Ba YaKa adolescent boys nominate accessible adult men as preferred spear hunting models
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Lew-Levy, Sheina [1] ; Milks, Annemieke [3] ; Kiabiya Ntamboudila, Francy [4] ; Broesch, Tanya [1] ; Kline, Michelle A. [2]
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[1] Simon Fraser University
Simon Fraser University
Canadá
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[2] Brunel University London
Brunel University London
Hillingdon, Reino Unido
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[3] Institute of Archaeology of University College London
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[4] Faculté des Lettres, Arts, et Sciences Humaines of Marien Ngouabi University
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- Localización: Current anthropology: A world journal of the sciences of man, ISSN 0011-3204, Nº. 5, 2021, págs. 631-640
- Idioma: inglés
- Títulos paralelos:
- Les adolescents Ba YaKa nomment les hommes adultes qui leurs sont accessibles comme modèles préférés pour l’apprentissage de la chasse à la lance
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Resumen
Humans are selective social learners. In a cultural landscape with many potential models, learners must balance the cost associated with learning from successful models with learning from accessible ones. Using structured interviews, we investigate the model selection biases of Congolese BaYaka adolescent boys learning to hunt with spears (n p 24; mean age [mage] p 15.79 years; range, 12–20 years). Results from social relations models suggest that adolescents nominated accessible adult men (closely related kin and neighbors) as preferred spear hunting models. Direct cues for success were not strong predictors for adolescent nomination in the statistical models, despite learners justifying model selection according to teaching and spear hunting skill. Indirect cues including body mass index, age, and cross-domain prestige were weak predictors for adolescent nomination. We interpret these findings as suggesting that BaYaka spear hunting knowledge is widely shared in the community, with all adult men participating in spear hunting and therefore having the requisite experience to transmit this skill. This supports previous findings that in egalitarian societies with low rates of role specialization, prestige has limited importance for cross-domain learning.
