ArticlePLoS computational biology2025
Memory, innovation and vertical learning.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
One of the most controversial and actively debated questions about both human and non-human animal culture concerns its relationship with adaptation - under what circumstances might we expect culture, and the ability to learn socially from others, to be beneficial, and favored by natural selection? Existing theory posits that the benefit of social learning depends on the rate at which the environment changes, and recent work has shown that this relationship is mediated by how much information an individual can retain over time - by memory. Based on extensive ethnographic research, vertical learning-social learning from parent to offspring-appears to be an extremely salient and important type of social learning. Here we develop large-scale agent-based simulation models to investigate the evolutionary relationship between vertical social learning in particular and the memory and retention of cultural information. We show that the benefit of vertical learning depends on how quickly those information is forgotten and on the exact way in which individuals innovate. This work points to the importance of a complex interplay between the size of cultural repertoires, the benefits of cultural preservation, changing selective environments, and the mechanism of innovation - none of which can be fully understood in isolation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.