The role of letter features in visual-word recognition: Evidence from a delayed segment technique

Abstract

Do all visual features in a word's constituent letters have the same importance during lexical access? Here we examined whether some components of a word's letters (midsegments, junctions, terminals) are more important than others. To that end, we conducted two lexical decision experiments using a delayed segment technique with lowercase stimuli. In this technique a partial preview appears for 50ms and is immediately followed by the target item. In Experiment 1, the partial preview was composed of terminals+junctions, midsegments+junctions, or midsegments+terminals - a whole preview condition was used as a control. Results only revealed an advantage of the whole preview condition over the other three conditions. In Experiment 2, the partial preview was composed of the whole word except for the deletion of midsegments, junctions, or terminals - we again employed a whole preview condition as a control. Results showed the following pattern in the latency data: whole preview=delay of terminals<delay of junctions<delay of midsegments. Thus, some components of a word's constituent letters are more critical for word identification than others. We examine how the present findings help adjust current models of visual word identification or develop new ones.

Can I order a burger at rnacdonalds.com? Visual similarity effects of multi-letter combinations at the early stages of word recognition.

“…Indeed, it has been claimed that one of the processing deficits of individuals with dyslexia is at binding the visual features of letters and words (see Pammer, 2014). Although an answer to the binding problem in printed word recognition would undeniably be beyond the scope of the present study, the high degree of perceptual similarity between docurnent and document at the early stages of word recognition suggest that, as occurs with other visual objects, Gestalt principle of good continuation of form also apply to letter/word recognition (i.e., rn→m; see Rosa, Perea, & Enneson, 2016, for evidence of this principle when deleting visual features from letters in printed word recognition; see also Pelli et al, 2009, for discussion of Gestalt principles in letter identification). Further research should be conducted to determine, in detail, the role of visual similarity with multi-letter homoglyphs during visual word recognition and reading.…”

Do Diacritical Marks Play a Role at the Early Stages of Word Recognition in Arabic?

“…A limitation of these models is the lack of a detailed specification of the “letter feature” level and how this level is mapped onto the “abstract” letter level—for instance, current computational models of visual word recognition still use the unrealistic uppercase font devised by Rumelhart and Siple (1974) (see Blais et al, 2009; Davis, 2010; Rosa et al, 2016, for discussion). The special characteristics of the Arabic alphabet raises a number of questions (e.g., position-dependent allography, diacritical marks, and its cursive nature) that may help implement a more comprehensive model of visual word recognition in alphabetic languages.…”

Is nevtral NEUTRAL? Visual similarity effects in the early phases of written-word recognition

“…As Davis (2010) indicated, future implementations of these models should incorporate a more sophisticated letter coding scheme to encode letter representations from their visual features. Three of the main challenges for modelers are how to specify (1) the most diagnostic visual elements of letters (e.g., lines, curves, intersections, terminations) in the initial phases of word processing (see Blais et al, 2009;Rosa, Perea, & Enneson, 2016, for discussion); (2) how these visual features are dynamically weighted (see Wiley, Wilson, & Rapp, 2016) 3 ; and (3) how visual information is mapped onto abstract representations (see Grainger et al, 2016). Although a thorough description of these questions would be beyond the scope of this study, it is clear that additional research is needed to help determine the time course of visual similarity effects across letters in during written-word recognition.…”