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Record W4213429091 · doi:10.1098/rsbl.2021.0371

Social factors and the neurobiology of pathogen avoidance

2022· review· en· W4213429091 on OpenAlexafffund
Martin Kavaliers, Klaus‐Peter Ossenkopp, Cashmeira-Dove Tyson, Indra R. Bishnoi, Elena Choleris

Bibliographic record

VenueBiology Letters · 2022
Typereview
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsUniversity of GuelphWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyNeuroscienceSensory systemAmygdalaDisgustSocial behaviorPsychologySocial psychologyEcology

Abstract

fetched live from OpenAlex

Although the evolutionary causes and consequences of pathogen avoidance have been gaining increasing interest, there has been less attention paid to the proximate neurobiological mechanisms. Animals gauge the infection status of conspecifics and the threat they represent on the basis of various sensory and social cues. Here, we consider the neurobiology of pathogen detection and avoidance from a cognitive, motivational and affective state (disgust) perspective, focusing on the mechanisms associated with activating and directing parasite/pathogen avoidance. Drawing upon studies with laboratory rodents, we briefly discuss aspects of (i) olfactory-mediated recognition and avoidance of infected conspecifics; (ii) relationships between pathogen avoidance and various social factors (e.g. social vigilance, social distancing (approach/avoidance), social salience and social reward); (iii) the roles of various brain regions (in particular the amygdala and insular cortex) and neuromodulators (neurotransmitters, neuropeptides, steroidal hormones and immune components) in the regulation of pathogen avoidance. We propose that understanding the proximate neurobiological mechanisms can provide insights into the ecological and evolutionary consequences of the non-consumptive effects of pathogens and how, when and why females and males engage in pathogen avoidance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.112
GPT teacher head0.397
Teacher spread0.284 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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 routes2
Has abstractyes

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