The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition
Fig 1
Description of the lexical categorization model (LCM).
(a) Word-likeness distributions (kernel density estimates), based on the orthographic Levenshtein distance (OLD20 [29]) of words (gray), pseudowords (blue), and consonant strings (yellow) including an example for each category. (b) Probability that a letter string given its OLD20 value is a word (gray line) or a non-word (i.e., either pseudoword or consonant string; blue line). The black line represents the estimated entropy (see Eq 1, Methods section, for more details), which combines the probabilities of being a word or non-word across all possible OLD20 values. The LCM’s central hypothesis is that word-sensitive lvOT activation reflects this entropy function across all possible levels of word-likeness, effectively representing the difficulty of the lexical categorization process postulated for the lvOT. In an attempt to assess the internal stability of the LCM, we estimated LCM simulations based on only subsets of the words, pseudowords, and consonant strings used for the results presented here. In doing so, we found that only about 8% of the lexical items are needed to achieve stable LCM simulations (see S6 Fig).