Exploring the spatial reasoning ability of neural models in human IQ tests. (August 2021)

• Record Type: Journal Article
• Authors: Kim, Hyunjae; Koh, Yookyung; Baek, Jinheon; Kang, Jaewoo
• Is Part Of: Neural networks. Volume 140(2021)
• Page Start: 27
• Page End: 38
• Publication Date: 2021-08
• Subjects: Spatial reasoning -- Human IQ test -- Neural networks; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.02.018
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spatial understanding of neural models. First, we describe the following two spatial reasoning IQ tests: rotation and shape composition. Using well-defined rules, we constructed datasets that consist of various complexity levels. We designed a variety of experiments in terms of generalization, and evaluated six different baseline models on the newly generated datasets. We provide an analysis of the results and factors that affect the generalization abilities of models. Also, we analyze how neural models solve spatial reasoning tests with visual aids. We hope that our work can encourage further research into human-level spatial reasoning and provide a new direction for future work.