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Record W4379163000 · doi:10.54254/2753-7064/3/20220368

Understanding Intuition: Can Rapid Cognition Perform Better than Rational Thinking in Differentiating Artworks between Artist and Artistic Style Transfer

2023· article· en· W4379163000 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications in Humanities Research · 2023
Typearticle
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsIntuitionPaintingCognitionStyle (visual arts)Cognitive stylePsychologyCognitive psychologyCognitive scienceAestheticsArtVisual arts

Abstract

fetched live from OpenAlex

This study attempts to provide evidence that judgements based on rapid cognition can have higher accuracy than judgements based on rational thinking in particular situations. The design of the experiment was based on the previous study by Sun et al. 2022 that compared cognitive differences in artworks between artists and art style transfer. In the experiment of this paper, the stimuli were generated from 24 pairs of digital artworks done by AI and human painters respectively, and participants were asked to differentiate between the stimuli. The results indicated that participants made more correct choices when there was not enough time to process all the details than when there was enough time to consider all the evidence. This study once again demonstrates that rapid cognition holds advantages in analyzing complex information in a short period of time.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.563
GPT teacher head0.419
Teacher spread0.144 · 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