Semantic Transparency,Brain Monitoring and Evaluation of Hybrid Cognitive Architectures

In constructing hybrid systems, there is a need for a principled basis to determine the relative roles or functions of artificial neural network and symbolic approaches. The primary objective of the work to be reported is the construction of a conceptual and methodological framework that permits an iterative sequence in which a hybrid model predicts the basis of cognitive performance and an objective analysis of performance provides empirical data, evaluating (and thus constraining) the structure and processes of the model. In seeking a linkage between a hybrid model of cognition and human performance the concept of “semantic transparency” has been adopted, since it can be used in analyzing and describing both the chracteristics of a model of cognition and the processes underlying human performance. An overview of a specific, ”strong” hybrid architecture is presented. The characteristics of the virtual machines which compose it and the nature of their interaction are illustrated. An approach to the questions of evaluation is described based on empirical data obtained by brain monitoring of subjects during cognitive performance.