mlsanityData to 14 Oct 2026

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

arXiv 2601.12577 ↗First posted 18 Jan 2026q-bio.NC cs.AIcited by 1

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

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.

Key facts. Cited by 1 paper 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.

Builds on

6 of 61 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  4. 2020Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Futurecited by 83
  5. 2016Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausiblecited by 7
  6. 2022Adaptive patch foraging in deep reinforcement learning agentscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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