A simple semi-automated home-tank method and procedure to explore classical associative learning in adult zebrafish
Bibliographic record
Abstract
The zebrafish is a laboratory species that gained increasing popularity the last decade in a variety of subfields of biology, including toxicology, ecology, medicine, and the neurosciences. An important phenotype often measured in these fields is behaviour. Consequently, numerous new behavioural apparati and paradigms have been developed for the zebrafish, including methods for the analysis of learning and memory in adult zebrafish. Perhaps the biggest obstacle in these methods is that zebrafish is particularly sensitive to human handling. To overcome this confound, automated learning paradigms have been developed with varying success. In this manuscript, we present a semi-automated home tank-based learning/memory test paradigm utilizing visual cues, and show that it is capable of quantifying classical associative learning performance in zebrafish. We demonstrate that in this task, zebrafish successfully acquire the association between coloured-light and food reward. The hardware and software components of the task are easy and cheap to obtain and simple to assemble and set up. The procedures of the paradigm allow the test fish to remain completely undisturbed by the experimenter for several days in their home (test) tank, eliminating human handling or human interference induced stress. We demonstrate that the development of cheap and simple automated home-tank-based learning paradigms for the zebrafish is feasible. We argue that such tasks will allow us to better characterize numerous cognitive and mnemonic features of the zebrafish, including elemental as well as configural learning and memory, which will, in turn, also enhance our ability to study neurobiological mechanisms underlying learning and memory using this model organism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".