Abstract
Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions—a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment.
Highlights
Graded probabilistic prediction is thought to play a crucial role in language processing
In our event-related potential (ERP) study [41], we showed some evidence of an ERP effect that was more prolonged and that had a more anterior distribution than the N400 effect, which was selectively evoked by unrelated target words in the high versus low predictive validity blocks
We report whole-brain effects at a voxel-level threshold of p
Summary
Graded probabilistic prediction is thought to play a crucial role in language processing. We use multiple types of contextual information to predict upcoming information at multiple grains and levels of representation. Inputs that confirm these predictions are processed more efficiently than inputs that are not predicted (see [1] for a recent review), and inputs that disconfirm these predictions allow us to adapt to our ever-changing communicative environments [2,3,4,5]. While the N400—a negative-going ERP waveform that peaks between 300-500mm from the onset of a given target word—is thought to reflect semantic facilitation when the target confirms prior semantic predictions While the N400—a negative-going ERP waveform that peaks between 300-500mm from the onset of a given target word—is thought to reflect semantic facilitation when the target confirms prior semantic predictions (e.g. [8,9]), a later set of ERP components, which tend to peak after the N400 time window, appear to be selectively modulated when the target disconfirms medium or high probability semantic predictions ([5,7,10,11]; see Discussion section)
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