PCA of pairwise distances.

To explore which of the pairwise distances contributed to the underlying observed effect of prosthesis categorisation we ran a data-driven analysis (PCA) on the 5 distances of the one-handed group. Values in the table are the weights given to each distance within a component. The first component shows a ‘main effect’ of interindividual differences across participants, in which some individuals have overall larger distances than others across all condition pairs. In our calculated indices, we control for this effect by normalising the individual’s selectivity indices by their Hands ↔ Tools distance (see ‘Methods‘). The second component explains almost half of the remaining variance (after accounting for the interindividual differences in component 1). In the second component, individuals showing greater distances between the active prostheses and the tool condition also show greater similarity between the active prosthesis and the cosmetic prosthesis conditions. In other words, when the active prosthesis condition moves away from the tool category, it also tends to get closer to the cosmetic prostheses (as can be seen by the high weights and opposite signs of these 2 distances in the second component). This data-driven analysis provides further support for the hypothesised categorical shift of prosthesis representation. PCA, principle component analysis (XLSX)为探究哪些配对距离对假肢分类的观测效应存在潜在贡献，我们针对单侧手组的5项距离指标开展了数据驱动分析（主成分分析，Principal Component Analysis，简称PCA）。表格中的数值为各成分内各项距离对应的权重。第一个成分体现了被试间个体差异的"主效应"，即部分被试在所有条件配对下的整体距离均大于其他被试。在计算指标时，我们通过以被试的"手部↔工具"距离对个体选择性指标进行归一化，以此控制该主效应（详见"方法"部分）。第二个成分可解释第一成分校正个体差异后剩余方差的近一半。在第二个成分中，主动假肢与工具类条件间距离更大的被试，其主动假肢与装饰性假肢条件间的相似性也更高。换言之，当主动假肢条件偏离工具类别时，其往往也会更趋近于装饰性假肢类别——这可通过第二个成分中这两项距离的高权重与相反符号得以验证。该数据驱动分析为假肢表征的假设性类别偏移提供了进一步支撑。本分析采用的主成分分析相关数据以XLSX格式存储。