Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that “inefficient” learning systems may be more sensitive to structural changes in a dynamic environment.

Previous psycholinguistic research has debated the nature of the mental representation of verbs and the access of relevant verb information in sentence processing. In this study, we used behavioral and electrophysiological methods to examine the representation of verbs in and out of sentence contexts. In five experiments, word naming and event-related potential (ERP) components were used to measure the speed and the amplitude, respectively, associated with different verb-object combinations that result in different degrees of fit between the verb and its object. Both naming speed and ERP amplitudes (N400) are proven to be sensitive indices of the degree of fit, varying as a function of how well the object fits the verb in terms of selectional restrictions. The results suggest that the semantic features of the verb's arguments are an integral part of the mental representation of verbs, and such information of the verb is accessed and used on-line during sentence processing. Implications of these results are discussed in light of recent computational semantic models that view the lexicon through high-order lexical co-occurrences in language use.