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The Knowledge Instinct, Cognitive Functions of Music and Cultural Evolution

2016· book-chapter· en· W2411099392 on OpenAlexaff
Leonid Perlovsky, Nobuo Masataka, Michel Cabanac

Bibliographic record

VenueAdvances in multimedia and interactive technologies book series · 2016
Typebook-chapter
Languageen
FieldNeuroscience
TopicCognitive Science and Education Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsConsciousnessCognitive dissonanceCognitionCognitive scienceInstinctPsychologyUnificationEpistemologySociocultural evolutionPhilosophySociologyComputer scienceSocial psychologyEvolutionary biologyBiologyNeuroscience

Abstract

fetched live from OpenAlex

Evolution of music ability has been considered a mystery from Aristotle to Darwin and as no adaptive purpose has been identified yet, making music is still a puzzle for evolutionary biologists. This chapter considers a new theory of music origin and evolution, identifying a cognitive function of music which helps overcoming cognitive dissonance based on the unification of consciousness that is differentiated by language. According to this theory, music is fundamental for cultural evolution. The reason for music strongly affecting us is that it helps overcoming unpleasant emotions of cognitive contradictions, which are conditions of accumulating knowledge. The chapter considers experimental evidence supporting this theory and the joint evolution of music, culture, and consciousness.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.036
GPT teacher head0.337
Teacher spread0.301 · 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 designTheoretical or conceptual
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

Citations0
Published2016
Admission routes1
Has abstractyes

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