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The seminal work of Oliver Selfridge (Selfridge & Neisser, 1960) laid the foundations for a cognitive theory of letter perception. In Selfridge’s “pandemonium” model, letter identification is achieved by hierarchically organized layers of feature and letter detectors. Support for such a hierarchical organization was provided at that time by neurophysiological studies of the cat visual cortex, and over the years, a general consensus has developed in favor of a generic feature‐based approach to letter perception. One key guiding principle here is that isolated letter perception is just a simplified case of visual object recognition (e.g., Pelli et al., 2006). Our knowledge of visual object perception, much of which has been derived from neurophysiological studies of nonhuman primates, should therefore inform our knowledge of letter perception in humans, as exemplified in the model presented in Figure 3.2. This is a blueprint for a model of letter perception (Grainger et al., 2008) adapted from a classic account of object recognition (Riesenhuber & Poggio, 1999; see Dehaene et al., 2005, for an extension of this approach to visual word recognition).

Evidence concerning the nature of letter features comes from studies using a method known as the confusion matrix. In a typical experiment used to generate a confusion matrix, isolated letters are presented in data‐limited conditions (brief exposures and/or low luminance and/or masking). Participants are asked to name the letters, and erroneous letter reports are noted. Error rates (e.g., reporting F when E was presented) are hypothesized to reflect visual similarity driven by shared features, and therefore an analysis of the pattern of letter confusions is expected to reveal the set of features used to identify letters. There are more than 70 published studies on letter confusability (Mueller & Weidemann, 2012). These have formed the basis of concrete proposals of lists of features for letters of the Roman alphabet, mainly consisting of lines of different orientation and curvature (see Fiset et al., 2008, for an alternative method for defining letter features, and Grainger et al., 2008, for a discussion of this technique).

Figure 3.2 Adaptation of Riesenhuber and Poggio’s (1999) model of object identification to the case of letter perception (Grainger et al., 2008). Information about simple visual features (lines of different orientation at precise locations in the visual field) extracted from the visual stimulus is progressively pooled across different locations (complex cells) and feature combinations (composite cells) as one moves up the processing hierarchy.

Grainger et al., 2008/With permission of Elsevier.