Bibliographic record
Abstract
Over the last two decades, neuroscientific research has considerably advanced our understanding of the neurobiological processes that underlie our interactions with artworks. Through a combination of behavioural and neuroimaging methods, experiments have identified sensory, perceptual, emotional and cognitive processes that make important contributions to our psychological experiences of art, in particular the emergence of aesthetic preferences. Here we conduct a selective review of this literature that will provide readers without a background in the neurosciences a first introduction into what we have learned so far. Our review is organised in three parts: First we describe research that has examined neurobiologicalprocesses involved in the sensation and perception of art. Next, we survey findings that cast light on the neural mechanisms underlying our emotional responses to art, including the contribution of the mesocorticolimbic reward circuitry to the computation of aesthetic liking. Third, we outline how cognitive processes associated with expectations, knowledge and expertise significantly influence our response to works of art. We conclude the chapter by discussing how the experience of art relies on an interdependence of sensation, emotion, and cognition, and how the major challenge of future neuroaesthetics research lies in improving our understanding of the complex interplay of these neural processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".