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Abstract

Subject-verb agreement is one of the most common grammatical encoding operations in language production. In many languages, morphological inflection on verbs code for the number of the head noun of a subject phrase (e.g., The key to the cabinets is rusty). Despite the relative ease with which subjectverb agreement is accomplished, people sometimes make agreement errors (e.g., The key to the cabinets are rusty). Such errors offer a window into the early stages of production planning. Agreement errors are influenced by both syntactic and semantic factors, and are more likely to occur when a sentence contains either conceptual or syntactic number mismatches. Little is known about the timecourse of these influences, however, and some controversy exists as to whether they are independent. The current study was designed to address these two issues using EEG. Semantic and syntactic factors influencing number mismatch were factorially-manipulated in a forced-choice sentence completion paradigm. To avoid EEG artifact associated with speaking, participants (N=20) were presented with a noun-phrase, and pressed a button to indicate which version of the verb ‘to be’ (is/are) should continue the sentence. Semantic number was manipulated using preambles that were semantically-integrated or unintegrated. Semantic integration refers to the semantic relationship between nouns in a noun-phrase, with integrated items promoting conceptual-singularity. The syntactic manipulation was the number (singular/ plural) of the local noun preceding the decision. This led to preambles such as “The pizza with the yummy topping(s)... “ (integated) vs. “The pizza with the tasty bevarage(s)...” (unintegrated). Behavioral results showed effects of both Local Noun Number and Semantic Integration, with more errors and longer reaction times occurring in the mismatching conditions (i.e., plural local nouns; unintegrated subject phrases). Classic ERP analyses locked to the local noun (0-700 ms) and to the time preceding the response (-600 to 0 ms) showed no systematic differences between conditions. Despite this result, we assessed whether difference might emerge using multivariate pattern analysis (MVPA). Using the same epochs as above, support-vector machines with a radial basis function were trained on the single-trial level to classify the difference between Local Noun Number and Semantic Integration conditions across time and channels. Results revealed that both conditions could be reliably classified at the single subject level, and that classification accuracy was strongest in the epoch preceding the response. Classification accuracy was at chance when a classifier trained to dissociate Local Noun Number was used to predict Semantic Integration (and vice versa), providing some evidence of the independence of the two effects. Significant inter-subject variability was present in the channels and time-points that were critical for classification, but earlier timepoints were more often important for classifying Local Noun Number than Semantic Integration. One result of this variability is classification performed across subjects was at chance, which may explain the failure to find standard ERP effects. This study thus provides an important first test of semantic and syntactic influences on subject-verb agreement with EEG, and demonstrates that where classic ERP analyses fail, MVPA can reliably distinguish differences at the neurophysiological level.