Before the N400: Effects of lexical–semantic violations in visual cortex

There exists an increasing body of research demonstrating that language processing is aided by context-based predictions. Recent findings suggest that the brain generates estimates about the likely physical appearance of upcoming words based on syntactic predictions: words that do not physically look like the expected syntactic category show increased amplitudes in the visual M100 component, the first salient MEG response to visual stimulation. This research asks whether violations of predictions based on lexical–semantic information might similarly generate early visual effects. In a picture–noun matching task, we found early visual effects for words that did not accurately describe the preceding pictures. These results demonstrate that, just like syntactic predictions, lexical–semantic predictions can affect early visual processing around ∼100 ms, suggesting that the M100 response is not exclusively tuned to recognizing visual features relevant to syntactic category analysis. Rather, the brain might generate predictions about upcoming visual input whenever it can. However, visual effects of lexical–semantic violations only occurred when a single lexical item could be predicted. We argue that this may be due to the fact that in natural language processing, there is typically no straightforward mapping between lexical–semantic fields (e.g., flowers) and visual or auditory forms (e.g., tulip, rose, magnolia). For syntactic categories, in contrast, certain form features do reliably correlate with category membership. This difference may, in part, explain why certain syntactic effects typically occur much earlier than lexical–semantic effects.

► We used MEG to study neural responses to violations of lexical–semantic prediction.
► We found effects of lexical–semantic predictability in visual cortex around 100 ms.
► This shows that lexical–semantic prediction may affect processing before the N400.
► We propose that lexical–semantic predictions may be translated into form-estimates.
► We argue that predictive/top-down accounts may help explain rapid language processing.