# Primate-like perceptual decision making emerges through deep recurrent reinforcement learning

arXiv: https://arxiv.org/abs/2601.12577 · HTML: https://mlsanity.com/paper/2601.12577/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 61 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible](https://mlsanity.com/paper/1606.01651/) (2016)
- [Adaptive patch foraging in deep reinforcement learning agents](https://mlsanity.com/paper/2210.08085/) (2022)

## Built on by (most cited first, 1 of 1)

- [Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement](https://mlsanity.com/paper/2604.22110/) (2026)

## Abstract (arXiv)

Progress has led to a detailed understanding of the neural mechanisms that underlie decision making in primates. However, less is known about why such mechanisms are present in the first place. Theory suggests that primate decision making mechanisms, and their resultant behavioral abilities, emerged to maximize reward in the face of noisy, temporally evolving information. To test this theory, we trained an end-to-end deep recurrent neural network using reinforcement learning on a noisy perceptual discrimination task. Networks learned several key abilities of primate-like decision making including trading off speed for accuracy, and flexibly changing their mind in the face of new information. Internal dynamics of these networks suggest that these abilities were supported by similar decision mechanisms as those observed in primate neurophysiological studies. These results provide experimental support for key pressures that gave rise to the primate ability to make flexible decisions.