Monthly
208 pp. per issue
8 1/2 x 11, illustrated
ISSN
0898-929X
E-ISSN
1530-8898
2014 Impact factor:
4.69

Journal of Cognitive Neuroscience

November 2015, Vol. 27, No. 11, Pages 2197-2214
(doi: 10.1162/jocn_a_00853)
© 2015 Massachusetts Institute of Technology
Activation Patterns throughout the Word Processing Network of L1-dominant Bilinguals Reflect Language Similarity and Language Decisions
Article PDF (1.2 MB)
Abstract

A crucial aspect of bilingual communication is the ability to identify the language of an input. Yet, the neural and cognitive basis of this ability is largely unknown. Moreover, it cannot be easily incorporated into neuronal models of bilingualism, which posit that bilinguals rely on the same neural substrates for both languages and concurrently activate them even in monolingual settings. Here we hypothesized that bilinguals can employ language-specific sublexical (bigram frequency) and lexical (orthographic neighborhood size) statistics for language recognition. Moreover, we investigated the neural networks representing language-specific statistics and hypothesized that language identity is encoded in distributed activation patterns within these networks. To this end, German–English bilinguals made speeded language decisions on visually presented pseudowords during fMRI. Language attribution followed lexical neighborhood sizes both in first (L1) and second (L2) language. RTs revealed an overall tuning to L1 bigram statistics. Neuroimaging results demonstrated tuning to L1 statistics at sublexical (occipital lobe) and phonological (temporoparietal lobe) levels, whereas neural activation in the angular gyri reflected sensitivity to lexical similarity to both languages. Analysis of distributed activation patterns reflected language attribution as early as in the ventral stream of visual processing. We conclude that in language-ambiguous contexts visual word processing is dominated by L1 statistical structure at sublexical orthographic and phonological levels, whereas lexical search is determined by the structure of both languages. Moreover, our results demonstrate that language identity modulates distributed activation patterns throughout the reading network, providing a key to language identity representations within this shared network.