A translational and multidisciplinary approach to studying the Garcia effect, a higher form of learning with deep evolutionary roots
Bibliographic record
Abstract
Animals, including humans, learn and remember to avoid a novel food when its ingestion is followed, hours later, by sickness - a phenomenon initially identified during World War II as a potential means of pest control. In the 1960s, John Garcia (for whom the effect is now named) demonstrated that this form of conditioned taste aversion had broader implications, showing that it is a rapid but long-lasting taste-specific food aversion with a fundamental role in the evolution of behaviour. From the mid-1970s onward, the principles of the Garcia effect were translated to humans, showing its role in different clinical conditions (e.g. side-effects linked to chemotherapy). However, in the last two decades, the number of studies on the Garcia effect has undergone a considerable decline. Since its discovery in rodents, this form of learning was thought to be exclusive to mammals; however, we recently provided the first demonstration that a Garcia effect can be formed in an invertebrate model organism, the pond snail Lymnaea stagnalis. Thus, in this Commentary, after reviewing the experiments that led to the first characterization of the Garcia effect in rodents, we describe the recent evidence for the Garcia effect in L. stagnalis, which may pave the way for future studies in other invertebrates and mammals. This article aims to inspire future translational and ecological studies that characterize the conserved mechanisms underlying this form of learning with deep evolutionary roots, which can be used to address a range of different biological questions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".