Assessing cognitive alignment in different types of dialog by means of a network model.

We present a network model of dialog lexica, called TiTAN (Two-layer Time-Aligned Network) series. TiTAN series capture the formation and structure of dialog lexica in terms of serialized graph representations. The dynamic update of TiTAN series is driven by the dialog-inherent timing of turn-taking. The model provides a link between neural, connectionist underpinnings of dialog lexica on the one hand and observable symbolic behavior on the other. On the neural side, priming and spreading activation are modeled in terms of TiTAN networking. On the symbolic side, TiTAN series account for cognitive alignment in terms of the structural coupling of the linguistic representations of dialog partners. This structural stance allows us to apply TiTAN in machine learning of data of dialogical alignment. In previous studies, it has been shown that aligned dialogs can be distinguished from non-aligned ones by means of TiTAN -based modeling. Now, we simultaneously apply this model to two types of dialog: task-oriented, experimentally controlled dialogs on the one hand and more spontaneous, direction giving dialogs on the other. We ask whether it is possible to separate aligned dialogs from non-aligned ones in a type-crossing way. Starting from a recent experiment (Mehler, Lücking, & Menke, 2011a), we show that such a type-crossing classification is indeed possible. This hints at a structural fingerprint left by alignment in networks of linguistic items that are routinely co-activated during conversation.