Computational models of inductive reasoning and their psychologicale examination:Towards an induction-based search-engine

The purpose of the present study is to propose computational models of human inductive reasoning, using a statistical analysis of Japanese linguistic data, and to develop a searchengine based on inductive reasoning. Osherson, et al. (1990) provided a psychological model of inductive reasoning based on the similarity between the premise and the conclusion and on knowledge of the category including the premise and the conclusion. Models of this kind are known as categorybased models. In contrast, Sloman (1993) proposed a model where the inductive reasoning is based only on the features of arguments (the feature-based model). These models were constructed based on the result from psychological evaluations concerning the relationship between arguments and their attributes which were selected in advance. However, it is difficult to objectively identify all the attributes that cover general human knowledge, which is necessary in order to construct a general model that simulates the human process of inductive reasoning. Moreover, the costs involved in conducting psychological evaluations for the sheer numbers of features means that they are prohibitively impractical. In order to avoid such problems, the present study proposes three types of models (a neural network model, a subjective probabilistic model, and a Bayesian model) that utilize the results of statistical analysis for a language corpus in computing co-occurrence probabilities for two words, rather than using psychological evaluation. A psychological experiment concerning inductive reasoning was conducted to evaluate the models. In comparisons of the experimental results and the simulation results for the neural network model and the subjective probabilistic model, good correlations were observed. We also successfully implemented a trial version of a searchengine based on inductive reasoning using the subjective probabilistic model. Introduction Osherson, et al. (1990) provided a psychological model of inductive reasoning based on the similarity between the premise and the conclusion and on knowledge of the category including the premise and the conclusion. Models of this kind are known as category-based models. In contrast, Sloman (1993) developed another type of psychological model for inductive reasoning, where only the similarity between the premise and the conclusion, based on their features, has an important role. This model is called the feature-based model. Sloman argues that the feature-based model is better able to account for the results from an experiment involving inductive reasoning for conditionals. Sloman’s model is a kind of neural network. In the model, input nodes represent the feature pattern of the conclusion, and the output node indicates the similarity of the conclusion to the premises. The strengths of weights between the input nodes and the output node are computed from the feature patterns of the premises according to a kind of delta rule. However, while this model is only capable of handling positive premises that include affirmative expressions, and so cannot accept negative premises consisting of negative expressions, the category-based model is valid for negative premises (Osherson, et al., 1991). Furthermore, Sloman’s model requires experimental data to determine the feature strengths of the premises and conclusion. However, it is difficult to identify all the features that cover general human knowledge that is necessary in order to construct a general model that simulates the human inductive reasoning process. Beyond this problem, costs involved in conducting psychological evaluations for the sheer numbers of features means that they are prohibitively impractical. Osherson’s model also faces similar problems, because it also needs experimental data to determine the strength of similarity between the premises and the conclusion, as well as the similarity between the premises and the category including the premises and the conclusion. In order to solve these problems for the construction of a general model of inductive reasoning, the present study proposes three types of models that use the results from the statistical analysis of a language corpus, instead of psychological evaluations of the relationships between objects and their attributes. The analysis results are used to compute co-occurrence probabilities between two words (in the case of the feature-based model) and to compute cooccurrence probabilities between a word and a category (in the case of the category-based models). The feature-based model proposed here has an input layer and one output node, where the weights between input