Associative Learning in Insects: Evolutionary Models, Mushroom Bodies, and a Neuroscientific Conundrum
Bibliographic record
Abstract
Environmental predictability has for many years been posited to be a key variable in whether learning is expected to evolve in particular species, a claim revisited in two recent papers.However, amongst many researchers, especially neuroscientists, consensus is building for a very different view, namely that learning ability may be an emergent property of nervous systems and, thus, all animals with nervous systems should be able to learn.Here we explore these differing views, sample research on associative learning in insects, and review our own work demonstrating learning in larval antlions (Neuroptera: Myrmeleontidae), a highly unlikely insect candidate.We conclude by asserting that the capacity for associative learning is the default condition favored by neuroscientists: Whenever selection pressures favor evolution of nervous systems, the capacity for associative learning follows ipso facto.Nonetheless, to reconcile these disparate views, we suggest that (a) models for the evolution of learning may instead be models for conditions overriding behavioral plasticity; and, (b) costs of learning in insects may be, in fact, costs associated with more complex cognitive skills, skills that are just beginning to be discovered, rather than simple associative 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".