Exploring the spatial reasoning ability of neural models in human IQ tests

Hyunjae Kim, Yookyung Koh, Jinheon Baek, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)27-38
Number of pages12
JournalNeural Networks
Publication statusPublished - 2021 Aug

Bibliographical note

Funding Information:
We would like to thank Sean S. Yi (Korea University) for the helpful feedback and revisions. This research was supported by Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR20C0021) . This research was also supported by the National Research Foundation of Korea ( NRF-2020R1A2C3010638 , NRF-2016M3A9A7916996 ).

Publisher Copyright:
© 2021 Elsevier Ltd


  • Human IQ test
  • Neural networks
  • Spatial reasoning

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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