Perceptual Features of Abstract Images for Metaphor Generation

Abstract

In this study, the roles of shape and color features in metaphor generation for abstract images were investigated through simulations using retrained convolutional neural network (CNN) models based on the pretrained CNN model, AlexNet. A computational experiment was conducted using five types of retrained object recognition models: an object recognition model using the cleaned ILSVRC-2012 training dataset, one to recognize more shape features using edge-detected images, one to recognize fewer shape features using blurred images, one to recognize fewer color features using grayscale images, and one to recognize only shape features using Canny edge-detected images. The metaphors generated for abstract images were collected from behavioral data obtained in a psychological experiment aimed at investigating the neural mechanisms of metaphor generation for abstract images. In the computational experiment, the simulation results of the five models for abstract images were compared to examine how well they predicted the objects used in the metaphors generated for abstract images in the psychological experiment. The edge-only model using Canny edge-detected images and the color-inhibited model using grayscale images exhibited better performance in metaphor recognition for abstract images than the control condition. This indicates that shape features play a more important role than color features in metaphor generation for abstract images. Furthermore, because the Canny edge detection technique extracts only object outlines that can be regarded as the caricaturization of objects, the caricatured images, based on the shape features of the abstract images, likely influence object recognition for metaphor generation.