Evolutionary and neuropsychological perspectives on addictive behaviors and addictive substances: relevance to the "food addiction" construct
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".