Literature DB >> 24381294

Brain-based translation: fMRI decoding of spoken words in bilinguals reveals language-independent semantic representations in anterior temporal lobe.

João Correia1, Elia Formisano, Giancarlo Valente, Lars Hausfeld, Bernadette Jansma, Milene Bonte.   

Abstract

Bilinguals derive the same semantic concepts from equivalent, but acoustically different, words in their first and second languages. The neural mechanisms underlying the representation of language-independent concepts in the brain remain unclear. Here, we measured fMRI in human bilingual listeners and reveal that response patterns to individual spoken nouns in one language (e.g., "horse" in English) accurately predict the response patterns to equivalent nouns in the other language (e.g., "paard" in Dutch). Stimuli were four monosyllabic words in both languages, all from the category of "animal" nouns. For each word, pronunciations from three different speakers were included, allowing the investigation of speaker-independent representations of individual words. We used multivariate classifiers and a searchlight method to map the informative fMRI response patterns that enable decoding spoken words within languages (within-language discrimination) and across languages (across-language generalization). Response patterns discriminative of spoken words within language were distributed in multiple cortical regions, reflecting the complexity of the neural networks recruited during speech and language processing. Response patterns discriminative of spoken words across language were limited to localized clusters in the left anterior temporal lobe, the left angular gyrus and the posterior bank of the left postcentral gyrus, the right posterior superior temporal sulcus/superior temporal gyrus, the right medial anterior temporal lobe, the right anterior insula, and bilateral occipital cortex. These results corroborate the existence of "hub" regions organizing semantic-conceptual knowledge in abstract form at the fine-grained level of within semantic category discriminations.

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Mesh:

Year:  2014        PMID: 24381294      PMCID: PMC6608172          DOI: 10.1523/JNEUROSCI.1302-13.2014

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  23 in total

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7.  Using precise word timing information improves decoding accuracy in a multiband-accelerated multimodal reading experiment.

Authors:  An T Vu; Jeffrey S Phillips; Kendrick Kay; Matthew E Phillips; Matthew R Johnson; Svetlana V Shinkareva; Shannon Tubridy; Rachel Millin; Murray Grossman; Todd Gureckis; Rajan Bhattacharyya; Essa Yacoub
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Review 10.  Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations.

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