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Record W4403701864 · doi:10.3389/fpsyg.2024.1485433

Only consciousness truly exists? Two problems for IIT 4.0’s ontology

2024· article· en· W4403701864 on OpenAlexfundno aff
Ignacio Cea, Niccolò Negro, Camilo Miguel Signorelli

Bibliographic record

VenueFrontiers in Psychology · 2024
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsnot available
FundersAzrieli FoundationFonds De La Recherche Scientifique - FNRS
KeywordsConsciousnessEpistemologyOntologyIntegrated information theoryCore (optical fiber)PsychologyCognitive scienceComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

In this article we present two ontological problems for the Integrated Information Theory of Consciousness 4.0: what we call the (i) the intrinsicality 2.0 problem, and (ii) the engineering problem. These problems entail that truly existing, conscious entities can depend on, and be engineered from, entities that do not objectively exist, which is problematic: if something does not exist in objective reality (i.e., in itself, independently of another entity's consciousness), then it seems that it cannot be part of the material basis and determinants of other entities that do exist on their own. We argue that the core origin of these problems lies in IIT's equation between true existence and phenomenal existence (consciousness), and the corresponding ontological exclusion of non-conscious physical entities (i.e., extrinsic entities) from objective reality. In short, these two problems seem to show that IIT should reconsider the ontological status of these extrinsic entities, because they need to exist objectively to account for the ontological implications of the scenarios we present here, which are permitted by the operational framework of the theory.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0040.040
Scholarly communication0.0090.024
Open science0.0030.010
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.330
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2024
Admission routes1
Has abstractyes

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