questions: (1) How is the cache searched for related entities and how is relatedness determined? (2) How is a ``prior intention'' determined i.e., what memory is there for intentional structure and how is that coordinated with information in main memory so that the relevant information can be found? The stack model coordinates intentional structure and attentional state to address just these issues.

Walker makes two problematic claims in comparing the two models' treatments of interruptions. First, dialogue A differs from dialogue B, in that the interruption in A is three utterances long whereas that in B is five utterances. If this difference affects the cache content as significantly as Walker's model requires, then the cache is very small; it could not accommodate a very large discourse segment. But then one must ask what happens in the cache model with a discourse segment that is longer than three or four utterances. Would such a segment require that the reader or writer explicitly repeat material that is five or more sentences back? Second, in discussing dialogue C Walker says that without (22b), (22c), and (23) the inference required to understand the discourse ``requires more effort to process,'' a claim that requires substantiation; in particular, utterance (22a) followed by (24) without the and that is required because of (22c) seems no more difficult to process than the fragment given. In both these cases, more empirical investigation is needed to determine the appropriate model.

With respect to return pops, Walker has misconstrued Sidner's stacked focus constraint. This constraint was postulated before Sidner integrated her work on local focus of attention with the focus-space stack work. Sidner's claim is significantly different from Walker's. In particular, it stipulates which pronouns cannot be used for entities other than those locally in focus (i.e., those that we now call centers of an utterance). With respect to most of this discussion, Walker does not discuss the potential cost of checking nonfocused entities, but this cost can only be low if very few entities are checked, not all of long-term memory. In the air compressor (not pump!) example to which she refers there was a room full of equipment that could have been made to work and that was visually salient. An alternative to the explanations Walker provides for the IRUs with return pops is that IRUs are a good way to shift attention to a prior discourse intention and segment (hence focus space).

Finally, we note that the cache model does suggest an interesting research issue that is not clearly raised in the stack model. In computer systems, when information is moved from main memory to the cache, it is moved in blocks larger than the individual piece of information that was initially sought. The guiding locality principle here is spatial locality: programs typically need to access information (instructions and data) that have addresses near each other in memory \cite{Patterson_Hennessy_1996}. The processing trade-off that computer designers must address is one of processing time versus hit rate: larger blocks take longer to move, but increase the likelihood that subsequently needed information will be in the cache. Implicit focus \cite{Grosz_1977b} and the treatment of functionally related entities \cite{Grosz_Joshi_Weinstein_1995} respond to a related issue in discourse processing, namely, what other than an entity itself becomes focused when the entity is focused? Although it is widely acknowledged that various related entities become salient (e.g., the door of a house, the time or location of a meeting), the determination of the scope of what becomes salient remains an open question \cite{Grosz_1981}. Whereas computer architects can depend on physical proximity in memory, discourse processing requires measures of conceptual closeness.

\section{The Psychology of Discourse Processing}

Like Walker, we support the integration of research in psycholinguistics and research in computational linguistics, and we support the contention that human information