Semantic Analyzer of Schemata Organization (SASO)

Abstract

A menu-driven software system was developed to implement schemata organization in a constraintsatisfaction neural network. The current neural-net model presents several advantages and modifications over previous related models. On the one hand, the schemata behavior shown by the present model is based on conceptual definitions and ratings obtained empirically from human subjects rather than on the idiosyncratic knowledge database of a single programmer. On the other hand, this property is most valuable to experimental research in which the current knowledge status of the subjects is critical for the experiments (e.g., word-recognition experiments).The present paper describes a menu-driven software system called Semantic Analyzer of Schemata Organization (SASO). This program was developed to implement schemata organization in a constraint-satisfaction neural network similar to the connectionist model for schemata postulated by Rumelhart, Smolensky, McClelland, and Hinton in 1986. However, the current neural-net model presents several advantages and modifications over previous related models. First, the schemata behavior shown by the present neural net is based on conceptual definitions and ratings obtained empirically from human subjects rather than on the idiosyncratic knowledge database of a single programmer. This property is especially well suited to experimental research in which the knowledge status of the subjects is critical for the experiments (e.g. , word-recognition experiments). Second, SASO provides the user with a set of utilities to process raw data generated by the subjects. It also automatically sets up the weight-connectivity matrix among concepts and generates the interface data files needed to run schemata simulations on the constraint-satisfaction neural-net program provided by McClelland and Rumelhart in 1988. A psychological experiment and computer neural-net simulations were carried out to test schemata organization in the current neural-net model. Furthermore, some statistical analysis was performed on the weight-eonnectivity matrix of the neural net. The advantages of using the current model as a computational tool for psychological experiments will be argued elsewhere in this paper, ANALYSIS AND SCALING OF CONCEPTUAL DEF1NITIONSThe first part of the study obtained the empirical data that were used in the neural-network simulations. The schema of room and related schemata were chosen for this experiment in order to work with some of the same schemata that Rumelhart et al. (1986) used in their simulations and thus be able to compare results. Rumelhart et al. (1986) chose a priori the descriptor concepts implemented in their network. By contrast, in the present study, a technique to obtain semantic networks from subjects was used to obtain the concepts of the neural network. Thus, the current schemata simulations are based on the information generated from the memories of a sample of 24 undergraduate students. The technique to obtain conceptual definitions is illustrated in the Method sec...

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