Referring expressions often show evidence of preferences, with some attributes (e.g. colour) being more frequent and more often included when they are not required, leading to overspecification. This observation underlies many computational models of Referring Expression Generation, especially those influenced by Dale and Reiter's Incremental Algorithm. However, more recent work has shown that in interactive settings, priming can alter preferences. This paper provides further experimental evidence for these phenomena, and proposes a new computational model that incorporates both attribute preferences and priming effects. We show that the model provides an excellent match to human experimental data.