# Human vs. supervised machine learning: Who learns patterns faster?

arXiv: https://arxiv.org/abs/2012.03661 · HTML: https://mlsanity.com/paper/2012.03661/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence](https://mlsanity.com/paper/2404.00029/) (2024)
- [Learning to Defer with Limited Expert Predictions](https://mlsanity.com/paper/2304.07306/) (2023)
- [Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2208.01573/) (2022)
- [Iterative Assessment and Improvement of DNN Operational Accuracy](https://mlsanity.com/paper/2303.01295/) (2023)
- [Transferring Domain Knowledge with (X)AI-Based Learning Systems](https://mlsanity.com/paper/2406.01329/) (2024)
- [DropTrack -- automatic droplet tracking using deep learning for microfluidic applications](https://mlsanity.com/paper/2205.02568/) (2022)
- [Comparing Reinforcement Learning and Human Learning using the Game of Hidden Rules](https://mlsanity.com/paper/2306.17766/) (2023)
- [Using Synthetic Images to Augment Small Medical Image Datasets](https://mlsanity.com/paper/2503.00962/) (2025)
- [One-Shot Identification with Different Neural Network Approaches](https://mlsanity.com/paper/2601.08278/) (2026)

## Abstract (arXiv)

The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find. The results show a high dependency between performance and the underlying patterns of the task. Whereas humans perform relatively similarly across all patterns, machines show large performance differences for the various patterns in our experiment. After seeing 20 instances in the experiment, human performance does not improve anymore, which we relate to theories of cognitive overload. Machines learn slower but can reach the same level or may even outperform humans in 2 of the 4 of used patterns. However, machines need more instances compared to humans for the same results. The performance of machines is comparably lower for the other 2 patterns due to the difficulty of combining input features.