In the study of human language comprehension, the N400 brain potential is often used as an index of on-line processing of meaning. However, a more precise account of its underlying computational process is still debated. Previous neural network modelling work simulated N400 amplitudes as change in a probabilistic representation corresponding to an internal implicit prediction error or Bayesian surprise at the level of meaning (Rabovsky, Hansen, & McClelland, 2018). Here we further test this account using a Bayesian learner model to simulate semantic processing of nouns from different semantic categories presented in an oddball like roving paradigm. We show that Bayesian surprise at the level of meaning significantly predicts N400 amplitudes and does so to a significantly higher degree than a non-Bayesian baseline. This sets the N400 in relation to ERP mismatch negativities in perceptual oddball paradigms, which have featured prominently in Bayesian accounts of brain function.

N400 amplitudes as Bayesian surprise at the level of meaning

The N400 brain potential has been used as a neural correlate of meaning-related processing in the brain, but its underlying computational mechanism is still not well understood. Although efforts to model the N400 as an update of a probabilistic representation of meaning have been promising, the limited scope of earlier models has restricted experiments to highly simplified sentences. Here, we expand modelling of the N400 to naturalistic sentences using a large-scale, state-of-the-art deep learning language model. We investigate the correspondence between updates in the internal state of the model and the N400 in one quantitative experiment and four qualitative experiments. Our findings suggest that activation updates in the model correspond to several N400 effects, but cannot account for all of them.

Meaning in brains and machines: Internal activation update in large-scale language model partially reflects the N400 brain potential

The N400 component of the event related brain potential is widely used to investigate language and meaning processing. However, despite much research the component’s functional basis remains actively debated. Recent work showed that the update of the predictive representation of sentence meaning (semantic update, or SU) generated by the Sentence Gestalt model (McClelland, St. John, & Taraban, 1989) consistently displayed a similar pattern to the N400 amplitude in a series of conditions known to modulate this event-related potential. These results led (Rabovsky, Hansen, and McClelland,2018) to suggest that the N400 might reflect change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error. However, a limitation of this work is that the model was trained on a small artificial training corpus and thus could not be presented with the same naturalistic stimuli presented in empirical experiments. In the present study, we overcome this limitation and directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the SU generated by a Sentence Gestalt model trained on a large corpus of texts. The results reported in this paper corroborate the hypothesis that the N400 component reflects the change in a probabilistic representation of meaning after every word presentation. Further analyses demonstrate that the SU of the Sentence Gestalt model and the amplitude of the N400 are influenced similarly by the stochastic and positional properties of the linguistic input.

Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus

A lot of studies on the N400 event-related brain potential (ERP) component have tried to understand its functional significance. Recently, the N400 was modeled in a neural network model as the update of a probabilistic representation of sentence meaning. The change in activation induced by a word was proposed to reflect a semantic prediction error that drives adaptation of the model’s connections (Rabovsky et al., 2018), which implies that more negative N400 amplitudes should lead to greater adaptation. By experimentally manipulating expectancy in a sentence reading task (n=33), we could show that this manipulation did not only influence N400 amplitudes, but also implicit memory: reaction times in a perceptual identification task were significantly faster for previously unexpected words. Additionally, difference in N400 amplitude correlated with the reaction time benefit for unexpected compared to expected items. These finding support the interpretation of the N400 as a prediction error and learning signal.

The N400 event-related potential component reflects a learning signal

FT47: Neuroscience

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The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

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N400 amplitudes as Bayesian surprise at the level of meaning

Meaning in brains and machines: Internal activation update in large-scale language model partially reflects the N400 brain potential

Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus

The N400 event-related potential component reflects a learning signal

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