Identifying and discriminating temporal events with connectionist language users

Connectionist learning algorithms related to back-propagation have proven so effective that recent work has seriously considered the possibility of developing systems which learn and use natural language rather than processing it. This approach is termed the study of `connectionist language users´. The connectionist language user paradigm is applied to several studies of the perception, processing, and description of events. In one study, a network was trained to discriminate the order with which objects appeared in a microworld. In a second study, networks described sequences of events in the microworld using `verbs´. In a third study `plan recognition´ was modeled. In the final study, networks answered questions that used verbs of possession. These results further strengthen the generality of the approach as a unified model of perception, action, and language