Noise methods in pattern perception

This experiment investigated 4 methods of inducing visual noise at 7 levels of noise. Fortysix Ss were presented 4800 sets of 3 pattems, with the task of judging which of 2 noisy patterns was more similar to the prototype pattern. The noisy patterns were generated by adding, moving, deleting or both adding and deleting elements of the prototype. All possible comparisons of each noise method were made at each noise level and the results plotted. The results show that the various methods of introducing noise do not produce identical results, and that the similarity assessment is dependent on noise koel. Explanations are offered to account for the results.     

Geometrical representation of two methods of linear least squares multiple correlation

Geometrical properties and relationships of the Doolittle and square root methods of multiple correlation, as represented in the variable subspace of an orthogonal person space, are shown. The method of representation is also useful for depicting zero-order and partial correlations, as well as for the more general problem of the combination of variables.     

Some multiple correlation and predictor selection methods

The Doolittle, Wherry-Doolittle, and Summerfield-Lubin methods of multiple correlation are compared theoretically as well as by an application in which a set of predictors is selected. Wherry's method and the Summerfield-Lubin method are shown to be equivalent; the relationship of these methods to the Doolittle method is indicated. The Summerfield-Lubin method, because of its compactness and ease of computation, and because of the meaningfulness of the interim computational values, is recommended as a convenient least squares method of multiple correlation and predictor selection.