- The idea behind this reference list started with a question on Twitter on how the human brain represents and computes uncertainty.
- 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:
- Bayesian inference with probabilistic population codes. Wei Ji Ma et al. 2006.
- Probabilistic Approaches to Neural Coding. Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P. N. Rao. 2007.
- Efficient codes and balanced networks. Sophie Deneve & Christian K Machens. 2016.
- Decision Theory: What "Should" the Nervous System Do? Konrad Körding. 2007.
- The Generalization of Prior Uncertainty during Reaching. Hugo L. Fernandes, Ian H. Stevenson, Iris Vilares and Konrad P. Kording. 2014.
- Better Optimism By Bayes: Adaptive Planning with Rich Models. Guez, A, Silver, D & Dayan P. 2014.
- The phasic dopamine signal maturing: from reward via behavioural activation to formal economic utility. W. Schultz,
W. Stauffer, A. Lak. 2017.
- Dopamine neurons learn relative chosen value from probabilistic rewards. Armin Lak, Stauffer W. & W. Schultz. 2016.
- Dopamine: generalization and bonuses. Kakade & Dayan. 2008.
- Two distinct neural mechanisms for category-selective responses. Noppeney U, Price C, Penny W & Friston KJ. 2006.
N additional papers(N <= 10-20):
- On the Origins of Suboptimality in Human Probabilistic Inference. Acerbi, L., Vijayakumar, S. & Wolpert, D. M. 2014.
- Predictability, Complexity, and Learning. Bialek, Nemenman & Tishby. 2001.