Beyond the Illusion of Controlled Environments: How to Embrace Ecological Pertinence in Research?
Bibliographic record
Abstract
Through the lens of preclinical research on substance use disorders (SUD), I propose a reflection aimed at re-evaluating animal models in neuroscience, with a focus on ecological relevance. While rodent models have provided valuable insights into the neurobiology of SUD, the field currently faces a validation crisis, with findings often failing to translate into effective human treatments. Originally designed to address the lack of reproducibility in animal studies, the current global gold standard of rigorous standardization has led to increasingly controlled environments. This growing disconnection between laboratory settings and real-world scenarios exacerbates the validation crisis. Rodent models have also revealed various environmental influences on drug use and its neural mechanisms, highlighting parallels with human behaviour and underscoring the importance of ecological relevance in behavioural research. Drawing inspiration from inquiries in ethology and evolutionary biology, I advocate for incorporating greater environmental complexity into animal models. In line with this idea, the neuroethological approach involves studying spontaneous behaviours in seminatural habitats while utilizing advanced technologies to monitor neural activity. Although this framework offers new insights into human neuroscience, it does not adequately capture the complex human conditions that lead to neuropsychiatric diseases. Therefore, preclinical research should prioritize understanding the environmental factors that shape human behaviour and neural architecture, integrating these insights into animal models. By emphasizing ecological relevance, we can achieve deeper insights into neuropsychiatric disorders and develop more effective treatment strategies. This approach highlights significant benefits for both scientific inquiry and ethical considerations. The controlled environment is a chimera; it is time to rethink our models. Here, I have chosen the prism of preclinical research on SUD to present, in a nonexhaustive manner, advances enabled by the use of rodent models, the crises faced by animal experimentation, the reflections and responses provided by laboratories, to finally propose rethinking our models around questions of ecological relevance, in order to improve both ethics and scientific quality. Although my discussion is illustrated by the situation in preclinical research on SUD, the observation drawn from it and the proposals made can extend to many other domains and species.
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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 | 0.224 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.121 |
| Scholarly communication | 0.020 | 0.040 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.013 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".