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Record W2558804374 · doi:10.37693/pjos.2016.7.16270

Paintings as “Visual Poetry”: Diagrammatic Iconicity in the Art of Juan Miró

2016· article· en· W2558804374 on OpenAlexvenueno aff
Hsin-yen Chen, I‐Wen Su

Bibliographic record

VenuePublic Journal of Semiotics · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
FundersNational Science Council
KeywordsIconicityDiagrammatic reasoningMetaphorPaintingMeaning (existential)ClosenessConceptual blendingLinguisticsSemioticsModality (human–computer interaction)ArtCognitionPsychologyComputer sciencePhilosophyEpistemologyVisual artsArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Conceptual Metaphor Theory (Lakoff and Johnson, 1980) and work on multimodal metaphors (Forceville, 2006) has opened up a new approach to the study of language and other modalities. However, relatively few cognitive linguistic investigations of visual art have been performed. We analyzed paintings done by Spanish Surrealist Juan Miró (1893-1983), focusing on diagrammatic iconicity, i.e. how his pictorial elements are arranged structurally in ways that correspond to their meaning. In particular, we examined the artist’s paintings between 1940 and 1970, based on the model advanced by Hiraga (2005). Our results show that iconic mappings like SIMILARITY IN MEANING IS SIMILARITY IN FORM, MORE CONTENT IS MORE FORM, and SEMANTIC RELEVANCE IS CLOSENESS, function as cognitive principles prevalent in these paintings. The current study therefore supports the proposal that diagrammatic iconicity operates across different semiotic systems, and at the same time contributes to the description and explanation of artistic practices involving language and painting.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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)0.0010.000

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 head0.025
GPT teacher head0.307
Teacher spread0.283 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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