Instance-based generalization for human judgments about uncertainty.

ABSTRACT: While previous studies have shown that human behavior adjusts in response to uncertainty, it is still not well understood how uncertainty is estimated and represented. As probability distributions are high dimensional objects, only constrained families of distributions with a low number of parameters can be specified from finite data. However, it is unknown what the structural assumptions are that the brain uses to estimate them. We introduce a novel paradigm that requires human participants of either sex to explicitly estimate the dispersion of a distribution over future observations. Judgments are based on a very small sample from a centered, normally distributed random variable that was suggested by the framing of the task. This probability density estimation task could optimally be sol

SUBMITTER: Schustek P 

PROVIDER: S-EPMC6002126 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature