Technical Program

Paper Detail

Paper: PS-1A.44
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Modeling the N400 brain potential as Semantic Bayesian Surprise
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Lea Musiolek, Felix Blankenburg, Dirk Ostwald, Milena Rabovsky, Freie Universität Berlin, Germany
Abstract: In research on human language comprehension, the N400 component of the event-related brain potential (ERP) has attracted attention as an electrophysiological indicator of meaning processing in the brain. However, despite much research, the specific functional basis of the N400 remains widely debated. Recent neural network modeling work suggests that N400 amplitudes can be simulated as the stimulus-induced change in internally represented probabilities of aspects of meaning (Rabovsky, Hansen, & McClelland, 2018). Here, we assess this idea based on single-trial N400 amplitudes measured in an oddball-like roving paradigm with written words from different semantic categories varying in semantic feature overlap. We model the N400 as Semantic Surprise, the change in the probability distribution of a stimulus’s semantic features for each trial. Simple condition-based analyses produced a significant effect of category switch on N400 amplitude, and the trial-by-trial modeling similarly revealed negative effects of Semantic Surprise on N400 amplitude. From fitting a forgetting parameter for each participant, we also gleaned insights into the rates of forgetting of past input to the semantic system. Thus, we provide a computationally explicit account of N400 amplitudes, which links the N400 and thus the neurocognitive processes involved in human language comprehension to the Bayesian brain hypothesis.