Effect of prefrontal cortex damage on resolving lexical ambiguity in text

The function of suppression of context-inappropriate meanings during lexical ambiguity resolution was examined in 25 adults with prefrontal cortex damage (PFCD) localized to the left (N=8), right (N=6), or bilaterally (N=11); and 21 matched Controls. Results revealed unexpected inverse patterns of suppression between PFCD and Control groups, with measures suggesting decreased interference across time in the PFCD group and increased interference in the Control group. The PFCD group, however, had significantly lower accuracy rates for the context-inappropriate condition. Results suggest a loss of the control aspects of inhibitory processes in lexical ambiguity resolution following PFCD. An aging effect on suppression function is also suggested.     

Stability of nonlinear principal components analysis: an empirical study using the balanced bootstrap

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate normality. For nonlinear PCA, however, standard options for establishing stability are not provided. The authors use the nonparametric bootstrap procedure to assess the stability of nonlinear PCA results, applied to empirical data. They use confidence intervals for the variable transformations and confidence ellipses for the eigenvalues, the component loadings, and the person scores. They discuss the balanced version of the bootstrap, bias estimation, and Procrustes rotation. To provide a benchmark, the same bootstrap procedure is applied to linear PCA on the same data. On the basis of the results, the authors advise using at least 1,000 bootstrap samples, using Procrustes rotation on the bootstrap results, examining the bootstrap distributions along with the confidence regions, and merging categories with small marginal frequencies to reduce the variance of the bootstrap results.