Time-constrained memory for reader-based text comprehension

Marvin Minsky (1986, p.18) writes at the beginning of The Society of Mind that "to explain the mind, we have to show how minds are built from mindless stuff, from parts that are much smaller and simpler than anything we'd consider smart." In this dissertation, I develop a model of a strictly quantitative (i.e., non-semantic) memory that can be used to specify a conceptual analyzer for teuchistic (i.e., 'constructionist') text comprehension. I view this model as a prototype of Minsky's "agents of the mind". Most importantly, I acknowledge the real-time processing constraints derived from the biological constraint (Feldman, 1984) and therefore, assume that linguistic comprehension is a race defined in terms of time-constrained memory processes. Because I do not model an adaptable memory, I partition memory into a static component, which consists of a massively parallel network of simple computing elements whose processes allow for the construction of clusters, and a dynamic component, where these clusters reside. Through specification browsers, the user of the system can input and modify both the topology of the network and the individual behavior of each computing element of static memory, which forms a 'knowledge' base. Clusters are built from the processing of an input text with respect to this 'knowledge' base and constitute the output of the system. Given that there is widespread disagreement on the nature, modus operandi, and use of inferences in text comprehension, the focus in this work is not on the knowledge required for comprehension, but rather on its specification in terms of constraints to satisfy through the exchange of simple signals and sequences of primitive memory operations to execute upon constraint satisfaction. I demonstrate at length how typical rules for the problems of syntax, referential resolution, lexical and structural disambiguation, and bridging inferences can be encoded in the proposed representational scheme, and thus illustrate how a theory of text understanding may be 'grounded' into a more fundamental quantitative time-constrained memory.