Hindsight bias is characterized by an exaggerated belief, following the occurrence of an event, that one could have predicted it beforehand. This 'Monday morning quarterbacking' has been found to compromise judgment in a large variety of domains including medicine, law, and accounting. For example, physicians who learn a correct diagnosis significantly exaggerate the likelihood that they could have arrived at that diagnosis. This bias interferes with learning. A physician who is able to recognize that he or she might not have made a correct diagnosis will be more willing to attend to the lessons to be learned from a difficult case.
This research addresses the causes of the hindsight bias. A hypothesized cause is the increased 'fluency' of cognitive processing that occurs when a correct answer is revealed or an event that has occurred. For example, if X occurs, I spend some time thinking about X. When asked whether I would have predicted that X, Y, or Z would occur, the augmented processing done on X makes its fluency higher than that for Y or Z. To test whether heightened fluency causes hindsight bias, I manipulate fluency by some unorthodox means and then assess the magnitude of the hindsight bias. For example, in one study the visual clarity of a printed statement will be manipulated. The statement will be truthfully labeled as either 'true' or 'false.' Subjects will be asked whether they would have rated the statement as true or false if they had not been told the actual truth status. I predict that when statements are printed less clearly they will produce less of a hindsight effect, because the unclear presentation will lower the fluency with which they are processed.
Another group of experiments will test whether people more knowledgeable about a topic are less able to quarantine new knowledge about that topic compared to those less knowledgeable. For example, a professor must ignore much of his or her knowledge in order to teach the novices taking a class. This is similar to the hindsight bias: in hindsight-bias situations, a person must ignore knowledge about outcomes in order to make accurate assessments. In the professor's situation, he or she must ignore content knowledge. I will examine the conditions that enable people to perform this task more or less successfully.
