We examined how spatial smoothing affects the result of multivariate classification analysis using the linear support vector machine (SVM) for decoding columnar-level organization. It has been suggested that the effect of spatial smoothing on decoding performance is minor because smoothing operation is an invertible data transformation and such invertible transformation does not remove information in multivariate pattern. Our theoretical consideration, however, revealed that generalization score (performance for test samples unused during classifier training) was susceptible to non-uniform scaling of input data; SVM classifier became less sensitive to variability in shrunk dimension. This result indicates that spatial smoothing reduces sensitivity of SVM classifier to high spatial frequency pattern so that the effect of smoothing implies the amount of information distributed in spatial frequencies. We also examined the effect of smoothing in an fMRI experiment of decoding ocular dominance responses. The results of group statistic showed that large smoothing reduced decoding accuracies while the smoothing effect at individual subject were not the same for all subjects. These results suggest that spatial smoothing can have major effect on decoding performance and the informative pattern for columnar level decoding resides in higher frequencies on average across subjects while it may distribute multiple frequencies at individual subject level.
Published by Elsevier B.V.