Decision Biases in Optimal Stopping Problems: Evidence for Oversampling Compared to a Bayesian Ideal Observer Model

The general consensus in the field of optimal stopping problems is that humans tend to terminate searches too early compared to computational models of optimality. However, we found contradicting results across three different decision making domains, demonstrating real-life decision-making scenarios in which humans show an oversampling bias.