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Understanding disgust

2012· review· en· W2332270376 on OpenAlexaff
Hanah A. Chapman, Adam K. Anderson

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2012
Typereview
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDisgustPsychologyCommitCognitionSocial psychologyCognitive psychologyDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

Disgust is characterized by a remarkably diverse set of stimulus triggers, ranging from extremely concrete (bad tastes and disease vectors) to extremely abstract (moral transgressions and those who commit them). This diversity may reflect an expansion of the role of disgust over evolutionary time, from an origin in defending the body against toxicity and disease, through defense against other threats to biological fitness (e.g., incest), to involvement in the selection of suitable interaction partners, by motivating the rejection of individuals who violate social and moral norms. The anterior insula, and to a lesser extent the basal ganglia, are implicated in toxicity- and disease-related forms of disgust, although we argue that insular activation is not exclusive to disgust. It remains unclear whether moral disgust is associated with insular activity. Disgust offers cognitive neuroscientists a unique opportunity to study how an evolutionarily ancient response rooted in the chemical senses has expanded into a uniquely human social cognitive domain; many interesting research avenues remain to be explored.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.784
GPT teacher head0.449
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations254
Published2012
Admission routes1
Has abstractyes

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