Glaze et al. show that individual variability in learning from noisy evidence involves a bias–variance trade-off that is best explained by a model using a sampling algorithm that approximates optimal inference.
- Christopher M. Glaze
- Alexandre L. S. Filipowicz
- Joshua I. Gold
