  Regularization occurs when the output a learner produces is less variable
than the linguistic data they observed. In an artificial language learning
experiment, we show that there exist at least two independent sources of
regularization bias in cognition: a domain-general source based on cognitive
load and a domain-specific source triggered by linguistic stimuli. Both of
these factors modulate how frequency information is encoded and produced, but
only the production-side modulations result in regularization (i.e. cause
learners to eliminate variation from the observed input). We formalize the
definition of regularization as the reduction of entropy and find that entropy
measures are better at identifying regularization behavior than frequency-based
analyses. Using our experimental data and a model of cultural transmission, we
generate predictions for the amount of regularity that would develop in each
experimental condition if the artificial language were transmitted over several
generations of learners. Here we find that the effect of cognitive constraints
can become more complex when put into the context of cultural evolution:
although learning biases certainly carry information about the course of
language evolution, we should not expect a one-to-one correspondence between
the micro-level processes that regularize linguistic datasets and the
macro-level evolution of linguistic regularity.

Computation and Language;Neurons and Cognition