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Record W4280610967 · doi:10.1016/j.isci.2022.104412

Neural activation associated with outgroup helping in adolescent rats

2022· article· en· W4280610967 on OpenAlexaff
Jocelyn Breton, Jordan S. Eisner, Vaidehi S. Gandhi, Natalie Musick, Aileen Zhang, Kimberly L. P. Long, Olga S. Perloff, Kelsey Hu, Chau M. Pham, Pooja Lalchandani, Matthew Barraza, Ben Kantor, Daniela Kaufer, Inbal Ben-Ami Bartal

Bibliographic record

VenueiScience · 2022
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsProsocial behaviorIngroups and outgroupsOutgroupEmpathyPsychologyDevelopmental psychologyAmygdalaSocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

Prosocial behavior, helping others in need in particular, occurs preferentially in response to the perceived distress of one's own group members or ingroup. To investigate the development of ingroup bias, neural activity during a helping test was analyzed in adolescent and adult rats. Although adults selectively released trapped ingroup members, adolescent rats helped both ingroup and outgroup members, suggesting that ingroup bias emerges in adulthood. Analysis of brain-wide neural activity, indexed by expression of the early-immediate gene c-Fos, revealed increased activity for ingroup members across a broad set of regions previously associated with empathy. Adolescents showed reduced hippocampal and insular activity and increased orbitofrontal cortex activity compared to adults. Non-helper adolescents demonstrated increased amygdala connectivity. These findings demonstrate that biases for group-dependent prosocial behavior develop with age in rats and suggest that specific brain regions contribute to prosocial selectivity, pointing to possible targets for the functional modulation of ingroup bias.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.337
Teacher spread0.277 · 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
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

Citations26
Published2022
Admission routes1
Has abstractyes

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