Spherical spline functions are used to fit the observed data at each recording site and can then provide a continuous projection of the (missing) data at any (intermediate) surface location.7 A fundamental characteristic of these functions is spline flexibility, that is, the degree to which these functions can be bent to best fit the actual data, which affects the smoothness of the continuous interpolation. Spline flexibility is determined by a constant m, which is an integer value greater than 1 (e.g., Carvalhaes and Suppes, 2011; Perrin et al., 1989; cf. Eq. 2 in Kayser and Tenke, 2006a). The most flexible spline function corresponds to m = 2, and increasingly more rigid splines correspond to greater values of m. The top panel of Figure 7 illustrates the effects of spline flexibility for the interpolation of the above data series for the hypothetical locations A-I (cf. Fig. 4) under the additional assumption that these equidistant locations are located on the surface of a sphere. Whereas the most flexible splines of m = 2 and m = 3 (red and green lines) directly
