Bibliographic record
Abstract
The paper is a review of the literature specialized in identifying brain areas and neurochemical functions that underlie different learning phenomena in teleost fish. The neuroethological approach, a description of the fish brain anatomy, ontogeny and phylogeny, the evolutionary discussion of the relationships between fish and mammals, and the cumulative evidence that suggests homologies in neurobehavioral functions between fish and mammals are introduced. Two predominant approaches for studying the neurobiology of learning in fish were identified, namely brain lesions and chemical stimulation. Regarding the effect of specific brain lesions,telencephalic ablationsonly affectedhabituationlearning (sensitization and classic conditioning were not impaired). Conversely, cerebellum lesions caused impairments in classical conditioning of eye-retraction and spatial learning (similar effects in mammals suggest that the functions of the cerebellum may have evolved early in vertebrate history). Regarding emotional learning, it is argued that research on avoidance and escape learning has been narrowly oriented and that new possibilities may derive from Hineline’s (1977) parametric analysis. Medium Pallium (MP) areas were identified as critical for emotional learning in fish. Furthermore, neurobehavioral functions of MP seem to be similar to the functions of the amygdala in mammals. Concerning neurochemical processes, antagonists of the NMDA receptors affected in a dose-dependent manner the acquisition of avoidance and fear conditioning. Alternatively, Nitric Oxide (NO) and cyclic Guanosine Monophosphate (cGMP) seem to be involved in the consolidation processof emotional learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".