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Record W2020996742 · doi:10.2147/sar.s56835

Evolutionary and neuropsychological perspectives on addictive behaviors and addictive substances: relevance to the "food addiction" construct

2014· review· en· W2020996742 on OpenAlexaff
Caroline Davis

Bibliographic record

VenueSubstance Abuse and Rehabilitation · 2014
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsYork University
Fundersnot available
KeywordsAddictionContext (archaeology)MedicineNicotinePsychiatryPsychologyDevelopmental psychologyBiology

Abstract

fetched live from OpenAlex

It has been argued that food cannot be "addictive", unlike conventional drugs of abuse, because it is an essential part of life. In this paper, evidence is reviewed, largely from an evolutionary psychobiological perspective, that plant-based psychoactive drugs (such as those derived from the opium poppy and the coca leaf) and gambling-related behaviors were once adaptive for human health and survival in a similar manner as energy-based foods were for nourishment. "Evolutionary mismatch" viewpoints contend that certain behaviors were enhanced during the hunter-gatherer lifestyle - from which our genetic endowment had its origins - because they bestowed both survival and reproductive advantages to the species. However, in the context of advanced technology and other rapid environmental changes, these same behaviors have tended to become maladaptive and greatly overexpressed. Similar to the manufactured purification of psychotropic plant-based substances, the reward impact of processed and hyperpalatable foods, with their high levels of sugar, fat, and salt, is much increased from foods produced in nature. It is concluded therefore that what was once beneficial and necessary for our survival has been altered and ultraprocessed into edible products that may be disadvantageous and potentially addictive.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.027
GPT teacher head0.337
Teacher spread0.310 · 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.

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

Citations103
Published2014
Admission routes1
Has abstractyes

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