Abstract
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.
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Sakamoto, K., Nakagawa, M. (2007). Risk Context Effects in Inductive Reasoning: An Experimental and Computational Modeling Study. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds) Modeling and Using Context. CONTEXT 2007. Lecture Notes in Computer Science(), vol 4635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74255-5_32
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DOI: https://doi.org/10.1007/978-3-540-74255-5_32
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