The distinction between letter strings that form words and those that look and sound plausible but are not meaningful is a basic one. Decades of functional neuroimaging experiments have used this distinction to isolate the neural basis of lexical (word-level) semantics, associated with areas such as the middle temporal, angular, and posterior cingulate gyri that overlap the default-mode network. In two functional magnetic resonance imaging (fMRI) experiments, a different set of findings emerged when word stimuli were used that were less familiar (measured by word frequency) than those typically used. Instead of activating default-mode network areas often associated with semantic processing, words activated task-positive areas such as the inferior prefrontal cortex and supplementary motor area, along with multi-functional ventral occipito-temporal cortices related to reading, while nonwords activated default-mode areas previously associated with semantics. Effective connectivity analyses of fMRI data on less familiar words showed activation driven by task-positive and multi-functional reading-related areas, while highly familiar words showed bottom-up activation flow from occipito-temporal cortex. These findings suggest functional neuroimaging correlates of semantic processing are less stable than previously assumed, with factors such as word frequency influencing the balance between task-positive, reading-related, and default-mode networks. More generally, this suggests results of contrasts typically interpreted in terms of semantic content may be more influenced by factors related to task difficulty than is widely appreciated.

It has been proposed that people can generate probabilistic predictions at multiple levels of representation during language comprehension. We used magnetoencephalography (MEG) and electroencephalography (EEG), in combination with representational similarity analysis, to seek neural evidence for the prediction of animacy features. In two studies, MEG and EEG activity was measured as human participants (both sexes) read three-sentence scenarios. Verbs in the final sentences constrained for either animate or inanimate semantic features of upcoming nouns, and the broader discourse context constrained for either a specific noun or for multiple nouns belonging to the same animacy category. We quantified the similarity between spatial patterns of brain activity following the verbs until just before the presentation of the nouns. The MEG and EEG datasets revealed converging evidence that the similarity between spatial patterns of neural activity following animate-constraining verbs was greater than following inanimate-constraining verbs. This effect could not be explained by lexical-semantic processing of the verbs themselves. We therefore suggest that it reflected the inherent difference in the semantic similarity structure of the predicted animate and inanimate nouns. Moreover, the effect was present regardless of whether a specific word could be predicted, providing strong evidence for the prediction of coarse-grained semantic features that goes beyond the prediction of individual words. Significance Statement Language inputs unfold very quickly during real-time communication. By predicting ahead, we can give our brains a "head start," so that language comprehension is faster and more efficient. Although most contexts do not constrain strongly for a specific word, they do allow us to predict some upcoming information. For example, following the context of "they cautioned the...," we can predict that the next word will be animate rather than inanimate (we can caution a person, but not an object). Here, we used EEG and MEG techniques to show that the brain is able to use these contextual constraints to predict the animacy of upcoming words during sentence comprehension, and that these predictions are associated with specific spatial patterns of neural activity.