What is learned from experience in a probabilistic environment?

Abstract

AbstractThree experiments explored what is learned from experience in a probabilistic environment. The task was a simulated medical decision‐making task with each patient having one of two test results and one of two diseases. The test result was highly predictive of the disease for all participants. The base rate of the test result was varied between participants to produce different inverse conditional probabilities of the test result given the disease across conditions. Participants trained using feedback to predict a patient's disease from a test result showed the classic confusion of the inverse error, substituting the forward conditional probability for the inverse conditional probability when tested on it. Additional training on the base rate of the test result did little to improve performance. Training on the joint probabilities, however, produced good performance on either conditional probability. The pattern of results demonstrated that experience with the environment is not always sufficient for good performance. That natural sampling leads to good performance was not supported. Further, because participants not trained on joint probabilities did, however, know them but still committed the confusion of the inverse error, the hypothesis that having joint probabilities would facilitate performance was not supported. The pattern of results supported the conclusion that people learn all the necessary information from experience in a probabilistic environment, but depending upon what the experience was, it may interfere with their ability to recall to memory the appropriate sample set necessary for estimating or using the inverse conditional probability. Copyright © 2004 John Wiley & Sons, Ltd.