The influence of addition and deletion neighbors on visual word identification was investigated in four experiments. Experiments 1 and 2 used Spanish stimuli. In Experiment 1, lexical decision latencies were slower and less accurate for words and nonwords with higher-frequency deletion neighbors (e.g., jugar in juzgar), relative to control stimuli. Experiment 2 showed a similar interference effect for words and nonwords with higher-frequency addition neighbors (e.g., conejo, which has the addition neighbor consejo), relative to control stimuli. Experiment 3 replicated this addition neighbor interference effect in a lexical decision experiment with English stimuli. Across all three experiments, interference effects were always evident for addition/deletion neighbors with word-outer overlap, usually present for those with word-initial overlap, but never present for those with word-final overlap. Experiment 4 replicated the addition/deletion neighbor inhibitory effects in a Spanish sentence reading task in which the participants' eye movements were monitored. These findings suggest that conventional orthographic neighborhood metrics should be redefined. In addition to its methodological implications, this conclusion has significant theoretical implications for input coding schemes and the mechanisms underlying word recognition.
Keywords: visual word recognition, lexical inhibition, neighborhood effects, orthographic input coding, SOLAR modelIn the past decade, cracking the orthographic code has become a key question for researchers in visual word recognition and reading (see Grainger, 2008, for a recent review). The origins of this quest can be traced 3 decades ago, when Coltheart, Davelaar, Jonasson, and Besner (1977) reported an experiment that has come to be considered a classic study in the field of visual word identification. To investigate lexical access procedures, Coltheart and colleagues manipulated an orthographic similarity metric that they labeled "N". The N metric had previously been suggested by Landauer and Streeter (1973) as a measure of the number of close "neighbors" of a stimulus, and was computed by counting the number of words that can be created by changing a single letter of the stimulus. For example, N ϭ 10 for the word river (which has an orthographic neighborhood that includes the words diver, liver, rover, rider, and rivet), whereas N ϭ 1 for the word drive, as only a single word (drove) can be formed by substituting a single letter. In a lexical decision task, Coltheart et al. (1977) found that N had no effect on the latency of "Yes" responses, but that "No" responses were significantly slower to large-N nonwords than to small-N nonwords. This was interpreted as evidence against a serial search model and in favor of a parallel access model like Morton's (1970) logogen model. It was argued that fixating a written word leads to the automatic activation of its neighbors, and that this lexical activation made it harder to reject large-N nonwords than small-N nonwords.In the years since Colth...