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Record W2135621511 · doi:10.1242/jeb.112144

Building the social brain, one task at a time

2015· article· en· W2135621511 on OpenAlexaff
Constance M. O’Connor

Bibliographic record

VenueJournal of Experimental Biology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTask (project management)Group livingSocial groupVariety (cybernetics)Brain sizeSocial complexityPsychologyEcologyBiologySocial psychologySociologyComputer scienceSocial scienceMedicineManagement

Abstract

fetched live from OpenAlex

Is it easier to live in a small town than in a big city? Living in a big city requires frequent interactions with many different individuals and responding appropriately to a huge variety of social situations, while living in a small town requires fewer types of interactions, with fewer individuals. The complexity of living in big groups has led to the ‘social brain hypothesis’, which suggests that as group size increases, brain size should increase to meet these social demands. However, the ‘task specialization hypothesis’ is a counter theory, which suggests that as social group size increases individuals start to perform increasingly specific tasks. According to this theory, someone living in a small town is more likely to be a jack-of-all-trades, while a big-city dweller will be more likely to have a highly specialized job. Thus, the ‘task specialization hypothesis’ suggests that as social group size increases, the brain should become increasingly specialized, rather than becoming larger overall.Sabrina Amador-Vargas, from the University of Texas at Austin and the Smithsonian Tropical Research Institute, with colleagues from both institutions and the University of Arizona, decided to investigate the ‘social brain hypothesis’ versus the ‘task specialization hypothesis’ in acacia ants (Pseudomyrmex spinicola). These ants live in social groups with a single queen and a host of workers, and have a symbiotic relationship with acacia trees. Each social group lives within a tree and workers chase off animals that try to feed on the acacia leaves. In return, the trees provide the ants with ‘Beltian bodies’ – small packets of food that the ants find irresistible.In wild ant colonies in Panamá, the researchers estimated colony size by marking and recapturing worker ants, and counting the number of entrance holes on the host tree. The scientists then assessed task specialization by counting how many of the worker ants performed the same task day after day. They also observed how workers responded to simulated foraging or defensive tasks. The researchers predicted that as colony size increased, worker ants would be less likely to switch jobs and more likely to exclusively perform either foraging tasks on the leaves or defensive tasks on the trunks. They further predicted that as social group size increased, the foraging ants would be more likely to ignore an intruder, while the defensive ants would be more likely to ignore Beltian bodies. After conducting behavioural observations, the researchers collected worker ants and measured the volumes of different brain areas.They found that as colony size increased, task specialization also increased. In larger social groups, worker ants were more likely to perform exclusively either defensive or foraging tasks, rather than switching between jobs. Importantly, the scientists showed that specific brain regions related to learning and memory became larger in foraging workers as social group increased, but smaller in defensive workers as social group increased. Thus, these results support the ‘task specialization hypothesis’, where ants living in small groups are more general jacks-of-all-trades and ants from big groups become more specialized for a specific job. The social brain isn't necessarily a bigger brain, but it is a unique brain that is built one increasingly specialized task at a time.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.008
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.023
GPT teacher head0.320
Teacher spread0.297 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2015
Admission routes1
Has abstractyes

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