Bibliographic record
Abstract
Size matters, especially when it comes to brains. Larger brains are often linked to increased mental performance, but not all animals are brainiacs. Even after considering differences in body size, some species have much larger brains than others. Different individuals within a single species can also vary tremendously in brain size. Scientists do not have a firm grasp of why this variation exists, despite centuries of interest, but some have suspected that brain size may depend on how difficult it is to find dinner. For animals that need to search complex habitats to find their prey, a big brain may equal a full stomach.A new study, led by Caleb Axelrod at the University of Guelph, Canada, tested this ‘habitat complexity’ hypothesis of brain size by comparing pumpkinseed sunfish (Lepomis gibbosus) that lived in different parts of a large lake. Some sunfish always stick close to shore, where they search intensely amongst fallen branches and dense vegetation for tasty but well-hidden morsels like snails and crayfish. Other sunfish inhabit open water in the middle of the lake and simply snack on plankton as it drifts by. Axelrod and his team spent two summers catching sunfish from both habitats and then carefully collected and weighed their brains.Exactly as predicted, the sunfish from complex, near-shore habitats had brains that were about 8% larger than those of open-water plankton eaters. This is the first evidence that brain size can be linked to differences in habitat within a single species and provides evidence for the hypothesis that living in complex environments may increase brain size. Even within each habitat, however, an individual fish may feed on different amounts of plankton versus hard-to-find prey. To test whether eating easy-to-catch plankton was linked to smaller brains, Axelrod also measured the size of the mouth; previous work had found that a small mouth is a good proxy for more plankton eating. Again, just as expected, small-mouthed fish that used the easier plankton-catching strategy tended to have smaller brains.Next, Axelrod wanted to determine whether near-shore sunfish had larger brains overall, or whether a specialized region was bigger. Perhaps these fish had a particularly good sense of smell or vision? In other animals, these sorts of regional differences are common, but in the sunfish the proportional size of each brain region was the same in the two habitats. The implication is that the brainiac near-shore sunfish may have broadly improved cognitive abilities, but this remains to be studied in detail.The process driving brain size differences in sunfish is not yet known. One possibility is that natural selection favours large-brained fish near the shore, or perhaps the simplicity of open-water living stunts brain growth. Regardless, it seems clear that the size of a sunfish brain is different depending on its habitat. So, should you complicate your life and fill your house with clutter to grow a bigger brain? It's probably too early to draw connections between fish and humans. But next time you're out fishing and trying to decide where to drop your hook, perhaps you should think about casting out far from shore to where the small-brained fish live.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".