Department of Physiology, University of Bern, Bern, Switzerland.
Institute of Neuroinformatics and Neuroscience Center Zurich, ETH and the University of Zurich, Zurich, Switzerland.
PLoS Comput Biol. 2020 May 18;16(5):e1007886. doi: 10.1371/journal.pcbi.1007886. eCollection 2020 May.
Accumulating evidence indicates that the human brain copes with sensory uncertainty in accordance with Bayes' rule. However, it is unknown how humans make predictions when the generative model of the task at hand is described by uncertain parameters. Here, we tested whether and how humans take parameter uncertainty into account in a regression task. Participants extrapolated a parabola from a limited number of noisy points, shown on a computer screen. The quadratic parameter was drawn from a bimodal prior distribution. We tested whether human observers take full advantage of the given information, including the likelihood of the quadratic parameter value given the observed points and the quadratic parameter's prior distribution. We compared human performance with Bayesian regression, which is the (Bayes) optimal solution to this problem, and three sub-optimal models, which are simpler to compute. Our results show that, under our specific experimental conditions, humans behave in a way that is consistent with Bayesian regression. Moreover, our results support the hypothesis that humans generate responses in a manner consistent with probability matching rather than Bayesian decision theory.
越来越多的证据表明，人类大脑根据贝叶斯法则应对感官不确定性。然而，目前尚不清楚当手头任务的生成模型由不确定的参数描述时，人类如何进行预测。在这里，我们测试了人类是否以及如何在回归任务中考虑参数不确定性。参与者从计算机屏幕上显示的有限数量的嘈杂点中推断出抛物线。二次参数取自双峰先验分布。我们测试了人类观察者是否充分利用了给定的信息，包括给定观察点和二次参数先验分布的二次参数值的可能性。我们将人类表现与贝叶斯回归进行了比较，贝叶斯回归是该问题的（贝叶斯）最优解决方案，还有三个次优模型，这些模型计算起来更简单。我们的结果表明，在我们特定的实验条件下，人类的行为与贝叶斯回归一致。此外，我们的结果支持了这样一种假设，即人类以与概率匹配而不是贝叶斯决策理论一致的方式生成响应。
