Information properties of morphologically complex words modulate brain activity during word reading

Abstract

Neuroimaging studies of the reading process point to functionally distinct stages in word recognition. Yet, current understanding of the operations linked to those various stages is mainly descriptive in nature. Approaches developed in the field of computational linguistics may offer a more quantitative approach for understanding brain dynamics. Our aim was to evaluate whether a statistical model of morphology, with well-defined computational principles, can capture the neural dynamics of reading, using the concept of surprisal from information theory as the common measure. The Morfessor model, created for unsupervised discovery of morphemes, is based on the minimum description length principle and attempts to find optimal units of representation for complex words. In a word recognition task, we correlated brain responses to word surprisal values derived from Morfessor and from other psycholinguistic variables that have been linked with various levels of linguistic abstraction. The magnetoencephalography data analysis focused on spatially, temporally and functionally distinct components of cortical activation observed in reading tasks. The early occipital and occipito-temporal responses were correlated with parameters relating to visual complexity and orthographic properties, whereas the later bilateral superior temporal activation was correlated with whole-word based and morphological models. The results show that the word processing costs estimated by the statistical Morfessor model are relevant for brain dynamics of reading during late processing stages.

Keywords: MEG; Morfessor; N400m; computational linguistics; computational modeling; language; morphology; orthography; surprisal.

Figure 1

Experimental stimuli. Examples of the four functionally distinct stimulus categories: words, pseudowords, symbols strings, and (pseudo)words embedded in Gaussian random noise. Each trial consisted of a fixation cross that appeared for 500 ms, followed by a single stimulus that was displayed for 1,500 ms

Figure 2

Source modeling. (a) The final source model for each participant consisted of four temporally, spatially and functionally identified ECDs. The locations of ECDs were identical across subjects, but the orientation was determined individually. (b) The ECD amplitude time courses averaged for each stimulus category and across participants highlight the distinct functional roles of the different ECDs. The dashed vertical line represents the time at which the ECD was localized

Figure 3

Visualization of how item‐level cortical activations are related to linguistic models, with the highest correlations displayed for each studied response type. The time course of activation is averaged in bins of 60 words (lowest, average, highest values of the model). The scatter plots depict the relative source amplitudes (averaged over the time window marked with gray in the time course) for individual words with respect to the linguistic model