The April issue of Topics in Cognitive Science is devoted to "Production of Referring Expressions: Bridging the Gap Between Computational and Empirical Approaches to Reference." Referring expressions have a long history in computational linguistics, dating back to the earliest natural language understanding systems like SHRDLU. It is only natural that, if a computer and a human are to have discourse about anything, they will each have to refer to things in the domain of discourse, in such a way that each understands the other.
The guest editors' introductory paper to the topic clarifies the issues dealt with here very well. I'll try to briefly summarize: on the one hand, we have computational linguistics approaches to the generation of referring expressions. A standard methodology for this has been basically a type of referential optimization. The domain of discourse is surveyed and used to figure out the least wordy way of referring to something unambiguously. This turns out to be not all that difficult in many situations, but it is also not very natural. It turns out that humans are frequently neither minimally wordy nor unambiguous with their own referring expressions.
This brings us to the other hand. There is quite a trove of literature in psycholinguistics which investigates human strategies for producing referring expressions. The point of the topic is to bring these two largely separate endeavors together, in two ways. Firstly, the adoption of more realistic constraints and features into computational referring systems is advocated. Secondly, the use of more algorithmically specified methods in explaining human performance in this area is also advocated. It is, on the whole, a very worthwhile topic promoting a worthy goal. In general I concur that computational models of human performance should strive to be more realistic, while cognitive models should be more specific or mathematical.