A simple semi-automated home-tank method and procedure to explore classical associative learning in adult zebrafish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it