Decoding semantic predictions from EEG prior to word onset

ABSTRACTThe outstanding speed of language comprehension necessitates a highly efficient implementation of cognitive-linguistic processes. The domain-general theory of Predictive Coding suggests that our brain solves this problem by continuously forming linguistic predictions about expected upcoming input. The neurophysiological implementation of these predictive linguistic processes, however, is not yet understood. Here, we use EEG (human participants, both sexes) to investigate the existence and nature of online-generated, category-level semantic representations during sentence processing. We conducted two experiments in which some nouns – embedded in a predictive spoken sentence context – were unexpectedly delayed by 1 second. Target nouns were either abstract/concrete (Experiment 1) or animate/inanimate (Experiment 2). We hypothesized that if neural prediction error signals following (temporary) omissions carry specific information about the stimulus, the semantic category of the upcoming target word is encoded in brain activity prior to its presentation. Using time-generalized multivariate pattern analysis, we demonstrate significant decoding of word category from silent periods directly preceding the target word, in both experiments. This provides direct evidence for predictive coding during sentence processing, i.e., that information about a word can be encoded in brain activity before it is perceived. While the same semantic contrast could also be decoded from EEG activity elicited by isolated words (Experiment 1), the identified neural patterns did not generalize to pre-stimulus delay period activity in sentences. Our results not only indicate that the brain processes language predictively, but also demonstrate the nature and sentence-specificity of category-level semantic predictions preactivated during sentence comprehension.STATEMENT OF SIGNIFICANCEThe speed of language comprehension necessitates a highly efficient implementation of cognitive-linguistic processes. Predictive processing has been suggested as a solution to this problem, but the underlying neural mechanisms and linguistic content of such predictions are only poorly understood. Inspired by Predictive Coding theory, we investigate whether the meaning of expected, but not-yet heard words can be decoded from brain activity. Using EEG, we can predict if a word is, e.g., abstract (as opposed to concrete), or animate (vs. inanimate), from brain signals preceding the word itself. This strengthens predictive coding theory as a likely candidate for the principled neural mechanisms underlying online processing of language and indicates that predictive processing applies to highly abstract categories like semantics.