On the shape and fabric of human history - PubMed (original) (raw)

Comparative Study

On the shape and fabric of human history

Russell D Gray et al. Philos Trans R Soc Lond B Biol Sci. 2010.

Abstract

In this paper we outline two debates about the nature of human cultural history. The first focuses on the extent to which human history is tree-like (its shape), and the second on the unity of that history (its fabric). Proponents of cultural phylogenetics are often accused of assuming that human history has been both highly tree-like and consisting of tightly linked lineages. Critics have pointed out obvious exceptions to these assumptions. Instead of a priori dichotomous disputes about the validity of cultural phylogenetics, we suggest that the debate is better conceptualized as involving positions along continuous dimensions. The challenge for empirical research is, therefore, to determine where particular aspects of culture lie on these dimensions. We discuss the ability of current computational methods derived from evolutionary biology to address these questions. These methods are then used to compare the extent to which lexical evolution is tree-like in different parts of the world and to evaluate the coherence of cultural and linguistic lineages.

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Figures

Figure 1.

Figure 1.

This figure positions linguistic traits on three dimensions. _R_v is the rate of change of vertically inherited cultural traits, _R_h is the rate of horizontal transmission and C is the degree of cultural cohesion (adapted from Gray et al. (2007)). In this hypothetical example, morpho-syntactical traits evolved slowly, are relatively rarely borrowed and are tightly bound together. In contrast, a random sampling of the total lexicon evolves rapidly, has lots of borrowing and reflects many different cultural histories.

Figure 2.

Figure 2.

A quartet containing the taxa i, j, k and l. The path-length from taxon i to taxon j is the sum of branches a, b and c.

Figure 3.

Figure 3.

A split graph showing the results of a NeighborNet analysis of 12 Indo-European languages. The graph shows strong conflicting signal for the positioning of Sranan. The split labelled (a) with the short-dashed line groups Sranan most closely with English, while the other one labelled (b) with the long-dashed line groups Sranan with Dutch and other closely related Germanic languages. Scale bar, 0.01.

Figure 4.

Figure 4.

A split graph showing the results of NeighborNet analyses of the Polynesian lexical data. The network has three main regions: Fijian dialects plus Rotuman, western Polynesian and Eastern Polynesian. There is substantial conflicting signal within each region consistent with the break-up of a dialect chain. Scale bar, 0.1.

Figure 5.

Figure 5.

A split graph showing the results of NeighborNet analyses of the Indo-European lexical data. Scale bar, 0.1.

Figure 6.

Figure 6.

A diagram showing the problem dialect chains cause for the construction of bifurcating trees. The dialects A, B and C are initially all mutually intelligible (note the permeable boundaries between the dialects). Innovations evolve in these dialects (filled circles; filled triangles) and diffuse through the network. However, if a dialect splits off from the network (e.g. the split between C and the other two languages), and this diffusion is only partially complete, then conflicting character histories can result. The filled circle characters support topology 1, whereas the filled triangle characters support topology 2. So, under the Dialect Chain/Network-Breaking model, areas where dialect chains were present should be poorly resolved in a phylogenetic analysis, and are better represented by a network diagram rather than a tree.

Figure 7.

Figure 7.

Split graphs showing the results of NeighborNet analyses of the lexical and typological data. The analyses used Hamming distances and splits were filtered to a threshold of 0.001. For Austronesian basic vocabulary, the average delta score was 0.33 and the average _Q_-residual = 0.0020. The average delta score for Austronesian typological data was 0.44 and the average _Q_-residual = 0.05. The respective figures for Indo-European were 0.21 and 0.001 (basic vocabulary) and 0.40 and 0.04 (typology). Known subgroups within each language family are colour-coded. Scale bar, 0.01.

Figure 8.

Figure 8.

Maximum clade credibility language tree for the 11 societies analysed by Rogers et al. The tree is constructed from basic vocabulary data with the analyses constrained on the basis of phonological and morphological innovations. To match languages to cultures, we assumed that Societies = Tahitian, Australs = Rurutuan, Cooks = Rarotongan.

Figure 9.

Figure 9.

Histograms showing the distribution of likelihood scores for (a) basic vocabulary, (b) functional aspects of canoe design, (c) symbolic aspects of canoe design and (d) randomization of the canoe data on the language tree. Likelihood scores close to zero indicate a good fit. The basic vocabulary data fit the tree the best (mean = −2.89, median = −2.89, s.d. = 2.31). Both the functional and symbolic aspects of canoe design are close to the random distribution (functional: mean = −6.64, median = −7.36, s.d. = 1.28; symbolic: mean = −6.13, median = −6.34, s.d. = 1.37; random: mean = −6.30, median = −6.92, s.d. = 1.45).

Figure 10.

Figure 10.

Split graphs showing the results of NeighborNet analyses of the (a) functional and (b) the symbolic aspects of canoe design. For functional traits, the average delta score was 0.46 and the average _Q_-residual = 0.03. For symbolic traits, the average delta score was 0.37 and the average _Q_-residual = 0.05. Scale bar, 0.01.

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