Monday, November 14, 2016

Brain ‘reads’ sentence same way in 2 languages

Ying Yanga, Jing Wanga, Cyntia Bailerb, Vladimir Cherkasskya, Marcel Adam Justa,  Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function.  NeuroImage  Available online 19 October 2016

An English-based model predicted the fMRI patterns for Portuguese sentences.
A cross-language model predicted both monolinguals’ and bilinguals’ activation.
Sentences from two languages were decodable from the same set of 38 brain locations.

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.

When the brain “reads” or decodes a sentence in English or Portuguese, its neural activation patterns are the same, researchers report.

Published in NeuroImage, the study is the first to show that different languages have similar neural signatures for describing events and scenes. By using a machine-learning algorithm, the research team was able to understand the relationship between sentence meaning and brain activation patterns in English and then recognize sentence meaning based on activation patterns in Portuguese.
The findings can be used to improve machine translation, brain decoding across languages, and, potentially, second language instruction.

“This tells us that, for the most part, the language we happen to learn to speak does not change the organization of the brain,” says Marcel Just, professor of psychology at Carnegie Mellon University.

“Semantic information is represented in the same place in the brain and the same pattern of intensities for everyone. Knowing this means that brain to brain or brain to computer interfaces can probably be the same for speakers of all languages,” Just says…
With 67 percent accuracy, the model predicted which sentences were read in Portuguese. The resulting brain images showed that the activation patterns for the 60 sentences were in the same brain locations and at similar intensity levels for both English and Portuguese sentences.

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Additionally, the results revealed the activation patterns could be grouped into four semantic categories, depending on the sentence’s focus: people, places, actions, and feelings. The groupings were very similar across languages, reinforcing the organization of information in the brain is the same regardless of the language in which it is expressed.

“The cross-language prediction model captured the conceptual gist of the described event or state in the sentences, rather than depending on particular language idiosyncrasies. It demonstrated a meta-language prediction capability from neural signals across people, languages, and bilingual status,” says Ying Yang, a postdoctoral associate in psychology and first author of the study.

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