Mental Models and Transfer Learning

Abstract

Using a laboratory experiment, we investigate the extent to which learning is transferred between related problems in the context of an updating task. The updating principle we study requires updating positively after a positive signal and negatively after a negative signal. In the first environment, most subjects initially fail to satisfy the principle but eventually adjust after feedback. The environment they face subsequently presents the same challenges but with different parameter values. We find weak evidence for transfer learning: only half of the subjects who learn to be consistent with the principle remain so when the parameter values are changed.