MétaCan
Menu
Back to cohort
Record W2064795174 · doi:10.1159/000330314

The Hippocampus of Food-Storing Birds

2011· letter· en· W2064795174 on OpenAlexaffabout
David F. Sherry

Bibliographic record

VenueBrain Behavior and Evolution · 2011
Typeletter
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsWestern University
Fundersnot available
KeywordsHippocampusNeuroscienceBiologyCommunicationPsychology

Abstract

fetched live from OpenAlex

when we examined sections from our control birds we saw something very striking. The hippocampus was clearly much larger in chickadees than shown in the atlas. Where the canary atlas showed a smooth curve as the dorsal surface of the brain travelled medially and descended between the hemispheres, the chickadees had a large bump. Previous work by many researchers on the songbird brain had prepared us for the idea that regions of the bird brain could vary between species. Song control nuclei vary in relative size between species and it seemed a likely hypothesis that food-storing birds, which can remember the spatial location of thousands of scattered food caches, might have a hippocampus that was bigger than the hippocampus of non-storing birds. With help from the Long Point Bird Observatory in Ontario, Anthony and I began collecting food-storing and nonstoring birds, sectioning the brains and measuring the relative size of the hippocampus. I contacted my former postdoctoral supervisor at Oxford, John Krebs, and John proposed that his team would collect European species in a parallel project. In Canada, Anthony and I, with help from George Wallace and the banders at Long Point, were able to collect individuals of 23 species from 3 food-storing families (chickadees, nuthatches and jays) and 10 non-food-storing families and subfamilies (wrens, kinglets, thrushes, mimids, starlings, warblers, cardinals, buntings, finchIn 1984–1985, Anthony Vaccarino and I performed a number of experiments on the effects of hippocampal lesions on the accuracy of cache recovery in food-storing black-capped chickadees [Sherry and Vaccarino, 1989]. Like most research, the genesis of this project involved many people. I presented a poster on memory for cache sites at the Neurobiology of Learning and Memory meeting in Irvine, Calif. in 1984 and Larry Squire pointed out to me that the obvious next experiment was to test for hippocampal involvement. I mentioned this idea to my friend Michael Leon at the same meeting, noting that I had no experience doing neurosurgery on 11-gram passerine birds and Michael, with his usual aplomb said that was no problem, there were colleagues at my own institution, the University of Toronto, like Alison Fleming, who could easily teach me the surgical skills. Alison introduced me to Anthony, who was working in Alison’s lab as an undergraduate, and we were on our way. We found that hippocampal lesions severely disrupted the ability of chickadees to find their caches. Fortunately for us, the birds continued to make caches and search for them, they just searched in the wrong places. This work formed part of Anthony’s undergraduate thesis and we published the paper in Behavioral Neuroscience in 1989 [Sherry and Vaccarino, 1989]. We had used the Stokes, Leonard and Nottebohm canary brain atlas [Stokes et al., 1974] as a guide to lesion placement but Published online: August 26, 2011

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.039
GPT teacher head0.297
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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".

Quick stats

Citations14
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueBrain Behavior and EvolutionSame topicPrimate Behavior and EcologyFrench-language works237,207