disrupted or restructured by disease, pharmacology, and reinforcement history.
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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 | 0.001 | 0.003 |
| 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 | 0.008 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".