  A large body of work in psycholinguistics has focused on the idea that online
language comprehension can be shallow or `good enough': given constraints on
time or available computation, comprehenders may form interpretations of their
input that are plausible but inaccurate. However, this idea has not yet been
linked with formal theories of computation under resource constraints. Here we
use information theory to formulate a model of language comprehension as an
optimal trade-off between accuracy and processing depth, formalized as bits of
information extracted from the input, which increases with processing time. The
model provides a measure of processing effort as the change in processing
depth, which we link to EEG signals and reading times. We validate our theory
against a large-scale dataset of garden path sentence reading times, and EEG
experiments featuring N400, P600 and biphasic ERP effects. By quantifying the
timecourse of language processing as it proceeds from shallow to deep, our
model provides a unified framework to explain behavioral and neural signatures
of language comprehension.
