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Record W1995247174 · doi:10.1080/096470490944860

On the Energy Cost of Mental Effort

2006· article· en· W1995247174 on OpenAlexafffund
Theodore L. Sourkes

Bibliographic record

VenueJournal of the History of the Neurosciences · 2006
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill University
FundersMemorial University of Newfoundland
KeywordsPsychicEnergy (signal processing)Mental activityCognitionPsychologyEnergy metabolismCognitive psychologyScale (ratio)Process (computing)Mental processNeuroscienceCognitive scienceComputer scienceMedicinePhysics

Abstract

fetched live from OpenAlex

The discovery of the Law of Conservation of Energy in the 1840s had consequences for psychological theory. Does the process of thinking involve a novel form of energy that is not recognized by physical science? E. L. Youmans (1821-1887) argued that "mental operations are dependent upon material changes in the nervous system." Kurd Lasswitz (1848-1910) introduced the term "psychophysical energy," based upon the electrical activity of the brain. At the beginning of the twentieth century Alfred Lehmann (1858-1921) claimed that intense mental effort leads to a net increase in oxygen utilization and regarded this as evidence of a specific psychic energy. His views were adopted and extended by Hans Berger (1873-1941). F. G. Benedict (1870-1957), drawing upon extensive experience with balance experiments conducted on humans in large-scale respiration calorimeters, concluded that mental effort probably had no effect upon the brain's metabolism. Modern approaches to the problem make use of PET imaging, which detects local changes in glucose utilization by the brain during cognitive activity.

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)0.010
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 score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.034
GPT teacher head0.223
Teacher spread0.189 · 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

Citations13
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the History of the NeurosciencesSame topicFunctional Brain Connectivity StudiesFrench-language works237,207