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The Manipulative Complexity of Lower Paleolithic Stone Toolmaking

Figure 4

Variability explained by principal components of stable hand configurations (data from automatic annotation).

Curves show the cumulative sum of variance explained by increasing numbers of principal components for 5 Acheulean and 3 Oldowan reduction sequences. Principal component analysis is of Acheulean (blue triangles, one for each of the corresponding 5 toolmaking sequences and for each principal component) and Oldowan (red circles, 3 toolmaking sequences) hand configuration data of the core holding (left) hand, when the hammerstone hand was moving faster than 0.5 m/s. Note, that some data points overlap for Principal Component 1 and higher order Principal Components. Inset: Conceptual drawing of Principle Component Analysis (PCA). PCA is a linear transformation that seeks to explain multiple, correlated dimensions (X1, X2) of variation in the data (grey cloud) in terms of uncorrelated dimensions termed principal components (PC1, PC2). This linearly uncorrelated representation can then be used to reduce the dimensionality of the 2-dimensional data, e.g. describing the data set by using only PC1 as 1-dimensional data set (effectively capturing the longitudinal characteristic structure of the data). We do not reduce the dimensionality of the data per se, but use the relative amount of variance explained by each principle component as a characteristic value for the complexity of the data set.

Figure 4