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Exploring the Landscape of Emotion in Aesthetic Experience

2020· book-chapter· en· W3048417421 on OpenAlexaff
Gerald C. Cupchik

Bibliographic record

VenueOxford University Press eBooks · 2020
Typebook-chapter
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSadnessHappinessFeelingPleasureAestheticsPsychologyAesthetic experienceEveryday lifeCognitive psychologySocial psychologyArtEpistemologyPhilosophyPsychotherapist

Abstract

fetched live from OpenAlex

Abstract The term “aesthetic emotion” was treated differently by scholars from the late 19th and early 20th centuries compared with those from the 21st, and there is much to learn from the “early” group. William James distinguished “subtler emotions” that encompass aesthetic, scientific, and ethical qualities, in contrast to “coarse emotions,” such as happiness or sadness, which are part of everyday life. Others, such as Bosanquet, Clay, Bullough, and Dewey described underlying processes that shape aesthetic experiences during episodes of creation and reception. Their insight is that everyday emotions are elevated to abstract and universal levels during aesthetic episodes, much like Aristotle described more than 2,000 years ago. Later researchers, such as Menninghaus and colleagues, were influenced by the “cognitive turn” and treat “aesthetic emotions” as hypothetical constructs whose independent existence is predicated on word frequencies associated with stimulus ratings. The precision of current empiricism can benefit from incorporating rich theoretical musings of the past about aesthetic processes. A comprehensive model should integrate processes related to aesthetics and emotion during creation and reception episodes. Formal properties of art or literary works can stimulate feelings of pleasure or excitement. The subject matter can offer suggestions that elicit personal connections and related emotions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.113
GPT teacher head0.233
Teacher spread0.121 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2020
Admission routes1
Has abstractyes

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