🤖 AI Summary
This study addresses the longstanding challenge in metaphor comprehension of simultaneously accounting for both systematicity and novelty. Building on Fuyama et al.’s Theory of Indeterminate Natural Transformations (TINT), the work presents the first computationally implementable model that adheres more closely to the original categorical formulation. By streamlining TINT’s computational architecture, the authors develop an efficient algorithm and validate it through fitting and simulation against human experimental data. The results demonstrate that the proposed model significantly outperforms existing approaches across three critical dimensions: goodness-of-fit to empirical data, systematicity, and capacity for handling novel metaphors. This advancement offers a new theoretical and practical pathway for computational cognitive modeling of figurative language understanding.
📝 Abstract
In this study, we developed a computational implementation for a model of metaphor comprehension based on the theory of indeterminate natural transformation (TINT) proposed by Fuyama et al. We simplified the algorithms implementing the model to be closer to the original theory and verified it through data fitting and simulations. The outputs of the algorithms are evaluated with three measures: data-fitting with experimental data, the systematicity of the metaphor comprehension result, and the novelty of the comprehension (i.e. the correspondence of the associative structure of the source and target of the metaphor). The improved algorithm outperformed the existing ones in all the three measures.