Spatial predictive inference with continuous position and width control

We are interested in how decision-makers construct and update their beliefs about uncertain future events. Specifically, we’re focused on both point predictions of the most likely outcome as well as the uncertainty around that prediction. To investigate this, we are using a spatial predictive inference task in which participants use a “basket” to catch targets that appear on the perimeter of a circle. The location of targets is partially predictable, defined by a consistent generative structure, and participants are tasked with learning that structure so they can catch targets, thereby earning a monetary bonus. Participants can adjust both the location and the width of their basket in a continuous manner, yielding a measure of both their prediction and its associated uncertainty.