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. 2011 Mar 30;6(3):e18239. doi: 10.1371/journal.pone.0018239

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Alex Mesoudi.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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(A) Time series of mean cultural complexity over time in Model 2 at different population sizes, N, under the assumption of direct bias. (B) Relationship between maximum cultural complexity _max and N for direct bias, indirect bias and unbiased transmission, and with N plotted on a logarithmic scale. For direct bias, maximum cultural complexity _max increases logarithmically with N up to N = 100 (line is a logarithmic best-fit with R² = 0.991 for N≤100), after which it plateaus. For indirect bias, a similar logarithmic increase followed by a plateau occurs to that under direct bias, but the values of _max are lower and the plateau occurs at higher values of N (around N = 1000; line is a logarithmic best-fit with R² = 0.994 for N≤1000). For unbiased transmission, N has no effect on _max. Parameters: c_i = 10, c_s = 5, λ = 1000, µ_i = µ_s = 0, all results are the average of 100 independent runs.

Inline graphic — (A) Time series of mean cultural complexity over time in Model 2 at different population sizes, N, under the assumption of direct bias. (B) Relationship between maximum cultural complexity _max and N for direct bias, indirect bias and unbiased transmission, and with N plotted on a logarithmic scale. For direct bias, maximum cultural complexity _max increases logarithmically with N up to N = 100 (line is a logarithmic best-fit with R² = 0.991 for N≤100), after which it plateaus. For indirect bias, a similar logarithmic increase followed by a plateau occurs to that under direct bias, but the values of _max are lower and the plateau occurs at higher values of N (around N = 1000; line is a logarithmic best-fit with R² = 0.994 for N≤1000). For unbiased transmission, N has no effect on _max. Parameters: c_i = 10, c_s = 5, λ = 1000, µ_i = µ_s = 0, all results are the average of 100 independent runs.