This paper tests the similarity in neural responses across repeated words and morphosyntactic features both within and between two languages. Prior work using priming has revealed robust cross-linguistic lexical effects and effects for shared grammatical form, such as argument structure; these methods have been less successful when applied to morphosyntactic features. Combining machine-learning based neural decoding with EEG data collected from Korean-English bilinguals we, first, replicate prior work showing successful classification of lexical items from EEG signals. We then extend this to demonstrate successful classification of morphosyntactic features of number and tense. Finally, we find that EEG decoding in one language does not successfully generalize to another, even when temporal differences are considered. Taken together, these results point to stable EEG representations for lexical items and morphosyntactic features, but suggest that these representations are different between the two languages investigated here.

One of the key issues in visual word recognition is the role of orthographic overlap in priming. However, most research investigating this topic has focused on priming with orthographic neighbors. In this study, we investigate priming effects of word-final letter overlap and their interaction with word frequency, prime duration, and morphology. In Experiment 1 with briefly presented primes (SOA=34 ms, N = 123), we obtained similar facilitation from non-morphological overlap (compel-TRAVEL) and inflectional suffix overlap (turned-CALLED), regardless of word frequency. In Experiment 2 when primes were fully recognizable (SOA=150 ms, N=123), only non-morphological overlap showed inhibition among lower frequency prime words. These results are inconsistent with predictions of the Interactive Activation model (McCelland and Rumelhart, 1981), and suggest (i) different weights of inhibition and facilitation depending on prime duration and morphological structure of words as well as (ii) the involvement of a reset mechanism in long SOA conditions.

Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations.