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).
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).
Fig 3.
fMRI-based Evaluation of the LCM model: fMRI whole brain analyses.
(a, b, d, e) Significant correlation results between BOLD signals and LCM simulations modeled as a single, continuous predictor in Study 1 (a) and Study 3 (d), and between BOLD activation and word-likeness represented by the OLD20 as a single, continuous predictor in Study 2 (b) and Study 3 (e). (c, f) Significantly increased BOLD signals for words as compared to non-words in Study 2 (c) and Study 3 (f). For OLD20 results and word > non-word contrasts of Study 1, see S2 Fig). Thresholds for all whole brain analyses: voxel level: p < .001 uncorrected (cluster-forming); cluster level: p < .05 family-wise error corrected. No other regions than those displayed were significant.
Fig 4.
Comparison of BOLD activation across studies and contrasts.
Significant LCM activation clusters in the lvOT across studies show some overlap, but no overlap to word-likeness and lexicality contrasts. Thresholds for all whole brain analyses: voxel level: p < .001 uncorrected (cluster-forming); cluster level: p < .05 family-wise error corrected.
Table 1.
Significant activation clusters from the fMRI evaluation with respective anatomical labels (most likely regions from the Juelich and Harvard-Oxford atlases including % overlap), cluster size (in mm3), and peak voxel coordinates (MNI space).
Fig 5.
Simulations for Words (W), pseudowords (PW), and consonant strings (CS; all medians) used in Study 1 from (a) the lexical categorization model (LCM), (b) the interactive account model (IA), and (c) the effort and engagement model (E&E). (d) LCM, (e) IA, and (f) E&E simulations for Study 2. (g) LCM, (h) IA, and (i) E&E simulations for Study 3. Empirically observed lvOT ROI activation for W, PW, CS, and scrambled letters (SL), extracted from the same peak voxel region, i.e., defined in Study 1, of interest (ROI) of (j) Study 1, and (k) Study 2. (l) For the animal decision and catch trial detection task of Study 3 the ROI data was extracted from the peak voxel of Study 3. We present the percent signal change, in arbitrary units, for each condition, including the variance across participants (horizontal line represents the median; box +- 1 standard deviation; whiskers +- 2 standard deviations). Besides, we present model comparisons for the LCM, E&E, and IA models of Study 1 (S1) in (m), of Study 2 (S2) in (n), and of Study 3 (S3) in (o). We show the mean difference between the model simulated contrasts and the observed contrast differences from the ROI data. We standardize these model comparisons via the standard deviations of the observed data (SD). I.e., the difference between simulated and observed contrast differences in standard deviations of the observed data. For Study 1, we summarize three contrasts, which can be inspected in detail in (p), Study 2 includes only one contrast presented in (n), and Study 3 combines six contrasts shown in detail in (q). In the single contrast figures (pq), the solid black line and dot show the mean observed difference (across all participants), dashed lines +- 1 standard deviation, and dotted lines +-2 standard deviations also from the observed differences. Green dots show the LCM simulated contrast estimate, blue dots the IA contrast estimate, and red dots the E&E contrast estimates. In (q) the left panel represents the animal detection and the right panel the catch trial detection task. (r) The linear relationship between lvOT ROI activation and LCM-simulations in study 3 separated for words and non-words and the two tasks conducted in the study.
Fig 6.
Behavioral training of lexical categorization: Paradigm and results. (a) Temporal structure of the training study, including the three training sessions on three separate days, as well as pre- and post-training assessments of reading speed (based on the Salzburger Lese Screening/SLS; see Methods). (b) Lexical decision paradigm performed by the participants during the three training sessions, including feedback screens. (c, d) Lexical decision response times in relation to (c) word-likeness (OLD20; i.e., high OLD20 represents low word-likeness; see Fig 1) and (d) separated for stimulus categories (W: Words; PW: Pseudowords; CS: Consonant strings) from 76 non-native learners of German across three sessions. For comparison, data for a group of 48 native German speakers (i.e., typical readers) who completed the lexical decision task with the same stimuli are presented. (e) Predicted response time reduction with session, i.e., from session 1 to 2 (green) and from session 1 to 3 (blue), extracted from fitted linear mixed models using the remef function to remove all but the variance which reflects the session by entropy interaction (for details see the Methods section). (f) Change of reading speed (pre-post; see Methods) correlated with the individual training effect (quantified as the estimate for the LCM by session interaction from a linear mixed model with random slopes). The boxplot (right panel) shows the overall increase in reading speed on the pre-post test. For all boxplots, the horizontal lines represent the median, the boxes represent +/- 1 standard deviation, and the whiskers represent +/- 2 standard deviations.
Fig 7.
Schematic description of processes during visual word recognition as assumed in the lexical categorization model. The figure includes (i) word-likeness estimations in posterior visual-perceptual regions, (ii) lexical categorization in the left ventral occipito-temporal cortex (lvOT), and (iii) the extraction of word meaning in anterior regions including temporal and inferior frontal cortex. The lexical categorization process implemented in lvOT is schematically represented by the uncertainty (entropy), to visualize the LCM’s assumption that higher degrees of categorization uncertainty–in sections of intermediate word-likeness (yellow)–may require further elaborative processing to reach a lexical categorization.