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.
Figures & tables
Figure 1: Schematic diagram of natural transformation search in TINT. Based on the canonical functor derived from the occurrence of association f , new functors such as F1 and F2 are searched as the construction of natural transformations ϑ1 and ϑ2 , respectively.
Figure 2: The specific example of an exploration in TINT, beginning with “A butterfly is like a dancer.”
Figure 3: Construction of a natural transformation (at the same time a functor) by the object-based method
Figure 4: A correspondence between images via the objective method
Figure 5: The algorithms of the triangle method
Figure 6: The distance between the triangle structures
Figure 7: The association weights assigned to the latent category. Only the weights from the initial images of the source to that of the target are shown.
Figure 8: The output of the algorithms evaluated in 1. data fit (gray), 2. systematicity (orange), and 3. novelty (blue). The β value along the x -axis is the inverse temperature parameter for the softmax methods. The circles and triangles in three colors are of the algorithms proposed in Ikeda et al. (that adopted the absolute errors, instead of the square errors for the distance between triangle structures, d ).