The ram cichlid (Mikrogeophagus ramirezi) learns an associative task: a new fish species for memory research
Bibliographic record
Abstract
Fish are the most species rich and evolutionarily oldest vertebrate taxon. This represents opportunities for biologists who intend to employ laboratory animals in their comparative or translational research. Yet, the overwhelming majority of such studies use a single fish species, the zebrafish, a suboptimal strategy from the comparative standpoint. Neuronal plasticity (learning and memory) is perhaps one of the most complex biological phenomena from a mechanistic standpoint, and thus its analysis could benefit from the use of evolutionarily ancient and simple vertebrate model organisms, i.e., fish species. However, learning & memory research with the zebrafish has been replete with problems. Here, we employ a novel fish species, the ram cichlid, we argue will be particularly appropriate for this purpose for practical as well as ethological/ecological reasons. First, we investigate whether the ram cichlid exhibits innate preference for certain colours (red, blue, yellow or green) in a four-choice task, the plus maze. Subsequently, we pair the apparently least preferred colour (green, the conditioned stimulus or CS) with food reward (the unconditioned stimulus, US) in the plus maze, a CS-US associative learning task. After eight pairing trials, we run a probe trial during which only the CS is presented. At this trial, we find significant preference to the CS, i.e., acquisition of memory of CS-US association. We argue that our proof-of-concept study demonstrating fast acquisition of CS-US association in the ram cichlid, coupled with the universal utility of some genome editing methods, will facilitate the mechanistic analysis of learning and memory.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".