The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition
Fig 2
Evaluation based on simulations: LCM and model comparisons based on simulations of lvOT benchmark effects from the fMRI literature.
Model comparisons involve the lexicon model, local combination detector model (CD), effort and engagement model (E&E), the interactive account model (IA; for implementations and detailed simulations see S1 Text), and the cognitive Dual Route model, specifically its orthographic lexicon (DRC Lex.) and its grapheme to phoneme conversion route (DRC GPR; for detailed simulations see S1 Fig). Simulated lvOT activation (in arbitrary units: min = 0; max = 6) for all groups of letter strings is presented using bar graphs depicting their respective mean activation. In addition, horizontal black bars indicate significant differences of the simulation results between letter string categories, as derived from linear models (Bonferroni corrected). LCM simulated lvOT activation is presented, from left to right, (a) for words (W), pseudowords (PW), consonant strings (CS), pseudohomophones (PH), (b) words and pseudowords matched on number of syllables, number of Coltheart’s orthographic neighbors, frequency of the highest frequency neighbor, initial bigram frequency, final bigram frequency, and summated bigram frequency (mW, mPW), and (c) the word similarity effect comparing words (cmW: comparative matched words) to non-words with high word similarity (matched on quadrigram frequency; hWS), to non-words with intermediate word similarity (matched on bigram frequency; iWS), and, to non-words with low word similarity (lWS). In addition, (d) the effect of log. transformed word frequency (for all words and pseudowords as tested in [14]) and (e) log. transformed bigram frequency are presented as scatter plots with a linear regression line. Note that for bigram frequency, also a non-linear regression line is shown. In (d) and (e), each dot represents one letter string, the more saturated the blue gets, the more letter strings are included. See text for more detailed description of the replicated benchmark effects including the specific stimulus sets used and references to the original studies. (f) Qualitative model comparisons showing the sum of correctly simulated stimulus differences (orange bars) and all correct minus all incorrectly simulated effects excluding null effects (cyan bars). The LCM and the IA were able to correctly simulate all contrasts. (g) Correlation matrix of all model parameters included in the model comparison, showing that simulations of the non-linear models, i.e., LCM, E&E, and IA, were substantially correlated (all r’s > .4).