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Record W4413581098 · doi:10.31234/osf.io/fk3yt_v2

Superstitious conditioning forms the experience of free will under causal determinism

2025· preprint· en· W4413581098 on OpenAlexfundno aff
Cooper Kansala, Emre Cicek, Vanessa Nkansah-Okoree, A. Cassandra Golding, Nirosha J. Murugan, Nicolas Rouleau

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsDeterminismFree willConditioningEpistemologyPsychologyPositive economicsPhilosophyEconomicsMathematicsStatistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.290
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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