Instance-based generalization for human judgments about uncertainty

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 solved by inferring the dispersion parameter of a normal distribution. We find that although behavior closely tracks uncertainty on a trial-by-trial basis and resists an explanation with simple heuristics, it is hardly consistent with parametric inference of a normal distribution. Despite the transparency of the simple generating process, participants estimate a distribution biased towards the observed instances while still strongly generalizing beyond the sample. The inferred internal distributions can be well approximated by a nonparametric mixture of spatially extended basis distributions. Thus, our results suggest that fluctuations have an excessive effect on human uncertainty judgments because of representations that can adapt overly flexibly to the sample. This might be of greater utility in more general conditions in structurally uncertain environments.Author summary: Are three heavy tropical storms this year compelling evidence for climate change? A suspicious clustering of events may reflect a real change of the environment or might be due to random fluctuations because our world is uncertain. To generalize well, we should build a probability distribution over our observations defined in terms of latent causes. If data is scarce we are forced to make strong assumptions about the shape of the distribution ideally incorporating our prior knowledge. In our task, human behavior is consistent with probabilistic inference but reveals a tendency to generalize based on observed instances enhancing the effect of random patterns on behavioral judgments. The decreased reliance on available constraints through prior knowledge corresponds to a dominance of bottom-up sensory information. Maintaining a balance with expectation-driven top-down information is crucial for proper generalization. Our work provides evidence for the necessity to include graded instance-based generalization into the mathematical formulation of cognitive models. The investigation of the determinants and neural substrates of this inferential bias is expected to give insights into the richness but also fallibility of human inferences.