Kayo Sakamoto - Academia.edu (original) (raw)

Papers by Kayo Sakamoto

Mechanisms that underlie the inductive reasoning process in risk contexts are investigated. Exper... 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 process different knowledge spaces are proposed and compared. These knowledge spaces—a feature-based space and a category-based space—are both constructed from the soft clustering of the same corpus data. The simulation for the category-based model resulted in a slightly more successful replication of experimental findings for two kinds of risk conditions using two different estimated model parameters than the other simulation. Finally, the cognitive explanation by the category-based model based on a SVM for contextual inductive reasoning is discussed.

Various learning theories stress the importance of negative learning (e.g., Bruner, 1959;. Howeve... more Various learning theories stress the importance of negative learning (e.g., Bruner, 1959;. However, the effects of negative premises have rarely been discussed in any detail within theories of inductive reasoning (with the exception of . Although have proposed some computational models that can cope with negative premises and verified their psychological validity, they did not consider cases where category-based induction theory is ineffective, such as when the entities in both negative and positive premises belong to the same category. The present study was conducted to test the hypothesis that, even when negative and positive premises involve same-category entities, people can estimate the likeliness of an argument conclusion by comparing feature similarities. Based on this hypothesis, two computational models are proposed to simulate this cognitive mechanism. While both these models were able to simulate the results obtained from the psychological experiment, a perceptron model could not. Finally, we argue that the mathematical equivalence (from Support Vector Machines perspective) of these two models suggests that they represent a promising approach to modeling the effects of negative premises, and, thus, to fully handling the complexities of feature-based induction on neural networks.

A computational model of cognitive inductive reasoning that accounts for risk context effects is ... 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 entities in inductive reasoning arguments. Multipliers related to the kernel functions have the role of adjusting similarities and can explain rating shifts between two different risk contexts. Model fitting data supports the SVM-based model with kernel functions as a model of inductive reasoning in risk contexts. Finally, the paper discusses how the multipliers for kernel functions provide a satisfactory cognitive theoretical account of similarity adjustment.

Cognitive Systems Research, 2007

Existing computational models of human inductive reasoning have been constructed based on psychol... more Existing computational models of human inductive reasoning have been constructed based on psychological evaluations concerning the similarities or relationships between entities. However, the costs involved in collecting psychological evaluations for the sheer number of entities that exist mean that they are prohibitively impractical. In order to avoid this problem, the present article examines three types of models: a category-based neural network model, a category-based Bayesian model, and a feature-based neural network model. These models utilize the results of a statistical analysis of a Japanese corpus computing co-occurrence probabilities for word pairs, rather than using psychological evaluations. Argument strength ratings collected by a psychological experiment were found to correlate well with simulations for the category-based neural network model.

The purpose of the present study is to propose computational models of human inductive reasoning,... 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 and on knowledge of the category

Mechanisms that underlie the inductive reasoning process in risk contexts are investigated. Exper... 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 process different knowledge spaces are proposed and compared. These knowledge spaces—a feature-based space and a category-based space—are both constructed from the soft clustering of the same corpus data. The simulation for the category-based model resulted in a slightly more successful replication of experimental findings for two kinds of risk conditions using two different estimated model parameters than the other simulation. Finally, the cognitive explanation by the category-based model based on a SVM for contextual inductive reasoning is discussed.

Various learning theories stress the importance of negative learning (e.g., Bruner, 1959;. Howeve... more Various learning theories stress the importance of negative learning (e.g., Bruner, 1959;. However, the effects of negative premises have rarely been discussed in any detail within theories of inductive reasoning (with the exception of . Although have proposed some computational models that can cope with negative premises and verified their psychological validity, they did not consider cases where category-based induction theory is ineffective, such as when the entities in both negative and positive premises belong to the same category. The present study was conducted to test the hypothesis that, even when negative and positive premises involve same-category entities, people can estimate the likeliness of an argument conclusion by comparing feature similarities. Based on this hypothesis, two computational models are proposed to simulate this cognitive mechanism. While both these models were able to simulate the results obtained from the psychological experiment, a perceptron model could not. Finally, we argue that the mathematical equivalence (from Support Vector Machines perspective) of these two models suggests that they represent a promising approach to modeling the effects of negative premises, and, thus, to fully handling the complexities of feature-based induction on neural networks.

A computational model of cognitive inductive reasoning that accounts for risk context effects is ... 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 entities in inductive reasoning arguments. Multipliers related to the kernel functions have the role of adjusting similarities and can explain rating shifts between two different risk contexts. Model fitting data supports the SVM-based model with kernel functions as a model of inductive reasoning in risk contexts. Finally, the paper discusses how the multipliers for kernel functions provide a satisfactory cognitive theoretical account of similarity adjustment.

Cognitive Systems Research, 2007

Existing computational models of human inductive reasoning have been constructed based on psychol... more Existing computational models of human inductive reasoning have been constructed based on psychological evaluations concerning the similarities or relationships between entities. However, the costs involved in collecting psychological evaluations for the sheer number of entities that exist mean that they are prohibitively impractical. In order to avoid this problem, the present article examines three types of models: a category-based neural network model, a category-based Bayesian model, and a feature-based neural network model. These models utilize the results of a statistical analysis of a Japanese corpus computing co-occurrence probabilities for word pairs, rather than using psychological evaluations. Argument strength ratings collected by a psychological experiment were found to correlate well with simulations for the category-based neural network model.

The purpose of the present study is to propose computational models of human inductive reasoning,... 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 and on knowledge of the category