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Reference list on the representation and computation of uncertainty in the human brain
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  1. The idea behind this reference list started with a question on Twitter on how the human brain represents and computes uncertainty.
  2. The goal is to compile 20-30 papers which can provide a decent overview of the biggest obstacles facing the field of uncertainty computation and representation in the human brain.

Tentative list of 10 papers for getting started:

  1. Bayesian inference with probabilistic population codes. Wei Ji Ma et al. 2006.
  2. Probabilistic Approaches to Neural Coding. Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P. N. Rao. 2007.
  3. Efficient codes and balanced networks. Sophie Deneve & Christian K Machens. 2016.
  4. Decision Theory: What "Should" the Nervous System Do? Konrad Körding. 2007.
  5. The Generalization of Prior Uncertainty during Reaching. Hugo L. Fernandes, Ian H. Stevenson, Iris Vilares and Konrad P. Kording. 2014.
  6. Better Optimism By Bayes: Adaptive Planning with Rich Models. Guez, A, Silver, D & Dayan P. 2014.
  7. The phasic dopamine signal maturing: from reward via behavioural activation to formal economic utility. W. Schultz,
    W. Stauffer, A. Lak. 2017.
  8. Dopamine neurons learn relative chosen value from probabilistic rewards. Armin Lak, Stauffer W. & W. Schultz. 2016.
  9. Dopamine: generalization and bonuses. Kakade & Dayan. 2008.
  10. Two distinct neural mechanisms for category-selective responses. Noppeney U, Price C, Penny W & Friston KJ. 2006.

N additional papers(N <= 10-20):

  1. On the Origins of Suboptimality in Human Probabilistic Inference. Acerbi, L., Vijayakumar, S. & Wolpert, D. M. 2014.
  2. Predictability, Complexity, and Learning. Bialek, Nemenman & Tishby. 2001.
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