# Five Points to Check when Comparing Visual Perception in Humans and Machines

arXiv: https://arxiv.org/abs/2004.09406 · HTML: https://mlsanity.com/paper/2004.09406/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices](https://mlsanity.com/paper/2201.12382/) (2022)
- [Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be](https://mlsanity.com/paper/2110.05861/) (2021)
- [Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study](https://mlsanity.com/paper/2108.00107/) (2021)
- [AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation](https://mlsanity.com/paper/2203.06558/) (2022)
- [Recurrent Vision Transformer for Solving Visual Reasoning Problems](https://mlsanity.com/paper/2111.14576/) (2021)
- [Arguments for the Unsuitability of Convolutional Neural Networks for Non--Local Tasks](https://mlsanity.com/paper/2102.11944/) (2021)
- [Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI](https://mlsanity.com/paper/2401.11835/) (2024)
- [Human Machine Co-Creation. A Complementary Cognitive Approach to Creative Character Design Process Using GANs](https://mlsanity.com/paper/2311.13960/) (2023)

## Abstract (arXiv)

With the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed towards comparing information processing in humans and machines. These studies are an exciting chance to learn about one system by studying the other. Here, we propose ideas on how to design, conduct and interpret experiments such that they adequately support the investigation of mechanisms when comparing human and machine perception. We demonstrate and apply these ideas through three case studies. The first case study shows how human bias can affect how we interpret results, and that several analytic tools can help to overcome this human reference point. In the second case study, we highlight the difference between necessary and sufficient mechanisms in visual reasoning tasks. Thereby, we show that contrary to previous suggestions, feedback mechanisms might not be necessary for the tasks in question. The third case study highlights the importance of aligning experimental conditions. We find that a previously-observed difference in object recognition does not hold when adapting the experiment to make conditions more equitable between humans and machines. In presenting a checklist for comparative studies of visual reasoning in humans and machines, we hope to highlight how to overcome potential pitfalls in design or inference.