  I present a computational-level model of semantic interference effects in
word production. Word production is cast as a rate-distortion problem where an
agent selects words to minimize a measure of cost while also minimizing the
resources used to compute the output word based on perceptual input and
behavioral goals. I show that similarity-based interference among words arises
naturally in this setup, and I present a series of simulations showing that the
model captures some of the key empirical patterns observed in Stroop and
Picture-Word Interference paradigms. I argue that the rate-distortion account
of interference provides a high-level formalization of computational principles
that are instantiated more mechanistically in existing models.
