Areas within the left-lateralized neural network for language have been found to be sensitive to syntactic complexity in spoken and written language. Previous research has revealed that these areas are active for sign language as well, but whether these areas are specifically responsive to syntactic complexity in sign language independent of lexical processing has yet to be found. To investigate the question, we used fMRI to neuroimage deaf native signers' comprehension of 180 sign strings in American Sign Language (ASL) with a picture-probe recognition task. The ASL strings were all six signs in length but varied at three levels of syntactic complexity: sign lists, two-word sentences, and complex sentences. Syntactic complexity significantly affected comprehension and memory, both behaviorally and neurally, by facilitating accuracy and response time on the picture-probe recognition task and eliciting a left lateralized activation response pattern in anterior and posterior superior temporal sulcus (aSTS and pSTS). Minimal or absent syntactic structure reduced picture-probe recognition and elicited activation in bilateral pSTS and occipital-temporal cortex. These results provide evidence from a sign language, ASL, that the combinatorial processing of anterior STS and pSTS is supramodal in nature. The results further suggest that the neurolinguistic processing of ASL is characterized by overlapping and separable neural systems for syntactic and lexical processing.

Models of speech production posit a role for the motor system, predominantly the posterior inferior frontal gyrus, in encoding complex phonological representations for speech production, at the phonemic, syllable, and word levels [Roelofs, A. A dorsal-pathway account of aphasic language production: The WEAVER++/ARC model. Cortex, 59(Suppl. C), 33–48, 2014; Hickok, G. Computational neuroanatomy of speech production. Nature Reviews Neuroscience, 13, 135–145, 2012; Guenther, F. H. Cortical interactions underlying the production of speech sounds. Journal of Communication Disorders, 39, 350–365, 2006]. However, phonological theory posits subphonemic units of representation, namely phonological features [Chomsky, N., & Halle, M. The sound pattern of English , 1968; Jakobson, R., Fant, G., & Halle, M. Preliminaries to speech analysis. The distinctive features and their correlates . Cambridge, MA: MIT Press, 1951], that specify independent articulatory parameters of speech sounds, such as place and manner of articulation. Therefore, motor brain systems may also incorporate phonological features into speech production planning units. Here, we add support for such a role with an fMRI experiment of word sequence production using a phonemic similarity manipulation. We adapted and modified the experimental paradigm of Oppenheim and Dell [Oppenheim, G. M., & Dell, G. S. Inner speech slips exhibit lexical bias, but not the phonemic similarity effect. Cognition, 106, 528–537, 2008; Oppenheim, G. M., & Dell, G. S. Motor movement matters: The flexible abstractness of inner speech. Memory & Cognition, 38, 1147–1160, 2010]. Participants silently articulated words cued by sequential visual presentation that varied in degree of phonological feature overlap in consonant onset position: high overlap (two shared phonological features; e.g., /r/ and /l/) or low overlap (one shared phonological feature, e.g., /r/ and /b/). We found a significant repetition suppression effect in the left posterior inferior frontal gyrus, with increased activation for phonologically dissimilar words compared with similar words. These results suggest that phonemes, particularly phonological features, are part of the planning units of the motor speech system.

Visual speech influences the perception of heard speech. A classic example of this is the McGurk effect, whereby an auditory /pa/ overlaid onto a visual /ka/ induces the fusion percept of /ta/. Recent behavioral and neuroimaging research has highlighted the importance of both articulatory representations and motor speech regions of the brain, particularly Broca's area, in audiovisual (AV) speech integration. Alternatively, AV speech integration may be accomplished by the sensory system through multisensory integration in the posterior STS. We assessed the claims regarding the involvement of the motor system in AV integration in two experiments: (i) examining the effect of articulatory suppression on the McGurk effect and (ii) determining if motor speech regions show an AV integration profile. The hypothesis regarding experiment (i) is that if the motor system plays a role in McGurk fusion, distracting the motor system through articulatory suppression should result in a reduction of McGurk fusion. The results of experiment (i) showed that articulatory suppression results in no such reduction, suggesting that the motor system is not responsible for the McGurk effect. The hypothesis of experiment (ii) was that if the brain activation to AV speech in motor regions (such as Broca's area) reflects AV integration, the profile of activity should reflect AV integration: AV > AO (auditory only) and AV > VO (visual only). The results of experiment (ii) demonstrate that motor speech regions do not show this integration profile, whereas the posterior STS does. Instead, activity in motor regions is task dependent. The combined results suggest that AV speech integration does not rely on the motor system.