Incoherence and text comprehension : cognitive and computational models of inferential control
This thesis describes work on defining and modelling text comprehension. The basis of my approach is a theory of comprehension as a form of abductive reasoning. The specific problem addressed is inferential control and the management of alternative, competing representations. This problem is related to issues of representation quality, with decisions between representations being made on the basis of quality comparisons. Simultaneously, monitoring of representation quality determines when comprehension halts; in other words, there is some kind of threshold against which quality is compared. In the first part of the thesis I analyse concepts of representation quality, describing the structure of episodic and semantic representations and processes. I then look at metrics for representation quality before developing my own metric. The metric is based on the concept of incoherence, derived from the structural potential of representations. The second part of the thesis describes a computational model of incoherence, the Incoherence-Driven Comprehender (IDC). IDC combines AI implementation technology with insights from cognitive psychological studies of text comprehension. I show how IDC can be applied to various comprehension tasks. Throughout the thesis I suggest how aspects of IDC's architecture and behaviour may offer a fresh perspective on human comprehension.