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Record W3005063130 · doi:10.15353/jirr.v2.1572

Artificial Mental Representation and Creative Pursuit

2019· article· en· W3005063130 on OpenAlexvenueno aff
Kuil Schoneveld

Bibliographic record

VenueJournal of integrative research & reflection · 2019
Typearticle
Languageen
FieldPsychology
TopicPhilosophy and Theoretical Science
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityRepresentation (politics)Mental representationCognitive scienceEpistemologySociologyWork (physics)PsychologyComputer scienceSocial psychologyPhilosophyEngineeringCognitionPolitical scienceLaw

Abstract

fetched live from OpenAlex

This article first situates the notion of mental representation among the views of Franz Brentano and Daniel Dennett. It then discusses the accounts of creativity of Paul Thagard and Margaret Boden. This is then formally developed with the work of Geraint Wiggins and merged with contemporary work on mental representation in neural networks. Using Paul Smolensky's discussion of symbolic layering, we are then led to the conclusion that artificial neural networks can implement mental representations in creative activities.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.120
GPT teacher head0.498
Teacher spread0.377 · 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 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
Published2019
Admission routes1
Has abstractyes

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