TY  - JOUR
A1  - Rabovsky, Milena
A1  - McClelland, James L.
T1  - Quasi-compositional mapping from form to meaning
BT  - a neural network-based approach to capturing neural responses during human language comprehension
JF  - Philosophical transactions of the Royal Society of London : B, Biological sciences
N2  - We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of human language processing should explain both the outcome of the comprehension process and the continuous internal processes underlying this performance. These points motivate our discussion of a neural network model of sentence comprehension, the Sentence Gestalt model, which we have used to account for the N400 component of the event-related brain potential (ERP), which tracks meaning processing as it happens in real time. The model, which shares features with recent deep learning-based language models, simulates N400 amplitude as the automatic update of a probabilistic representation of the situation or event described by the sentence, corresponding to a temporal difference learning signal at the level of meaning. We suggest that this process happens relatively automatically, and that sometimes a more-controlled attention-dependent process is necessary for successful comprehension, which may be reflected in the subsequent P600 ERP component. We relate this account to current deep learning models as well as classic linguistic theory, and use it to illustrate a domain general perspective on some specific linguistic operations postulated based on compositional analyses of natural language. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.
KW  - language
KW  - meaning
KW  - event-related brain potentials
KW  - neural networks
KW  - N400
KW  - P600
Y1  - 2019
U6  - https://doi.org/10.1098/rstb.2019.0313
SN  - 0962-8436
SN  - 1471-2970
SN  - 0080-4622
VL  - 375
IS  - 1791
PB  - Royal Society
CY  - London
ER  -