Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension

•The N400 ERP component is thought to reflect difficulty of semantic access.•A neural network model of word comprehension exhibits N400 waveforms.•The N400 reflects transient over-activation within semantic representations.•The model accounts for how multiple factors influence N400 amplitudes and behavior.•The account has advantages over one based directly on “implicit prediction error”.

The study of the N400 event-related brain potential has provided fundamental insights into the nature of real-time comprehension processes, and its amplitude is modulated by a wide variety of stimulus and context factors. It is generally thought to reflect the difficulty of semantic access, but formulating a precise characterization of this process has proved difficult. Laszlo and colleagues (Laszlo & Plaut, 2012; Laszlo & Armstrong, 2014) used physiologically constrained neural networks to model the N400 as transient over-activation within semantic representations, arising as a consequence of the distribution of excitation and inhibition within and between cortical areas. The current work extends this approach to successfully model effects on both N400 amplitudes and behavior of word frequency, semantic richness, repetition, semantic and associative priming, and orthographic neighborhood size. The account is argued to be preferable to one based on “implicit semantic prediction error” (Rabovsky & McRae, 2014) for a number of reasons, the most fundamental of which is that the current model actually produces N400-like waveforms in its real-time activation dynamics.