The Intersection of Ultra-Processed Foods, Neuropsychiatric Disorders, and Neurolaw: Implications for Criminal Justice
Bibliographic record
Abstract
Over the last decade there has been increasing interest in the links between the consumption of ultra-processed foods and various neuropsychiatric disorders, aggression, and antisocial behavior. Neurolaw is an interdisciplinary field that seeks to translate the rapid and voluminous advances in brain science into legal decisions and policy. An enhanced understanding of biophysiological mechanisms by which ultra-processed foods influence brain and behavior allows for a historical reexamination of one of forensic neuropsychiatry’s most famous cases—The People v. White and its associated ‘Twinkie Defense’. Here in this Viewpoint article, we pair original court transcripts with emergent research in neurolaw, including nutritional neuroscience, microbiome sciences (legalome), pre-clinical mechanistic research, and clinical intervention trials. Advances in neuroscience, and related fields such as the microbiome, are challenging basic assumptions in the criminal justice system, including notions of universal free will. Recent dismissals of criminal charges related to auto-brewery syndrome demonstrate that courts are open to advances at the intersection of neuromicrobiology and nutritional neuroscience, including those that relate to criminal intent and diminished capacity. As such, it is our contention that experts in the neurosciences will play an increasing role in shaping research that underpins 21st-century courtroom discourse, policy, and decision-making.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".