Computational Models of Inductive Reasoning and Their Psychological Examination: Towards an Induction-Based Search-Engine
Isaac Naveh, Ron Sun · eScholarship (California Digital Library) · 2005
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.