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Record W4390744863 · doi:10.31234/osf.io/zxv5n

Towards an interdisciplinary ‘science of the mind’: a call for enhanced collaboration between philosophy and neuroscience

2024· preprint· en· W4390744863 on OpenAlexfundno aff
Alessandra Buccella, Uri Maoz, Liad Mudrik

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsnot available
FundersTempleton World Charity FoundationCanadian Institute for Advanced ResearchFetzer InstituteJohn Templeton Foundation
KeywordsNeurophilosophyCognitive sciencePhilosophy of mindNeurolawPhilosophy of psychologyCultural neuroscienceEpistemologyPsychologySociologyNeuroscienceSocial neuroscienceCognitionPhilosophyMetaphysicsSocial cognition

Abstract

fetched live from OpenAlex

In recent decades, the neuroscientific community has moved from describing the neural underpinnings of mental phenomena – as characterized by experimental psychology and philosophy of mind – to attempting to redefine those mental phenomena based on neural findings. Nowadays, many are intrigued by the idea that neuroscience might provide the ‘missing piece’ that would allow philosophers (and, to an extent, psychologists, too) to make important advances, generating new means that these disciplines lack to close knowledge gaps and answer questions like ‘What is Free Will? and ‘Do humans have it?’. In this paper, we argue that instead of striving for neuroscience to replace philosophy in the ongoing quest to understanding human thought and behavior, more synergetic relations should be established, where neuroscience does not only inspire philosophy but also draws from it. We claim that such a collaborative co-evolution, with the two disciplines nourishing and influencing each other, is key to resolving long-lasting questions that have thus far proved impenetrable for either discipline on its own.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0020.007
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.105
GPT teacher head0.416
Teacher spread0.312 · 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.

Study designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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