Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function.

The aim of the study was to test the cross-language generative capability of a model that predicts neural activation patterns evoked by sentence reading, based on a semantic characterization of the sentence. In a previous study on English monolingual speakers (Wang et al., submitted), a computational model performed a mapping from a set of 42 concept-level semantic features (Neurally Plausible Semantic Features, NPSFs) as well as 6 thematic role markers to neural activation patterns (assessed with fMRI), to predict activation levels in a network of brain locations. The model used two types of information gained from the English-based fMRI data to predict the activation for individual sentences in Portuguese. First, it used the mapping weights from NPSFs to voxel activation levels derived from the model for English reading. Second, the brain locations for which the activation levels were predicted were derived from a factor analysis of the brain activation patterns during English reading. These meta-language locations were defined by the clusters of voxels with high loadings on each of the four main dimensions (factors), namely people, places, actions and feelings, underlying the neural representations of the stimulus sentences. This cross-language model succeeded in predicting the brain activation patterns associated with the reading of 60 individual Portuguese sentences that were entirely new to the model, attaining accuracies reliably above chance level. The prediction accuracy was not affected by whether the Portuguese speaker was monolingual or Portuguese-English bilingual. The model's confusion errors indicated an accurate capture of the events or states described in the sentence at a conceptual level. Overall, the cross-language predictive capability of the model demonstrates the neural commonality between speakers of different languages in the representations of everyday events and states, and provides an initial characterization of the common meta-language neural basis.

Commonalities and differences in the neural representations of English, Portuguese, and Mandarin sentences: When knowledge of the brain-language mappings for two languages is better than one.

This study extended cross-language semantic decoding (based on a concept's fMRI signature) to the decoding of sentences across three different languages (English, Portuguese and Mandarin). A classifier was trained on either the mapping between words and activation patterns in one language or the mappings in two languages (using an equivalent amount of training data), and then tested on its ability to decode the semantic content of a third language. The model trained on two languages was reliably more accurate than a classifier trained on one language for all three pairs of languages. This two-language advantage was selective to abstract concept domains such as social interactions and mental activity. Representational Similarity Analyses (RSA) of the inter-sentence neural similarities resulted in similar clustering of sentences in all the three languages, indicating a shared neural concept space among languages. These findings identify semantic domains that are common across these three languages versus those that are more language or culture-specific.