# Visual-Imagery-Based Analogical Construction in Geometric Matrix Reasoning Task

arXiv: https://arxiv.org/abs/2208.13841 · HTML: https://mlsanity.com/paper/2208.13841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [RAVEN: A Dataset for Relational and Analogical Visual rEasoNing](https://mlsanity.com/paper/1903.02741/) (2019)
- [Measuring abstract reasoning in neural networks](https://mlsanity.com/paper/1807.04225/) (2018)
- [Scale-Localized Abstract Reasoning](https://mlsanity.com/paper/2009.09405/) (2020)
- [Automatic Item Generation of Figural Analogy Problems: A Review and Outlook](https://mlsanity.com/paper/2201.08450/) (2022)
- [Learning Spatially Structured Image Transformations Using Planar Neural Networks](https://mlsanity.com/paper/1912.01553/) (2019)

## Abstract (arXiv)

Raven's Progressive Matrices is a family of classical intelligence tests that have been widely used in both research and clinical settings. There have been many exciting efforts in AI communities to computationally model various aspects of problem solving such figural analogical reasoning problems. In this paper, we present a series of computational models for solving Raven's Progressive Matrices using analogies and image transformations. We run our models following three different strategies usually adopted by human testees. These models are tested on the standard version of Raven's Progressive Matrices, in which we can solve 57 out 60 problems in it. Therefore, analogy and image transformation are proved to be effective in solving RPM problems.