Learn More
The purpose of this research was to construct a computational model of the metaphor generation process. In order to construct the model, first, the probabilistic relationship between concepts and words was computed with a statistical analysis of language data. Secondly, a computational model of the metaphor generation process was constructed with results of(More)
Mechanisms that underlie the inductive reasoning process in risk contexts are investigated. Experimental results indicate that people rate the same inductive reasoning argument differently according to the direction of risk aversion. In seeking to provide the most valid explanation of this, two kinds of models based on a Support Vector Machine (SVM) that(More)
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 search-engine 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(More)
Various learning theories stress the importance of negative learning effects of negative premises have rarely been discussed in any detail within theories of inductive reasoning (with the exception of Osherson et al., 1990). Although Sakamoto et al. (2005) have proposed some computational models that can cope with negative premises and verified their(More)
We studied decision making in situations in which there is a monetary incentive to take risk, and in which the risk taking option sometimes involves deception. We conducted a within participant experiment in which we compared risk taking in deception conditions to pure (non-deceptive) gambles with equivalent risks and outcomes. We confirmed the four-fold(More)
We studied deceptive decision making in hypothetical scenarios that involved risk of being caught of deceiving, or a penalty after being caught of deceiving, or both. We found that the deception rate was the lowest in the scenarios involving both the risk and the penalty. Our hierarchical model for deception suggests that in balancing the possible benefits(More)
A computational model of cognitive inductive reasoning that accounts for risk context effects is proposed. The model is based on a Support Vector Machine (SVM) that utilizes the kernel method. Kernel functions within the model are assumed to represent the functions of similarity computations based on distances between premise entities and conclusion(More)