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Record W4388459989 · doi:10.1163/22134468-bja10097

Causation Bridges the Two Times

2023· article· en· W4388459989 on OpenAlexaff
Holly Andersen

Bibliographic record

VenueTiming & Time Perception · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCausationAffordanceContingencyPhenomenology (philosophy)EpistemologyBridge (graph theory)Philosophy of sciencePsychologyCognitive scienceCausality (physics)Cognitive psychologyPhilosophyPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract The two-times problem, where time as experienced seems to have distinctive features different than those found in fundamental physics, appears to be more intractable than necessary, I argue, because the two times are marked out from the positions furthest apart: neuroscience and physics. I offer causation as exactly the kind of bridge between these two times that authors like Buonomano and Rovelli (forthcoming) are seeking. It is a historical contingency from philosophical discussions around phenomenology, and a methodological artefact from neuroscience, that most studies of temporal features of experience require subjects to be sufficiently still that their engagement with affordances in the environment can be at best tested in artificial and highly constrained ways. Physics does not offer an account of causation, but accounts of causation are tied to or grounded in physics in ways that can be clearly delineated. Causation then serves as a bridge that coordinates time as experienced, via interaction with affordances in the environment, with time in physics as it constrains causal relationships. I conclude by showing how an information-theoretic account of causation fits neatly into and extends the information gathering and utilizing system (IGUS) of Gruber et al. ( Front. Psychol., 13 , 718505).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.012

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 head0.051
GPT teacher head0.293
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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