A change in taste: the role of microRNAs in altering hedonic value
Bibliographic record
Abstract
The mechanisms associated with neophobia and anhedonia remain largely unknown. Neuropsychological disorders such as depression and schizophrenia are associated with excessive fear and anhedonia, and have been linked to microRNA 137. We hypothesized that microRNAs (miRNAs) in the snail Lymnaea stagnalis are important for regulating feeding behaviour through either preventing neophobia or establishing hedonic value. To test these hypotheses, we used an injection of poly-l-lysine (PLL) to inhibit miRNA biogenesis and observed its effects on feeding behaviour. We repeated these experiments with pre-exposure to novel stimuli capable of eliciting neophobia to disentangle the processes predicted to regulate feeding behaviour. Next, we exposed snails to food stimuli of high hedonic value after PLL injection to reset their hedonic value for that food. Finally, we consolidated our results with previous research by examining the effect of PLL injection on a one-trial appetitive classical conditioning procedure (1TT) to induce long-term memory (LTM). We found that miRNAs are likely not required for preventing neophobia. Moreover, we discovered that snails experienced anhedonia in response to inhibition of miRNA biogenesis, resulting in diminished feeding behaviour for food stimuli with a previously high hedonic value. Snails showed diminished feeding behaviour for multiple food stimuli of high hedonic value post-1TT with PLL injection. This finding suggests that PLL causes anhedonia rather than an impairment of LTM formation following the 1TT procedure. This is the first evidence suggesting that inhibiting the biogenesis of miRNAs contributes to anhedonia in L. stagnalis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".