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Journal of Cognitive Neuroscience

June 2007, Vol. 19, No. 6, Pages 971-980.
(doi: 10.1162/jocn.2007.19.6.971)
© 2007 Massachusetts Institute of Technology
Grammar or Serial Order?: Discrete Combinatorial Brain Mechanisms Reflected by the Syntactic Mismatch Negativity
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Abstract

If word strings violate grammatical rules, they elicit neurophysiological brain responses commonly attributed to a specifically human language processor or grammar module. However, an ungrammatical string of words is always also a very rare sequence of events and it is, therefore, not always evident whether specifically linguistic processes are at work when neurophysiological grammar indexes are being reported. We here investigate the magnetic mismatch negativity (MNN) to ungrammatical word strings, to very rare grammatical strings, and to common grammatical phrases. In this design, serial order mechanism mapping the sequential probability of words should neurophysiologically dissociate frequent grammatical phrases from both ungrammatical and rare grammatical strings. However, if syntax as a discrete combinatorial system is reflected, the prediction is that the rare, correctly combined items group with the highly frequent grammatical strings and stand out against ungrammatical strings. Using magnetoencephalography as a measure of human brain activity, we replicated the previously reported syntactic mismatch negativity (sMMN), which distinguishes highly unfamiliar ungrammatical word sequences from common grammatical strings. Crucially, a significant interaction demonstrated that the sMMN specifically distinguished syntactic violations from common grammatical strings, but not uncommon from common grammatical word strings. This significant interaction argues in favor of a genuinely grammatical origin of the sMMN and provides direct neurophysiological evidence for a discrete combinatorial system for word and morpheme sequences in the human brain. The data are more difficult to explain in the context of serial order models that map co-occurrence probabilities of words.