The Effects of Negative Premises on Inductive Reasoning: A Psychological Experiment and Computational Modeling Study
Masanori Nakagawa, Kayo Sakamoto · eScholarship (California Digital Library) · 2006
Various learning theories stress the importance of negative learning (e.g., Bruner, 1959;Hanson, 1956).However, the 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 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.