# Modeling Human Categorization of Natural Images Using Deep Feature Representations

arXiv: https://arxiv.org/abs/1711.04855 · HTML: https://mlsanity.com/paper/1711.04855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal](https://mlsanity.com/paper/2202.01691/) (2022)
- [CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention](https://mlsanity.com/paper/2301.06216/) (2023)

## Abstract (arXiv)

Over the last few decades, psychologists have developed sophisticated formal models of human categorization using simple artificial stimuli. In this paper, we use modern machine learning methods to extend this work into the realm of naturalistic stimuli, enabling human categorization to be studied over the complex visual domain in which it evolved and developed. We show that representations derived from a convolutional neural network can be used to model behavior over a database of >300,000 human natural image classifications, and find that a group of models based on these representations perform well, near the reliability of human judgments. Interestingly, this group includes both exemplar and prototype models, contrasting with the dominance of exemplar models in previous work. We are able to improve the performance of the remaining models by preprocessing neural network representations to more closely capture human similarity judgments.