Looking for a Structural Network: Effects of Changing Size and Style on Letter Recognition

The accuracy of identifying backward-masked strings of unrelated letters was measured to test a prediction of a structural network model in which information about the absolute size of letters in a font is factored out from other types of information and is represented by a single size parameter. In this model, variation from block to block in the font size should be handled relatively easily by changing this parameter. In contrast, variation in several aspects of the font style would require changing multiple parameters and should be more difficult. In three experiments, the font was alternated in style, size, size and style, or not at all, at the end of each block of eight trials. The accuracy was lower overall when the font was varied, but contrary to the predictions of the model, variation in size reduced the accuracy of performance as much as did variation in style and variation in size and style.