  Spoken communication occurs in a "noisy channel" characterized by high levels
of environmental noise, variability within and between speakers, and lexical
and syntactic ambiguity. Given these properties of the received linguistic
input, robust spoken word recognition -- and language processing more generally
-- relies heavily on listeners' prior knowledge to evaluate whether candidate
interpretations of that input are more or less likely. Here we compare several
broad-coverage probabilistic generative language models in their ability to
capture human linguistic expectations. Serial reproduction, an experimental
paradigm where spoken utterances are reproduced by successive participants
similar to the children's game of "Telephone," is used to elicit a sample that
reflects the linguistic expectations of English-speaking adults. When we
evaluate a suite of probabilistic generative language models against the
yielded chains of utterances, we find that those models that make use of
abstract representations of preceding linguistic context (i.e., phrase
structure) best predict the changes made by people in the course of serial
reproduction. A logistic regression model predicting which words in an
utterance are most likely to be lost or changed in the course of spoken
transmission corroborates this result. We interpret these findings in light of
research highlighting the interaction of memory-based constraints and
representations in language processing.
