MétaCan
Menu
Back to cohort
Record W3047483035 · doi:10.1073/pnas.2001772117

An objective evaluation of the beholder’s response to abstract and figurative art based on construal level theory

2020· article· en· W3047483035 on OpenAlexfundno aff
Celia Durkin, Eileen Hartnett, Daphna Shohamy, Eric R. Kandel

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
FundersAzrieli FoundationDana FoundationHoward Hughes Medical Institute
KeywordsConstrual level theoryLiteral and figurative languageAestheticsArtPsychologyComputer scienceLinguisticsSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Does abstract art evoke a different cognitive state than figurative art? To address this question empirically, we bridged art theory and cognitive research and designed an experiment leveraging construal level theory (CLT). CLT is based on experimental data showing that psychologically distant events (i.e., occurring farther away in space or time) are represented more abstractly than closer events. We measured construal level elicited by abstract vs. representational art and asked subjects to assign abstract/representational paintings by the same artist to a situation that was temporally/spatially near or distant. Across three experiments, we found that abstract paintings were assigned to the distant situation significantly more often than representational paintings, indicating that abstract art was evocative of greater psychological distance. Our data demonstrate that different levels of artistic abstraction evoke different levels of mental abstraction and suggest that CLT provides an empirical approach to the analysis of cognitive states evoked by different levels of artistic abstraction.

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)0.005
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 score0.880
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.198
GPT teacher head0.409
Teacher spread0.211 · 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

Citations29
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicAnimal and Plant Science EducationFrench-language works237,207